Reviewerly/exHarmony huggingface dataset:
The muon (g-2) Standard-Model prediction and GeV-scale new physics

The discrepancy between the standard model (SM) prediction for the muon anomalous magnetic moment and the experimental result is accompanied by other anomalies.A crucial input for the prediction is the hadronic vacuum polarization inferred from hadronic data.However, the two most accurate determinations from KLOE and BaBar disagree by almost 3.Additionally, the combined data-driven result also disagrees with the most precise lattice determinations in the intermediate energy window at the level of ∼ 4.2.We show with a simple model that all these discrepancies could be accounted for by a new boson produced resonantly around the KLOE centre-of-mass energy and decaying promptly yielding lepton pairs and missing energy in the final states, while complying with all phenomenological constraints.
High-Dimensional Indexing Scheme for Scene Graph Retrieval

Recently, content-based image retrieval has been used for monitoring and tracking criminal behavior in images and videos. In addition, with the development of machine learning techniques, image searches using scene graphs have also been studied. The characteristic of scene graph data is that there are very many dimensions of the data. There is a problem that it is generally difficult to retrieve. In this paper, we propose a distributed in-memory-based high-dimensional indexing scheme. Wet can exploit the proposed scheme for similarity search using large high-dimensional vectors for content-based image retrieval and scene graphs. Furthermore, we propose a multi-level indexing scheme to utilize the master/slave model. In addition, we perform performance evaluations to demonstrate the superiority and validity of the proposed scheme.
Using Transformers and Deep Learning with Stance Detection to Forecast Cryptocurrency Price Movement

The volatility of cryptocurrencies and exclusivity of crypto communities has made cryptocurrency investment inaccessible for common people. With machine learning, harnessing social media trends that affect price in a random field like cryptocurrency will provide everybody the ability to earn money. Although existing research utilizes sentiment analysis to label posts based solely on English, this project will use NLP to perform stance detection with respect to a certain entity to make predictions. The second part of this project will apply this stance detection to real-world prices, using an RNN to turn stance data into price data. The stance detection model, RoBERTa, reached an accuracy of 80%. An independent price prediction model using an RNN achieved a mean absolute error of $1144, a relatively minimal error considering that the price of crypto reaches $60000. This endeavor proves the difficulty in proving cryptocurrency prices, but the model's steady improvement indicates that future work on social media trends may be promising after all.
An Active Reference Reset Method Adapting Distribution Shift for Robust System Anomaly Detection

In order to ensure the operation reliability of process systems in various industries, faults and unplanned shutdowns must be detected early. Traditionally, they have been detected based on the domain knowledge of industrial engineers. However, data-based methods have been developed with recent advances in machine-learning algorithms. These methods can detect signs of shutdowns and set the alarms off. However, they can not account for data distributions that have undergone changes after shutdowns. To handle this challenge, we propose a robust system anomaly detection algorithm using the distribution-based active reference set reset method. The experimental results on real-world industry data show that the proposed method could detect abnormal signs early at various thresholds.
A Functional Verification Study of Quantum Key Distribute Networks and Services with a Trusted Node applied in KOREN

The development of quantum cryptographic communication technology and the establishment of quantum cryptographic communication infrastructure and services have been presented in the public and private business sectors. In this paper, quantum cryptography communication equipment from various vendors is established in the Korea Advanced Research Network (KOREN) operated by the National Information Society Agency(NIA), and functions and interoperability are verified in the test network. So far, basic verification standards and operation guidelines for quantum cryptography communication equipment have not been established. In this paper, we would like to examine the basic functions, linkage functions, and long-term driving requirements of each device that provides quantum cryptography communication services.
Refining Action Boundaries for One-stage Detection

Current one-stage action detection methods, which simultaneously predict action boundaries and the corresponding class, do not estimate or use a measure of confidence in their boundary predictions, which can lead to inaccurate boundaries. We incorporate the estimation of boundary confidence into one-stage anchor-free detection, through an additional prediction head that predicts the refined boundaries with higher confidence. We obtain state-of-the-art performance on the challenging EPICKITCHENS-100 action detection as well as the standard THUMOS14 action detection benchmarks, and achieve improvement on the ActivityNet-1.3 benchmark.
Monocular Depth Estimation using Improved CSR

Depth-estimation from a single input image can be used in applications such as robotics and autonomous driving. Re-cently, depth-estimation networks using CSR(Channel to Sapce unRolling) [1] has been proposed. The purpose of CSR is to reduce computation in the decoder part of a UNet-typed depth-estimation network. By upsampling at a high magnification, CSR converts thick channel and low resolution data into thin channel and high resolution data. We go one step further and propose a method to increase the accuracy by adding a kind of preprocessing process before the reshape operation during the CSR process. When the proposed method is applied, the amount of computation is slightly increased compared to the case of applying a simple CSR, but the effect of improving the accuracy was confirmed. The proposed method was tested on the NYU-V2 dataset, and it was confirmed that a similar effect was obtained when both a lightweight network and a heavy-weight network were used as encoders.
The Pattern Analysis-based Data Imputation Method for Data Table

Data preprocessing is an essential process in order to train a model and obtain high predictive performance in AI(artificial intelligence) technology, which is the core technology of the 4th industrial revolution era. In the real world, data missing and errors always occur, and such flawed data can pose serious risks to the performance of the model. However, the existing simple correction techniques or multiple imputation techniques used to correct missing data may not have a large correction effect due to the low accuracy. We proposed a method to correct the data by designing an imputation algorithm based on the data table pattern.
Adaptive Tsallis Entropy Regularization for Efficient Reinforcement Learning

In this paper, we present adaptive regularization using Tsallis entropy (ART) for efficient exploration in reinforcement learning (RL) problem. Tsallis entropy represents various types of entropy with an additional parameter called entropic index, which generalizes the Shannon-Gibbs entropy. Tsallis entropy is often utilized as a regularization of the policy function where the resulting policy becomes stochastic and balances exploration and exploitation. Previous work on using Tsallis entropy for exploration only considered the entropic index between zero and one, which is a limited set of entropies. However, in this paper, we extend it to wider range of entropic index and enable to employ all types of Tsallis entropy with a positive entropic index in RL problem. Furthermore, we propose the condition of the optimal entropic index which has the smallest regret bound among all positive entropic index. Using this condition, we can automatically determine the optimal entropic index without demanding a brute force search to find the proper regularization of policy. In the experiment, simply applying the ART to the existing RL methods leads to fast convergence and performance improvement.
Federated Flowchart: Overview of State-of-the-Arts based on Federated Learning Process

Federated learning centrally trains global statistical models by aggregating the local models trained on each device with localized data. Accordingly, federated learning protects the privacy of each device by eliminating the need to transmit distributed data generated in real-time on each device to a central data center. However, unlike other machine learning paradigms, this new paradigm raises new problems in large-scale and high-density networks composed of various devices, such as privacy protection due to the numerous devices, and distributed optimization due to the different resources of each device. In this paper, we describe the characteristics, challenges, and process of federated learning, and provide an overview of state-of-the-arts according to each phase of the process.
Dual Camera Based High Spatio-Temporal Resolution Video Generation For Wide Area Surveillance

Wide area surveillance (WAS) requires high spatiotemporal resolution (HSTR) video for better precision. As an alternative to expensive WAS systems, low-cost hybrid imaging systems can be used. This paper presents the usage of multiple video feeds for the generation of HSTR video as an extension of reference based super resolution (RefSR). One feed captures video at high spatial resolution with low frame rate (HSLF) while the other captures low spatial resolution and high frame rate (LSHF) video simultaneously for the same scene. The main purpose is to create an HSTR video from the fusion of HSLF and LSHF videos. In this paper we propose an end-to-end trainable deep network that performs optical flow (OF) estimation and frame reconstruction by combining inputs from both video feeds. The proposed architecture provides significant improvement over existing video frame interpolation and RefSR techniques in terms of PSNR and SSIM metrics and can be deployed on drones with dual cameras.
Semi-Automatic Generating Semantic Markup Webpage from Structured Data with Semantic Matching

Along with the development of the knowledge graph, it required more significant structured data to build it. Publishing massive structured data, such as relational databases, require substantial effort. Therefore, this work pro-poses a framework for semi-automatic generating structured data and creating a semantic matching algorithm for the table’s attributes and schema.org’s properties. The algorithm uses Wu Palmer Similarity (WUP) and WordNet as semantic similarity measurements. Although the obtained scores are still pretty fair, the whole framework runs well at 0.4285 for WUP+k and 0,3833 for WUP.
Ballistic Timing of Smiles is Robust to Context, Gender, Ethnicity, and National Differences

Smiles are highly variable. In some, contraction of the orbicularis oculi raises the cheeks and amplifies their intensity. In others, smile controls counteract the oblique pull of the zygomatic major, alter their shape, and decrease their intensity. Despite this variability, some features appear to be stereotypic. These features include a high correlation between the amplitude and velocity of smile onsets and same for smile offsets. The larger a smile's amplitude, the greater its velocity. This dependence is referred to as ballistic timing. In two relatively large publicly available databases (EB+ and Belfast), we tested the hypothesis of ballistic timing of smile onsets and offsets. We found high and consistent non-linear correlations between amplitude and velocity of both onsets and offsets that were robust to individual differences in persons (gender and ethnicity), context, presence or absence of the Duchenne marker, and country of residence (United States, Ireland, Peru). All <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R_{2}$</tex> exceeded 0.85. The findings were highly consistent with ballistic timing. They have implications for detecting smiles that may be posed (which have been found to violate ballistic timing) and for realistic synthesis of smiles in social robots and virtual humans. Smiles that depart from ballistic timing are likely to be perceived as false or uncanny.
Audio-driven Facial Animation: A Survey

This paper provides a survey of the techniques for audio-driven 3D facial animation. This paper reviews linguistic-based animation and deep-learning-based animation. First, we present a brief historical review of technological evolution. Then we introduce each method. We discuss limitations and future directions at the end of the paper.
Spatial-Temporal Transformer for Crime Recognition in Surveillance Videos

Human-related crime recognition from surveillance videos becomes an even more challenging task when dealing with relatively similar human actions.We propose a transformer-based model that relies on the spatial-temporal representation of extracted skeletal trajectories for fine-grained classification.We validate the effectiveness of our model on the complex HR-Crime dataset consisting of videos representing 13 categories of human-related crimes.Quantitative and qualitative results suggest that building a transformer architecture with coupled spatial and temporal modules enables the model to compete in performance while improving intrinsic interpretability.
Temporal-Invariant Video Representation Learning with Dynamic Temporal Resolutions

Recent studies for similarity-based self-supervised representation learning tend to consider only fixed temporal coverage from a given video. However, this approach limits that a model learns temporally persistent representations since it cannot reflect spatial and temporal information gaps from resolution variations. To overcome the limitation, this paper proposes a Temporal Adaptive Teacher-Student (TATS) framework that encourages the trained model to be robust on spatio-temporal variations. Our key approach is optimizing similarity-based learning that utilizes several views with dynamic temporal resolutions. From a given video, TATS captures spatio-temporal invariant clues for temporally persistent representation with cross-resolution correspondence between local and global views. Extensive experiments show that our TATS achieves competitive downstream (action recognition and video retrieval) performances on benchmarks (UCF101 and HMDB51).
Lazy Net: Lazy Entry Neural Networks for Accelerated and Efficient Inference

Modern edge devices have become powerful enough to run deep learning tasks, but there are still many challenges, such as limited resources such as computing power, memory space, and energy. To address these challenges, methods such as channel pruning, network quantization and early exiting has been introduced to reduce the computational load for achieve this tasks. In this paper, we propose LazyNet, an alternative network of applying skip modules instead of early exiting on a pre-trained neural network. We use a small module that preserves the spatial information and also provides metrics to decide the computational flow. If the data sample is easy, the network skips most of the computation load and if not, the network computes the sample for accurate classification. We test our model with various backbone networks and cifar-10 dataset and show reduction on model inference time, memory consumption and increased accuracy to prove our results.
Optimal Estimation of Entangled States in Classical Quantum Simulator

Estimating the entanglement of qubits without measurement is a critical problem in quantum computing. Quantum algorithms using many qubits entail a substantial computational load as the quantum state increases exponentially. A mathematical tool like Schmidt's decomposition is virtually impossible to apply directly to such an algorithm. We propose a novel method for estimating entanglement states more precisely and quickly without measurement using a classical state vector quantum simulator. The fundamental principle of our idea is to project the state of the desired qubit from the entire quantum state into classical bits. Our concept can work independently of the exponential problem in classical quantum computers, ensuring faster-estimating entanglement relationships with fewer computing resources. In this paper, we present the verification results of our ideas using QPlayer, a special-purpose classical quantum simulator.
Height Normalizing Image Transform for Efficient Scene Specific Pedestrian Detection

Surveillance cameras typically study the exact same scene day and night, year after year, but utilize general pedestrian detectors trained to detect pedestrians from any viewing angle. By specializing the detector to the scene it is used on, its need for computational resources can be reduced and thereby its carbon footprint and operational cost. We propose a (optionally straightened) height normalizing image transform, (S)HeNIT, to be used as a preprocessing step to a CNN detector. It removes the pixel height variations of pedestrians moving on a ground plane. Therefore, the visual variation is reduced, and the available pixels are more evenly redistributed between close and distant pedestrians. This allows the detector to be specialized to a specific scene by fine-tuning and pruning it on rendered, scene specific, synthetic data. The transform also allows for lower resolution input images to be used, making recognition more difficult, which is desirable from a privacy standpoint.
Lessons Learned from Delta and Omicron Variants Transmissions Leveraging Clustering Approach by Sub-Districts in Jakarta

Various variants of COVID-19 have entered Indonesia, such as the delta and the omicron variants. The delta variant has a higher severity than the omicron variant, but the transmission rate for the omicron variant is much faster. The government encourages citizens to get booster vaccines to reduce the effect of the delta and omicron variants. The booster vaccine produced a better effect on citizens than on people who received only the two doses. Therefore, in this study, we observe the transmission of COVID-19 and the vaccine locations on the sub-districts level using the clustering approach. The data we use are COVID-19 positive cases, died, treated, and self-isolated cases. Meanwhile, the vaccination data are $1^{\text{s}\text{t}}$ dose, $2^{\text{n}\text{d}}$ doses, stage 3 of $1^{\text{s}\text{t}}$ dose, and stage 3 of $2^{\text{n}\text{d}}$ doses. The Dunn Index and Hubert Index methods determined the best number of clusters before the clustering process. Silhouette and Davies Bouldin are used to find better clustering between Fuzzy C-Means, K-Means, and Partition Around Medoids (PAM). The results obtained from this study showed that the K-Means method was the best with the best number of clusters, namely 3. Jagakarsa and Kebon Jeruk entered high levels at the time of the delta variant due to the COVID-19 case and vaccination spread. However, Jagakarsa and Kebon Jeruk dropped to the intermediate level during the omicron variant. The benefit of this study is to help the government pay more attention to high COVID-19 cases and low vaccine distribution.
On the Design Principles for Deep Learning-based Wireless Data Collection and Channel Estimation

The big success of deep learning (DL) has motivated the application of DL to wireless systems in recent years. In this paper, we explain issues and corresponding solutions to be considered in the DL-based wireless channel estimation. Specifically, we discuss the neural network architecture design and generative adversarial network (GAN)-based training data collection.
Privacy-Preserving Learning Models for Communication: A tutorial on Advanced Split Learning

Along with the development of the internet, there is an explosion of data that is assembled from diverse sources such as mobile phones or sensors with unprecedented volumes. Big data paves the way for devising state-of-the-art Machine Learning models, which are employed for various tasks such as predicting or analysis, and bring massive impacts on people's daily lives. However, traditional learning architectures pose risks of privacy leakage. In deal with this problem, Split Learning was introduced. Split Learning is an emerging Distributed Collaborative Machine Learning approach with privacy-preserving nature. In this work, we first introduce the fundamentals of Split Learning and then describe major challenges of current learning models. We then depict modern Split Learning and their potential applications in variety fields.
Lightweight Object Recognizer for Edge Devices

Recently, as personal edge devices such as smartphones have become popular, real-time object recognizers are attracting attention. Edge devices not only have limited memory and storage, but also have various types of HW platforms depending on the purpose of use. In particular, depending on the GPU type of edge devices, whether to use a parallel processing framework essential for deep learning computation acceleration such as OpenCL or CUDA is determined. Therefore, for real-time object recognition in various edge devices, it is necessary to support deep learning computation acceleration optimized for the GPU structure of each edge device. To this end, in this paper, we propose a deep learning framework that analyzes the GPU structure of edge devices and automatically applies the optimal parallel processing technique (OpenCL or CUDA) according to the GPU type. In addition, based on the proposed deep learning framework, we implement a real-time object recognizer based on the field image on the edge device.
Object Detection using Speech Recognition

Nearly all practical applications, including autonomous navigation, visual systems, face recognition, and more, rely on object detection. In this paper, object detection and speech recognition are combined to help visually impaired people who want to use voice commands to find a certain object. People who are blind or visually challenged can move more independently if they are aware of their surroundings. With the use of the OpenCV libraries, a model has been implemented, and good results have been obtained. In this paper, a thorough review of object detection employing region-based conventional neural network (CNN)- based learning systems for practical applications has been conducted. This study examines the various object identification processes utilizing YOLOV4 object detection techniques and talks through detection, including a speech recognition system that was created by transcribing spoken language into text.
Super Resolution Network for Artistic Paintings

In this paper, we propose a method of increasing resolution in artistic painting using a deep learning network. Existing SR networks have focused on extracting features that contain sufficient information in a low-resolution state. In this paper, we propose a method that can increase the resolution up to 8 times in the artistic painting domain by improving the upsampling method at the end of the network. The proposed method was confirmed to be usefully applicable to existing artistic painting through experiments.