Reviewerly/exHarmony huggingface dataset:
Object Selection Using LSTM Networks for Spontaneous Gaze-Based Interaction

Two years on with Covid-19, touchless technology has evolved from a device that symbolizes luxury to something that is necessary. Eye tracker is one type of touchless technologies that uses user's gaze to interact with computer without touching the screen. Development of spontaneous gazebased interaction is progressing very rapidly. Researchers have developed various object selection methods without prior gazeto-screen calibration. Recently, the conventional approach of setting threshold was developed as a gaze-based object selection method. However, the use of threshold values is considered non-adaptive and requires additional data pre-processing to handle noises. To overcome this problem, deep learning is used as an object selection method for spontaneous gaze-based interaction. Deep learning does not require any data preprocessing method to achieve accurate object selection results. Out of five deep learning algorithms that were evaluated, LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional Long Short-Term Memory) networks achieved comparable accuracy of $95.17 \pm 0.95$% and $95.15 \pm 1.17$%, respectively. In future, our research is promising for development of real-time object selection technique for touchless public display.
Known Plaintext Attacks on the Omar and abed Homomorphic Encryption Scheme

In 2020, Omar and abed proposed a new noise-free fully homomorphic encryption scheme that allows arbitrary computations on encrypted data without decryption. However, they did not provide a sufficient security analysis of the proposed scheme and just stated that it is secure under the integer factorization assumption. In this paper, we present known plaintext attacks on their scheme and illustrate them with toy examples. Our attack algorithms are quite simple: They require several times of greatest common divisor (GCD) computations using only a few pair of message and ciphertext.
Video Distance Measurement Method Based on the Weighted Average of the Depth Intervals Centers

In literature, there are two methods of distance measurement using video cameras: active and passive. Active methods use additional hardware to emit light beams from various configurations, and passive methods use light from the camera’s lens. Passive methods may use one or more camcorders. In the latter case, detection methods of an object are known based on the correlation with the focusing position of the focus lens and the identification of the image with maximum clarity. This article highlights solving some problems in this field by increasing the number of calibration states compared to the number of steps the focus lens can perform and implicitly increasing the measurement accuracy. The distance measurement method based on weighted average, according to the study conducted, comprises a calibration stage and a measurement stage.
Vision-based Autonomous Landing System for Quadcopter Drone Using OpenMV

Nowadays, drones have a wider variety of applications. The drones are now used for daily activities that the demand for drones is increase and the shift to a lower price. Then, drones manufactured with poor build quality. Consequently, the drone is now more vulnerable to crashes. The crash on landing is one of the most common errors made by novice users. This research utilizes a vision-based precision landing method for quadcopter drones equipped with low-cost cameras capable of tracking AprilTag. The system deploy the Pixhawk as the flight controller, Mission Planner as the Ground Control Station (GCS) software, the OpenMV H7 camera, telemetry, and a computer. The test results obtained are the outdoor accuracy for 2 m altitude is outdoors 8.94 cm, semi-outdoor 9.1 cm, and indoor 11.7 cm. The average accuracy for 3 m altitude outdoors is 9.35 cm, semi-outdoor 9.45 cm, and indoor 10.8 cm, which indicates the system can perform vision-based autonomous landing for quadcopter.
Real-time semantic segmentation on edge devices: A performance comparison of segmentation models

Recent advances in convolutional neural networks have led to considerably accurate semantic segmentation tasks. However, previous works use a pre-trained backbone borrowed from image classification tasks with considerable computational complexity, resulting in large latencies. Many methods have been proposed to reduce the latency of the segmentation network without loss of accuracy. These methods have reported varying experimental results with different computing devices, acceleration techniques, and input image sizes. Although most studies claim that their results are state-of-the-art, it is necessary to reconsider whether they are being compared under the same conditions. We propose a performance evaluation method of real-time semantic segmentation models to compare the performance under the same conditions fairly. In addition, we carry out an empirical study to evaluate the performance of recent real-time semantic segmentation networks and make a comparative analysis between them. We train the segmentation models using the same input data and data augmentation method. Then, the performance of the segmentation methods is analyzed regarding accuracy and speed. In contrast to most studies that exclude the time required for the pre-processing and post-processing steps, we measured the actual processing time needed to perform semantic segmentation with a real dataset. Further, we measured the processing speed and power consumption of the segmentation models in embedded devices in which real-time segmentation is applied, unlike previous studies that measured performance on a PC. Experimental results showed that the real-time semantic segmentation methods could not run in real-time on embedded devices when considering the pre-processing and post-processing steps. By comprehensively considering the inference speed, energy consumption, and processing time of semantic segmentation models, the experimental results show that FasterSeg-S is suited for embedded devices.
Wireless Blockchains: Trade-offs and Future Challenges

The low performance of blockchains has been considered as one of the main challenges in deploying blockchains. This blockchain performance has been discussed and analyzed for further improvements, especially in wired blockchains. In wired blockchains, transactions, messages for consensus protocols (CPs), and new blocks are all transmitted through wired links. However, even though wireless blockchains have actively emerged in recent years, most of blockchain works are still focused on wired blockchains. Being different from wired blockchains, transactions, messages for CPs, and new blocks can face the characteristics of wireless communications, such as limited communication resources, path loss, and higher interference. This is highly influential on the performance of wireless blockchains, especially on CPs that play an important role in fault tolerance inside blockchains. In this paper, therefore, we discuss the impacts of communications on wireless blockchains. We then provide trade-offs and future challenges, which need to be newly considered for higher performance in wireless blockchains.
Generative Data Augmentation via Wasserstein Autoencoder for Text Classification

Generative latent variable models are commonly used in text generation and augmentation. However generative latent variable models such as the variational autoencoder(VAE) experience a posterior collapse problem ignoring learning for a subset of latent variables during training. In particular, this phenomenon frequently occurs when the VAE is applied to natural language processing, which may degrade the reconstruction performance. In this paper, we propose a data augmentation method based on the pre-trained language model (PLM) using the Wasserstein autoencoder (WAE) structure. The WAE was used to prevent a posterior collapse in the generative model, and the PLM was placed in the encoder and decoder to improve the augmentation performance. We evaluated the proposed method on seven benchmark datasets and proved the augmentation effect.
Balanced Data Augmentation of Object Detection Via Boot-strapping

In this paper, we propose the balanced data augmentation method for object detection via boot-strapping. We demonstrate that the proposed method is a kind of boot-strapping algorithms for object detection and improves the performance of object detection in the VOC dataset. Our method not only makes data balance but also improves the detection accuracy.
Sparsification on Different Federated Learning Schemes: Comparative Analysis

High communication overhead is a major bottleneck in federated learning (FL). To overcome this issue, sparsification is utilized in various compression frameworks. Generally, local clients upload the updated weights to the server. However, in sparsification, we observed that local clients upload the difference between the updated weights and the original weights. Our study is to confirm the importance of uploading the difference of weights in sparsification and to observe how different the accuracy between the two schemes is.
Multitask Learning with Heterogeneous Tasks

It is general that more computation resources are required to achieve better performance of CNNs. This is even more severe when we perform multiple tasks. Multitask learning (MTL) can address this problem by sharing architecture across tasks. Previous MTL studies obtained a feature from a shared backbone or module and performed multiple tasks. However, learning from heterogeneous tasks has not gained much attention. For example, input data has different properties, such as resolutions and object classes; thus, the aggregation of the features generated from heterogeneous tasks is complicated. To mitigate this problem, we propose a simple learning strategy that generalizes the shared representation using multiple datasets for heterogeneous tasks. We evaluate the strategy using four datasets and show that learning from different tasks jointly gives a comparable performance to learning individual tasks and outperforms the existing strategy for some tasks.
Stochastic Differential Equation of the Quantization based Optimization

We propose a stochastic differential equation for a quantization-based optimization algorithm. A fundamental differential equation describes the state transition by an algorithm to analyze the dynamics of an optimization algorithm. According to the White Noise Hypothesis of quantization error with dense and uniform distribution, we can regard the quantization error as an identically independent distribution(i.i.d.) white noise. It leads that we can obtain a stochastic equation about the quantization-based optimization algorithm to analyze the global dynamics. Numerical experiments show that the proposed algorithm involves better performance than the conventional optimization algorithm, such as simulated annealing.
A Survey on Awesome Korean NLP Datasets

English based datasets are commonly available from Kaggle, GitHub, or recently published papers. Although benchmark tests with English datasets are sufficient to show off the performances of new models and methods, still a researcher need to train and validate the models on Korean based datasets to produce a technology or product, suitable for Korean processing. This paper introduces 15 popular Korean based NLP datasets with summarized details such as volume, license, repositories, and other research results inspired by the datasets. Also, I provide high-resolution instructions with sample or statistics of datasets. Full-length version paper with summarization of each datasets with tables is available at arXiv (https://arxiv.org/pdf/2112.01624.pdf).
OFedIT: Communication-Efficient Online Federated Learning with Intermittent Transmission

We study an online federated learning (OFL) where many edge nodes receive their own data sequentially and train a sequence of global functions (or models) under the orchestration of a central server while keeping data localized. In this framework, finding a communication-efficient algorithm is one of the challenges for online federated learning (OFL). We present a communication-efficient OFL algorithm (named OFedIT) using intermittent transmissions. Our main contribution is to theoretically prove that OFedIT over <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> time slots achieves an optimal sublinear regret bound <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(\sqrt{T})$</tex> . Furthermore, this asymptotic optimality is ensured even when data- and system-heterogeneity are taken into account. Our analysis reveals that OFedIT yields the almost same performance as the centralized counterpart (i.e., all local data are gathered at the server) while having the advantages of communication cost and data-privacy.
Classifying Laughter Using the Component Process Model

Most automatic laughter detection and classification systems adapt a black-box approach where expressive behaviors are mapped directly to labels, without providing any information about the situation that elicited the laughter. This study explored the application of Scherer's Component Process Model (CPM) to extract contextual information to determine the type of laughter. A dataset of 397 laughter samples is annotated with appraisal values and laughter labels. Acoustic features are extracted and mapped to appraisal variables, which in turn are mapped to laughter labels. Preliminary experiments show that appraisal variables do reveal some contextual information about laughter. Mirthful laughter is observed to have high appraisals in terms of novelty, pleasantness, and appropriateness, but low in goal congruency and control. Polite laughter, on the other hand, has low novelty and high levels of pleasantness, control, and appropriateness.
State Injection and Syndrome Decoding for Rotated Surface Code in Quantum Computing

In this paper, we analyze a syndrome decoding problem that occurs when performing state injection on rotated surface code with distance = 3 and propose a method to solve it. When state injection is performed on the rotated surface code with distance 3, there are some cases that the state of logical qubit becomes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha\vert 0&gt; \mathrm{L}-\upbeta\vert 1&gt; \mathrm{L}$</tex> . due to the syndrome decoding problem. In this paper, we analyze the problem and propose an syndrome decoding method for the state injection. In addition, this was verified using quantum simulators.
An Exploratory Study of Security Data Analysis Method for Insider Threat Prevention

Insider threats are steadily increasing, and the damage is also enormous. To prevent insider threats, security solutions, such as DLP, SIEM, etc., are being steadily developed. However, they have limitations due to the high rate of false positives. In this paper, we propose a data analysis method and methodology for responding to a technology leak incident. The future study may be performed based on the proposed methodology.
Conditional GAN based Collaborative Filtering with Data Augmentation for Cold-Start User

In this paper, we propose Cold-CFGAN, a collab-orative filtering using two Conditional Generative Adversarial Networks (CGANs). In Cold-CFGAN, one CGAN is used for data augmentation of cold-start users, and the other CGAN is used to recommend items using user condition vectors. Cold-CFGAN research uses an additional GAN model to generate data for cold-start users to resolve the cold start problem that occurs when implementing CGAN-based collaborative filtering and to further improve the accuracy of the model. To this end, we first identified the performance degradation problem of cold-start users through a series of preliminary experiments using an existing conditional GAN-based collaborative filtering (CFGAN). Then, we used the user profile and item purchase data to express the number of purchased items per user in the form of a percentile, and identified cold-start users with few purchase items. Using the profile of the identified cold-start user data, we found the data of the Item-Rich user with the most similar profile to the cold-start user based on the cosine similarity, and using the data of the Item-Rich user, we applied partial masking method to create augmented cold-start users. Then we train user augmentation GAN to generate fake Item-Rich user using the augmented cold -start user and corresponding Item-Rich user in real data. We use trained generator to generate Item-Rich user corresponding to cold-start user in real dataset. Then, we applied the generated Item-Rich user data to train the conditional GAN-based collaborative filtering and after training, we performed experiment. Through the experiment, we found improved performance for cold start users compared to the traditional approach, and also improved overall performance.
Interactive skin through a social- sensory speculative lens

This paper uses a speculative lens to explore the social and sensory trajectories of Interactive Skin, a class of skin-worn epidermal devices that augment the human body in ways that are significant for affective techno-touch. The paper presents and discusses the use of a speculative narrative on Interactive Skin futures produced through an exploratory research-collaboration with a Human–Computer Interaction (HCI) lab, combining data from speculative methods (cultural probe returns and a future-orientated workshop) with an ethnographic sensitivity to writing. The speculative narrative is in the form of a found archive of fictional fragments that are research provocations in their own right. We discuss their potentials, including the ability to foster interdisciplinary dialogue between social and HCI researchers and to agitate the socio-technological space of interactive skin futures, as well as their limitations. The paper concludes that a socially orientated speculative approach can provide useful insights on the interconnection between the senses, society, and technology in the context of emergent affective techno-touch technologies.
Generating traceable adversarial text examples by watermarking in the semantic space

The adversarial examples have been proven to reveal the vulnerability of the deep neural networks (DNNs) model, which can be used to evaluate the performance and further improve the robustness of the model. Because text data is discrete, it is more difficult to generate adversarial examples in the natural language processing (NLP) domain than in the image domain. One of the challenges is that the generated adversarial text examples should maintain the correctness of grammar and the semantic similarity compared with the original texts. In this paper, we propose an adversarial text generation model, which generates high-quality adversarial text examples through an end-to-end model. Moreover, the adversarial text examples generated by our proposed model are embedded with watermarks, which can mark and trace the source of the generated adversarial text examples and prevent the model from being maliciously or illegally used. The experimental results show that the attack success rates of the proposed model can still reach higher than 88% even on the AG’s News dataset where generating adversarial text examples is more difficult. And the quality of adversarial text examples generated by the proposed model is higher than that of the baseline models. At the same time, because of the generated adversarial text examples are embedded with strong robust watermarks, the model can be better protected.
Three-stage fusion framework for single image dehazing

Fog generally causes loss or blurring of image information, significantly affecting work and life. This study proposes a three-stage fusion method comprising multiscale dark channel image fusion based on the dark channel prior, multiexposure image fusion using a gamma-correction technique, and a final fusion combining the restoration-based and enhancement-based methods to ensure good saturation and contrast in the dehazing result. The proposed method is experimentally evaluated in detail, and qualitative as well as quantitative analyses are performed. The obtained results indicate that our method can effectively improve the haze removal performance and is advantageous for dealing with long-distance hazy images.
Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles

Monitoring and patrolling large water resources is a major challenge for nature conservation. The problem of acquiring data of an underlying environment that usually changes within time involves a proper formulation of the information. The use of Autonomous Surface Vehicles equipped with water quality sensor modules can serve as an early-warning system for contamination peak-detection, algae blooms monitoring, or oil-spill scenarios. In addition to information gathering, the vehicle must plan routes that are free of obstacles on non-convex static and dynamics maps. This work proposes a novel framework to obtain a collision-free policy using deterministic knowledge of the environment by means of a censoring operator and noisy networks that addresses the informative path planning with emphasis in temporal patrolling. Using information gain as a measure of the uncertainty reduction over data, it is proposed a Deep Q-Learning algorithm improved by a Q-Censoring mechanism for model-based obstacle avoidance. The obtained results demonstrate the effectiveness of the proposed algorithm for both cases in the Ypacaraí monitorization task. Simulations showed that the use of noisy-networks are a good choice for enhanced exploration, with 3 times less redundancy in the paths with respect to — greedy policy. Previous coverage strategies are also outperformed both in the accuracy of the obtained contamination model by a 13% on average and by a 37% in the detection of dangerous contamination peaks. Finally, the achieved results indicate the appropriateness of the proposed framework for monitoring scenarios with autonomous vehicles.
Single image super‐resolution based on sparse representation using edge‐preserving regularization and a low‐rank constraint

Sparse representation-based non-local self-similarity approaches have demonstrated promising performance in single image super-resolution reconstruction. This type of method, however, cannot always effectively preserve the key details of an image, resulting in edge artifacts and local structure blurring. To better preserve the image edge information, in this paper, a sparse representation super-resolution method based on non-local self-similarity is proposed. First, we impose slide window gradient domain guided filtering on both the low-resolution input image and the degraded restored image. Then, we utilize their difference as an edge-preserving regularization term and incorporate this regularization term into the non-local self-similarity-based sparse representation model to build a sparse coding model, which can enhance the restored high-resolution image patches’ detail information. Finally, the iterative threshold algorithm is used to calculate the sparse representation coefficients so that a high-resolution image can be estimated. Furthermore, to explore the potential structures of the subspaces spanned by similar patches, we enforce a low-rank matrix recovery technique on the generated super-resolution image, which can further refine the reconstruction quality. Experimental results prove that the new approach preserves the critical edge structures while suppressing noise and exceeding some popular methods both quantitatively and qualitatively.