Reviewerly/exHarmony huggingface dataset:
Improved adaptively robust estimation algorithm for GNSS spoofer considering continuous observation error

Once the spoofer has controlled the navigation system of unmanned aerial vehicle (UAV), it is hard to effectively control the error convergence to meet the threshold condition only by adjusting parameters of estimation if estimation of the spoofer on UAV has continuous observation error. Aiming at this problem, the influence of the spoofer's state estimation error on spoofing effect and error convergence conditions is theoretically analyzed, and an improved adaptively robust estimation algorithm suitable for steady-state linear quadratic estimator is proposed. It enables the spoofer's estimator to reliably estimate UAV status in real time, improves the robustness of the estimator in responding to observation errors, and accelerates the convergence time of error control. Simulation experiments show that the mean value of normalized innovation squared (NIS) is reduced by 88.5%, and the convergence time of NIS value is reduced by 76.3%, the convergence time of true trajectory error of UAV is reduced by 42.3%, the convergence time of estimated trajectory error of UAV is reduced by 67.4%, the convergence time of estimated trajectory error of the spoofer is reduced by 33.7%, and the convergence time of broadcast trajectory error of the spoofer is reduced by 54.8% when the improved algorithm is used. The improved algorithm can make UAV deviate from preset trajectory to spoofing trajectory more effectively and more subtly.
Boxting: A Blockchain based voting system

Voting is an essential part of a democratic process, from scholarly elections to presidential elections the voting process represents the choice of most voters. However, over time there have been many alerts and reports of fraud cases during important voting events using a traditional process and, as a result, the community and population have been affected in different ways. In this paper, we present Boxting, an internet-based solution using blockchain technology to boost transparency and security in elections. The main proposal of this research it’s to replace traditional processes that are vulnerable to different types of fraud such as pre-filled or dumped ballot boxes, identity theft, vote rigging, vote buying, or electronic systems crashes. This new approach uses blockchain to ensure the immutability of sent data and seeks to provide confidence to electors by guaranteeing that their votes will not be changed over time. Boxting testing results in real scenarios show that this approach fits with the elections use case and it’s cost-efficient compared with the current process and could be easily implemented at large-scale voting scenarios.
Frequency Estimation in the Shuffle Model with Almost a Single Message

We present a protocol in the shuffle model of differential privacy (DP) for the frequency estimation problem that achieves error ω(1) ⋅ O(log n), almost matching the central-DP accuracy, with 1 + o(1) messages per user. This exhibits a sharp transition phenomenon, as there is a lower bound of Ω (n1/4) if each user is allowed to send only one message. Previously, such a result is only known when the domain size B is o(n). For a large domain, we also need an efficient method to identify the heavy hitters (i.e., elements that are frequent enough). For this purpose, we design a shuffle-DP protocol that uses o(1) messages per user and can identify all heavy hitters in time polylogarithmic in B. Finally, by combining our frequency estimation and the heavy hitter detection protocols, we show how to solve the B-dimensional 1-sparse vector summation problem in the high-dimensional setting B=Ω(n), achieving the optimal central-DP MSE Õ(n) with 1 + o(1) messages per user. In addition to error and message number, our protocols improve in terms of message size and running time as well. They are also very easy to implement. The experimental results demonstrate order-of-magnitude improvement over prior work.
Poster

In this paper, we revisit the problem of secure shuffling in a three-server setting with an honest majority. We begin with the recent work of Araki. et al. (CCS'21) and use precomputation to improve the communication and round complexity of the online phase of their shuffle protocol. Our simple yet effective shuffling method is not limited to three parties and can be used in a variety of situations. Furthermore, the design of our solution allows for fine tuning to achieve improved efficiency based on the underlying application's parameters. Our protocols are initially presented with semi-honest security and then extended to support malicious corruption.
Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing

Text in one language can be mechanically translated into another language using machine translation (MT). It is possible to anticipate a sequence of words, generally modeling full sentences using machine translation in a single integrated model. Human language's flexibility makes automatic translation an artificial intelligence (AI) challenge of the highest order. A single model rather than a pipeline of fine-tuned models is now the best way to attain state-of-the-art outcomes in machine translation. For example, words having numerous meanings, phrases that use more than one grammatical structure, and other grammar issues make it difficult for a machine to translate; however, many misinterpretations translate to be a breeze. A teacher's job is to assist pupils in overcoming the emotional and cognitive obstacles that stand in the way of developing effective problem-solving abilities. Students will benefit from developing problem-solving abilities since they will apply what they have learned to new circumstances. MT-AI, machine translation technology, and products have been employed in a wide range of applications, including business travel, tourism, and cross-lingual information retrieval. Text translation and phonetic translation are two types of translations that focus on the content of the source language. It is possible to create self-learning systems by injecting machine learning techniques into existing software and then observing the results of such injection. Computer software can translate a massive volume of text in a short period. It takes longer for a human translator to perform the same work as a computer program. The simulation investigation is developed based on correctness and effectiveness, demonstrating the proposed framework's reliability of 95.1%.
Contrastive analysis of English and Indonesian adverbs

This study focuses on contrastive analysis of the characteristics and features of adverbs in English and Indonesian languages. This study explores all similarities and differences of adverbs according to their forms, formations, meanings and positions in both languages thoroughly and accordingly predict what sorts of errors the Indonesian learners of English may encounter. This research uses descriptive method by using library research in which all data are collected from linguistics books from both languages. The findings show that there are similarities and differences almost in all aspects of adverbs in both languages. (1). Both languages have two similar forms, as origin and derivative. But in Indonesian, the original and derivative ones can be classified again into reduplication word. (2). The formations of adverbs in both languages use different affixation systems as inflection and derivation. Indonesian uses various affixes such as prefixes, suffixes and confixes, while English uses only kinds of affixes as prefix and suffix. In forming adverbs from adjectives, English uses a suffix ‘-ly’, and a prefix ‘a-‘, meanwhile most of the Indonesian adverbs have the same form as adjectives or without any addition of affixes. Referring to adverbs derived from verbs, Indonesian ones may be formed through reduplicating the base form of the verbs, while English does not have this system. The formation of adverbs derived from nouns, English uses some suffixes such as, -ly, -ward(s), -ways, -wise, and a prefix, a-. Meanwhile Indonesian uses only one prefix ‘se-, one suffix –nya’ and by reduplicating the base form of the noun (e.g., hati becoming hati-hati), it is not in English ones. (3). In terms of their meanings, even though both English and Indonesian are alike, a great difference appears that several words called auxiliaries verbs in English and some English verbs such as, like, want, and wish, etc. have different word classes, concept and function in both languages. Those auxiliaries’ words and verbs are basically real adverbs as qualifiers in Indonesian. (4). Referring to adverb positions, generally, Indonesian adverbs are more mobile in sentences, meanwhile, English ones have certain common positions except some words should be placed permanently. These differences of adverbs in English and Indonesian are identified as problems caused by language interference for Indonesian students. Consequently, the results of this extensive research may be useful for teachers, students, curriculum development and materials of teaching.
quadcleanR: An R Package for the Cleanup and Visualization of Quadrat Data

Biodiversity is a crucial component of ecosystems, such that the monitoring of biodiversity is a priority for effective conservation programs.For communities of sessile organisms, it is common to implement quadrats (a frame isolating a known area) to quantify the ecosystem's biodiversity.Technological advancements like photo quadrats and machine learning provide potential for the collection of quadrat data from different monitoring programs and for studies to conduct larger scale analyses.However, for quadrat data from different methodologies to be compatible, data must be standardized to the same area.The quadcleanR R package was created to assist in standardizing quadrat areas while maintaining the spatial relationships between identified organisms and substrates as well as assist in the cleanup and visualization of quadrat data.This package aims to facilitate comparability between quadrat data from different sources and streamline data cleanup.
All Birds Must Fly: The Experience of Multimodal Hands-free Gaming with Gaze and Nonverbal Voice Synchronization

Eye tracking has evolved as a promising hands-free interaction mechanism to support people with disabilities. However, its adoption as a control mechanism in the gaming environment is constrained due to erroneous recognition of user intention and commands. Previous studies have suggested combining eye gaze with other modalities like voice input for improved interaction experience. However, speech recognition latency and accuracy is a major bottleneck, and the use of dictated verbal commands can disrupt the flow in gaming environment. Furthermore, several people with physical disabilities also suffer from speech impairments to utter precise verbal voice commands. In this work, we introduce nonverbal voice interaction (NVVI) to synchronize with gaze for an intuitive hands-free gaming experience. We propose gaze and NVVI (e.g., humming) for a spatio-temporal interaction applicable to several modern gaming apps, and developed 'All Birds Must Fly' as a representative app. In the experiment, we first compared the gameplay experience of gaze and NVVI (GV) with the conventional mouse and keyboard (MK) in a study with 15 non-disabled participants. The participants could effectively control the game environment with GV (expectedly a bit slower than MK). More importantly, they found GV more engaging, fun, and enjoyable. In a second study with 10 participants, we successfully validated the feasibility of GV with a target user group of people with disabilities.
Facial emotions recognition using local monotonic pattern and grey level co‐occurrence matrices images aided development

Abstract In this article, local monotonic pattern (LMP) paired with grey level co‐occurrence matrix (GLCM) methods are suggested to identify facial emotions with a high identification rate even when the face pictures are rotated. The proposed method extracts image features using the properties of the LMP algorithm and features extracted from the GLCM, which are then fed into the Support Vector Machine (SVM) process that reduces the dimensionality of the features vector and classifies the output into different facial expressions or emotions. The SVM performance rate is then compared to the K‐nearest neighbour approach (KNN) to see which method produces the best facial emotion identification and categorization. The study identified facial emotions in the images using advanced algorithms of GLCM and LMP models to be compared. As a result, the accuracy of SVM and KNN was utilized to determine the method's usefulness in classification using the application of MATLAB. A result of more than 93% was achieved using the SVM method compared with 89.4% using the KNN for the recognition process. The study also demonstrated that this approach would lead to more classification outcomes if the LMP and GLCM are combined with an edge‐based technique yielding a new method that is more efficient and more effective.
Googly Eyes: Displaying User's Eyes on a Head-Mounted Display for Improved Nonverbal Communication

Virtual reality (VR) has been becoming increasingly prevalent in daily lives of humans in various settings. One of the concerns for this technology in becoming mainstream is the isolation caused by today's occlusive headsets. An aspect of isolation is related to restricted nonverbal communication between headset-wearing and outside users. To address this gap, we developed "Googly Eyes", a system that acts for the outside users as a window into the eyes of the headset-wearing user. On a smartphone display that was attached to a FOVE head-mounted display (HMD), user's eye movements were visualized in real time using eye-tracking data. Our main goal is to increase social communication between HMD and non-HMD users, hence increase social presence; and increase enjoyment through improved communication. This paper includes the motivation, design rationale, and implementation details of the Googly Eyes along with preliminary results from a pilot study session and preliminary user studies with a small cohort.
Proactive look-ahead control of transaction flows for high-throughput payment channel network

Blockchain technology has gained popularity owing to the success of cryptocurrencies such as Bitcoin and Ethereum. Nonetheless, the scalability challenge largely limits its applications in many real-world scenarios. Off-chain payment channel networks (PCNs) have recently emerged as a promising solution by conducting payments through off-chain channels. However, the throughput of current PCNs does not yet meet the growing demands of large-scale systems because: 1) most PCN systems only focus on maximizing the instantaneous throughput while failing to consider network dynamics in a long-term perspective; 2) transactions are re-actively routed in PCNs, in which intermediate nodes only passively forward every incoming transaction. These limitations of existing PCNs inevitably lead to channel imbalance and the failure of routing subsequent transactions. To address these challenges, we propose a novel proactive look-ahead algorithm (PLAC) that controls transaction flows from a long-term perspective and proactively prevents channel imbalance. In particular, we first conduct a measurement study on two real-world PCNs to explore their characteristics in terms of transaction distribution and topology. On that basis, we propose PLAC based on deep reinforcement learning (DRL), which directly learns the system dynamics from historical interactions of PCNs and aims at maximizing the long-term throughput. Furthermore, we develop a novel graph convolutional network-based model for PLAC, which extracts the inter-dependency between PCN nodes to consequently boost the performance. Extensive evaluations on real-world datasets show that PLAC improves state-of-the-art PCN routing schemes w.r.t the long-term throughput from 6.6% to 34.9%.
Estimating Spinning Reserve Capacity With Extreme Learning Machine Method in Advanced Power Systems Under Ancillary Services Instructions

In advanced power systems, to balance load generation mismatches in the face of unexpected events and to meet unforeseen possibilities, load taking and shedding instructions are required under Ancillary Services (AS), which are performed by providing spinning reserve (SR) in the units. SR is an important factor, which is rather uncertain to predict due to the unpredictability of customers’ consumption, excessive or under-energy generation, and unpredictability in the integration of renewable energy sources. In this study, integration of renewable energy is addressed as a factor for predicting SR. Increasing the integration of solar and wind into the power system requires larger amounts of SR, which creates a significant increase in generation and emission costs. Optimum estimation of SR capacity helps system operators (SO) plan generators in advance and in a better bidding environment. In the past, estimation tools such as feed forward networks and time series models have been used to estimate load and electricity generation. In this article, Extreme Learning Machine (ELM) method is used to estimate SR capacity in the day-ahead and intraday market. The results obtained with ELM were also compared with the results observed with ANN, LR and SVM methods.
Bayesian mixture variational autoencoders for multi-modal learning

This paper provides an in-depth analysis on how to effectively acquire and generalize cross-modal knowledge for multi-modal learning. Mixture-of-Expert (MoE) and Product-of-Expert (PoE) are two popular directions in generalizing multi-modal information. Existing works based on MoE or PoE have shown notable improvement on data generation, while new challenges such as high training cost, overconfident experts, and encoding modal-specific features also emerge. In this work, we propose Bayesian mixture variational autoencoder (BMVAE) which learns to select or combine experts via Bayesian inference. We show that the proposed idea can naturally encourage models to learn modal-specific knowledge and avoid overconfident experts. Also, we show that the idea is compatible with both MoE and PoE frameworks. When being a MoE model, BMVAE can be optimized by a tight lower bound and is efficient to train. The PoE BMVAE has the same advantages and a theoretical connection to existing works. In the experiments, we show that BMVAE achieves state-of-the-art performance.
MRI Thigh Sequences in Determining the Tumor Size Using Fuzzy C-Means for Patients with Osteosarcoma

Osteosarcoma is the common type of bone cancer in children and adolescents. A magnetic resonance imaging (MRI) is one of the medical imaging techniques used by specialist to diagnose the medical conditions of Osteosarcoma patient. A radiofrequency pulse and gradient sequence known as MRI sequence produces a set of pictures with a specific appearance. In clinical, radiologists need to interpret MRI images and correlating them from various sequences for medical image findings. The process requires a lot of human input and therefore it is subjective, time-consuming, and non-reproducible. Image segmentation can be used to automate MR images into different segments. In image processing, various algorithms used to segment the medical images into region. However, due to the overlap of grayscale pixel values make the segmentation process becomes very difficult and challenging. The purpose of this study is to extract tumor on MRI Osteosarcoma based on three MRI thigh sequences namely T1, T2 and T1_FSE+GADO. The area and perimeter of the extracted tumor are then compared with the ground truth. In this study, two algorithms namely OTSU Thresholding (OT) and Fuzzy C-Means (FCM) were used to perform the segmentation on the MRI Osteosarcoma images. The performance of these two algorithms on segmenting the MRI Osteosarcoma from three MRI sequences are compared and discuss. The result shows that FCM could discriminate the abnormal region better in T1_FSE+GADO sequence. The average percentage error for area in T1_FSE+GADO sequence is 6.20% and average percentage error for perimeter is 6.74% compared to T2 sequence which is 7.18% and 7.71%.
Systematic Literature Review of Security Control Assessment Challenges

The advancement of cybersecurity has called for active effective information security management. Security control (SC) assessment must be empowered to ensure that security implementation is effective and provides expected protection. Lack of comprehensive literature analysis on SC assessment compared to risk and threats assessments are concerning. This research aims to systematically review the trends of SC assessment by identifying, categorizing and analyzing the challenges and available solutions of SC assessment. 34 articles were qualitatively selected with a definite contribution in SC assessment. These articles were reviewed using thematic analysis according to Theme 1: Assessment Challenges, and Theme 2: Proposed Solution. Findings from each theme are systematically categorized to answer research questions. The results of this review are significant in identifying the issues and areas of improvement for future research and can serve as the baseline for SC assessment characteristics.
Cervical Pre-cancerous Cell Image Classification System Using Histogram of Oriented Gradients and K-Nearest Neighbor Algorithms

Cervical cancer is a dangerous disease, with more than 99% of which contain Human Papillomavirus (HPV), threatening women worldwide. The Global Burden of Cancer Study (Globocan) has recorded 36,633 cervical cancer cases, ranking second in Indonesia. Analysis of Pap smear results manually as an early detection effort possesses many weaknesses. Therefore, as an early detection step in diagnosing cervical pre-cancerous cell images, an artificial intelligence system is highly required to assist medical personnel in providing fast and accurate diagnostic evaluations. This study utilized 972 training image data and 108 testing image data, with a cervical pre-cancerous cells image classification system using Histogram of Oriented Gradients (HOG) algorithms for feature extraction and KNN machine learning for the classification system. The gray level in the contrasting images between the texture of the nucleus, cytoplasm, and background had different pixel and bit depth intensity values. Hence, HOG obtained bin orientation for each pixel in the cell. The cosine KNN model demonstrated the best matrix performance, acquiring classification results of 0.8 for accuracy, 0.8 for precision, 0.889 for recall, 0.846 for specificity, and 0.771 for f-score. Moreover, the training data generated an accuracy of 69.3% and the fastest training time of 4.2359s.