Reviewerly/exHarmony huggingface dataset:
A Pearson-Type VII Distribution With Adaptive Parameters Selection-Based Interacting Multiple Model Kalman Filter

Existing robust filters under generalized nonstationary noises conditions are difficult to choose suitable prior parameters, this brief proposed a Pearson-type VII distribution with adaptive parameters selection based interacting multiple model (IMM) Kalman filter (IMMPVII KF). In the model conditional filtering process, both the one-step prediction and the measurement likelihood are modeled as Pearson-type VII distributions. They are decomposed into Gaussian-Gamma Hierarchies (GGH), which are then matched to the time-varying heavy-tailed properties of the noises by pre-selecting the sets of shape and rate parameters and the variational Bayesian (VB) technique. Finally, a new model probability update method for filter under non-Gaussian conditions is derived. Simulation results show that the filter proposed in this brief has better robustness and adaptability to generalized non-stationary noises than the existing filters.
Scale-Aware Visual-Inertial Depth Estimation and Odometry Using Monocular Self-Supervised Learning

For real-world applications with a single monocular camera, scale ambiguity is an important issue.Because self-supervised data-driven approaches that do not require additional data containing scale information cannot avoid the scale ambiguity, state-of-the-art deep-learning-based methods address this issue by learning the scale information from additional sensor measurements.In that regard, inertial measurement unit (IMU) is a popular sensor for various mobile platforms due to its lightweight and inexpensiveness.However, unlike supervised learning that can learn the scale from the ground-truth information, learning the scale from IMU is challenging in a self-supervised setting.We propose a scaleaware monocular visual-inertial depth estimation and odometry method with end-to-end training.To learn the scale from the IMU measurements with end-to-end training in the monocular self-supervised setup, we propose a new loss function named as preintegration loss function, which trains scale-aware ego-motion by comparing the ego-motion integrated from IMU measurement and predicted ego-motion.Since the gravity and the bias should be compensated to obtain the ego-motion by integrating IMU measurements, we design a network to predict the gravity and the bias in addition to the ego-motion and the depth map.The overall performance of the proposed method is compared to state-of-the-art methods in the popular outdoor driving dataset, KITTI dataset, and the author-collected indoor driving dataset.In the KITTI dataset, the proposed method shows competitive performance compared with state-of-the-art monocular depth estimation and odometry methods, i.e., root-mean-square error of 5.435 m in the KITTI Eigen split and absolute trajectory error of 22.46 m and 0.2975 degrees in the KITTI odometry 09 sequence.Different from other up-toscale monocular methods, the proposed method can estimate the metric-scaled depth and camera poses.Additional experiments on the author-collected indoor driving dataset qualitatively confirm the accurate performance of metric-depth and metric pose estimations.
Long Short-Term Memory for Non-Factoid Answer Selection in Indonesian Question Answering System for Health Information

Providing reliable health information to a community can help raise awareness of the dangers of diseases, their causes, methods of prevention, and treatment.Indonesians are facing various health problems partly due to the lack of health information; hence, the community needs media that can effectively provide reliable health information, namely a question answering (QA) system.The frequently asked questions are nonfactoid questions.The development of answer selection based on the classical approach requires distinctive engineering features, linguistic tools, or external resources.It can be solved using deep learning approach such as Convolutional Neural Networks (CNN).However, this model cannot capture the sequence of words in both questions and answers.Therefore, this study aims to implement a long short-term memory (LSTM) model to effectively exploit long-range sequential context information for an answer selection task.In addition, this study analyses various hyper-parameters of Word2Vec and LSTM, such as the dimension, context window, dropout, hidden unit, learning rate, and margin; the corresponding values that yield the best mean reciprocal rank (MRR) and mean average precision (MAP) are found to be 300, 15, 0.25, 100, 0.01, and 0.1, respectively.The best model yields MAP and MRR values of 82.05% and 91.58%, respectively.These results experienced an increase in MAP and MRR of 18.68% and 46.11%, respectively, compared to CNN as the baseline model.
BERT Model-based Natural Language to NoSQL Query Conversion using Deep Learning Approach

Databases are commonly used to store complex and distinct information.With the advancement of the database system, non-relational databases have been used to store a vast amount of data as traditional databases are not sufficient for making queries on a wide range of massive data.However, storing data in a database is always challenging for non-expert users.We propose a conversion technique that enables non-expert users to access and filter data as close to human language as possible from the NoSQL database.Researchers have already explored a variety of technologies in order to develop more precise conversion procedures.This paper proposed a generic NoSQL query conversion learning method to generate a Non-Structured Query Language from natural language.The proposed system includes natural language processing-based text preprocessing and the Levenshtein distance algorithm to extract the collection and attributes if there were any spelling errors.The analysis of the result shows that our suggested approach is more efficient and accurate than other state-of-the-art methods in terms of bilingual understudy scoring with the WikiSQL dataset.Additionally, the proposed method outperforms the existing approaches because our method utilizes a bidirectional encoder representation from a transformer multi-text classifier.The classifier process extracts database operations that might increase the accuracy.The model achieves state-of-the-art performance on WikiSQL, obtaining 88.76% average accuracy.
The Data Security and multi-cloud Privacy concerns

The security, privacy, and challenges of establishing trust in cloud computing are examined in this paper. It discusses the issues that must be resolved to guarantee the security, privacy, and reliability of data processed, stored, and shared in cloud architecture. Cloud computing is a rapidly growing field, with more and more individuals and organizations adopting it as their preferred data storage and processing method. However, with this growth comes the need for increased attention to privacy, security, and trust in cloud computing. In this paper, we review the current state of privacy, security, and trust in cloud computing and examine the various strategies and technologies being used to address these concerns. We discuss the importance of end-to-end encryption, strong access controls, and data anonymization techniques in protecting user data in the cloud. Additionally, we analyze the role of trusted third parties, such as auditors and certifications, in ensuring the integrity of cloud services. Finally, we consider the impact of emerging technologies, such as blockchain and homomorphic encryption, on the future of privacy, security, and trust in cloud computing. Overall, our analysis highlights the need for ongoing research and development in this area to ensure that cloud computing remains a secure and trustworthy platform for users.
Highly Secured Hybrid Image Steganography with an Improved Key Generation and Exchange for One-Time-Pad Encryption Method

Steganography is a subject of study that has been used to hide information throughout history. In cryptology science, the information to be hidden is encrypted. Both study subjects are widely used in information security and protection. In our study, digital image steganography, which is one of the application areas of steganography, was developed and applied to hide text in the selected images. While doing this, the low bands that will hide the data using the discrete Haar wavelet transform of the images obtained first. The text to be hidden is encrypted with the one-time-pad algorithm. The key used for the encryption is transmitted to the receiver using a transmission layer based on a Highly Secured Information Exchange Algorithm. The algorithms use a randomly generated key pool maintained by both the transmitter and the receiver. A key is selected from the pool randomly by generating a random key start point for every message. The pool size and the randomness are critical factors in guaranteeing no key repetition, which is a requirement for a one-time-pad. The ciphertext and the key starting point indicator are hidden in the low bands of the pictures by utilizing the least significant bit method. The optimal pixel adjustment process was applied to the pre-stego-images, this resulted in an improvement in the results. The results obtained in this study are compared against the pre-optimal pixel adjustment process results and the results obtained through peer studies. The test results show that the proposed method outperformed all the methods in terms of peak signal-to-noise ratio, structural similarity index metric, mean absolute error, mean consequential error and the encryption key security.
Helping Students Develop a Critical Eye with Refute Questions

To help students develop a critical eye for human and (increasingly) machine-generated artifacts such as code, documentation, and more, this workshop proposes Refute questions. Students are given an artifact created for a stated purpose and asked: Why does the artifact fail to serve that purpose? Students must provide evidence demonstrating this failure. After a hands-on introduction to Refute questions in their originally proposed context (an alternative to 'Explain in Plain English' questions), participants will receive and review a richer variety of Refute questions for autograded formative and summative assessments, targeting a CS1 course in Python. Then, based on their interest, they will join a subgroup to create theme-specific Refute questions and identify theme-specific challenges such as question-generation and evaluation strategies. Finally, as a group, participants will identify a wish-list of Refute-related tools to be created and research questions to be answered. The workshop caters to a diverse audience: CS instructors at all levels, designers of Intelligent Tutoring Systems and interactive e-books, and CS education researchers.
The Design and Development of Augmented Reality (AR) Application for Internet Evolution Learning Topics

Education field has seen numerous revolutions and innovations that have altered the teaching and learning process through the emergence of Information and Communications Technology (ICT) which provides numerous benefits to both educators and students. However, there is concern that students focus in the classroom becomes less due to the lack of pleasurable during the teaching and learning sessions. Thus, the existence of learning using Augmented Reality (AR) applications seems to help students improve their thinking skills in terms of cognitive as well as behaviourism. Therefore, this study proposed the Design and Development of Augmented Reality (AR) Application for Internet Evolution Learning Topics known as AREvo. The methodology used in this study was based on a design and development research (DDR) and ADDIE Model were used for designed and developed AREvo. Several platforms and software were used to develop AREvo. Several experts in Creative Multimedia field from higher education were appointed to evaluate the AREvo in terms of its functionality, content design, interaction design and presentation. The data analyze and obtained is in the form of frequency and feedback. Result revealed that AREvo application can be used as an additional material for instructors as well as students who are taking basic ICT course. In addition, AREvo is expected to provide a positive impact in terms of functionality and usability of this AR application in the present and also in the future.
Comparison of matrix factorization algorithms’ performance in tourist attraction recommendations

In recent years, the vast amount of offered items can overwhelm users in processing the information for decision-making purposes. In this case, recommender systems have become a functional tool to help people make good decisions. Big companies such as Netflix, Spotify, and YouTube have employed recommender systems in their platform. In the tourism industry, the case also exists, especially when travelers visit a tourist city such as Paris or Bali, which offers them many tourist attractions. In this paper, we employ collaborative filtering techniques to model a recommender system for tourist attractions. Singular value decomposition (SVD), SVD++, and non-negative matrix factorization (NMF) algorithms are used in this paper over a dataset which we collected previously from 44 users and 60 tourist attractions. The optimal parameters of the algorithm are searched based on exhaustive searching techniques against a set of parameter combinations. We used 5-fold cross-validation to evaluate the predicted rating in each algorithm in terms of Root Mean Square Error (RMSE) and Mean Average Error (MAE). Results show that using our dataset, the model built in SVD++ had the minimum error but took a longer training time, while NMF gave the worst RMSE but had a faster training time compared to SVD++.
Macroeconomic Gains from Closing Gender Educational Gaps in Niger

This paper explores the state of gender equality and education attainment of girls in Niger.It also estimates the macroeconomic gains from reducing gaps in education between boys and girls using a microfounded general equilibrium model.The analysis shows that Niger has made some progress toward higher educational attainment for girls, but the country still lags far behind other sub-Saharan African countries.The results from the general equilibrium model suggest that closing the gender gaps in education would boost female labor participation, increase income earned by women and improve fiscal outcomes.More importantly, closing the gender gap in years of schooling in each income percentile would boost long-term GDP by 11 percent.These significant economic gains from investing in girls' education will contribute to the achievements of the strategic goals defined under the Programme de Développement Economique et Social (PDES) 2022-26.
Average gradient-based adversarial attack

Deep neural networks (DNNs) are vulnerable to adversarial attacks which can fool the classifiers by adding small perturbations to the original example. The added perturbations in most existing attacks are mainly determined by the gradient of the loss function with respect to the current example. In this paper, a new average gradient-based adversarial attack is proposed. In our proposed method, via utilizing the gradient of each iteration in the past, a dynamic set of adversarial examples is constructed first in each iteration. Then, according to the gradient of the loss function with respect to all the examples in the constructed dynamic set and the current adversarial example, the average gradient can be calculated, which is used to determine the added perturbations. Different from the existing adversarial attacks, the proposed average gradient-based attack optimizes the added perturbations through a dynamic set of adversarial examples, where the size of the dynamic set increases with the number of iterations. Our proposed method possesses good extensibility and can be integrated into most existing gradient-based attacks. Extensive experiments demonstrate that, compared with the state-of-the-art gradient-based adversarial attacks, the proposed attack can achieve higher attack success rates and exhibit better transferability, which is helpful to evaluate the robustness of the network and the effectiveness of the defense method.
Preventing SIM Box Fraud Using Device Model Fingerprinting

SIM boxes have been playing a critical role in the underground ecosystem of international-scale frauds that steal billions of dollars from individual victims and mobile network operators across the globe.Many mitigation schemes have been proposed for these frauds, mainly aiming to detect fraud call sessions; however, one direct approach to this problem-the prevention of the SIM box devices from network use-has not drawn much attention despite its highly anticipated benefit.This is exactly what we aim to achieve in this paper.We propose a simple access control logic that detects when unauthorized SIM boxes use cellular networks for communication.At the heart of our defense proposal is the precise fingerprinting of device models (e.g., distinguishing an iPhone 13 from any other smartphone models on the market) and device types (i.e., smartphones and IoT devices) without relying on international mobile equipment identity, which can be spoofed easily.We empirically show that fingerprints, which were constructed from network-layer auxiliary information with more than 31K features, are mostly distinct among 85 smartphones and thus can be used to prevent the vast majority of illegal SIM boxes from making unauthorized voice calls.Our proposal, as the very first practical, reliable unauthorized cellular device model detection scheme, greatly simplifies the mitigation against SIM box frauds.* These two authors equally contributed.are useful for such attacks is that they provide a means of circumventing the billing system at the local MNOs.Recently, we have witnessed a surge of an emerging type of criminal activities with a renewed interest in SIM boxes [42],[52], [53].Organized crime groups have quickly learned that with SIM boxes, they can mount financial scamming operations targeting people in a certain nation while staying outside the legal boundaries of their targets.Scam calls made through SIM boxes show local phone numbers on the target's mobiles, rendering these calls much less suspicious than traditional VoIP-based scam calls.These financial scams typically have targets in developed countries (where the expected financial gain is large) while their operations take place in less regulated, attacker-chosen countries anywhere on Earth [9].Over 256,000 phone frauds, worth approximately $18.6 billion, occurred in China in 2020 [17], [30].Korea also has suffered a significant loss of approximately $0.6 billion due to scam calls [37].For simplicity, we will refer to those frauds using SIM boxes as "SIM box frauds" throughout this paper.
Research and application of pattern recognition LSTM based bridge data anomaly detection

The effectiveness of bridge condition assessment relies on the reliability of monitoring data, which can have multiple types of anomaly patterns in complex environments, and the accuracy and timeliness of traditional pattern recognition anomaly detection schemes do not meet the needs of practice. To address the multiple types of anomalies in bridge monitoring data, nine features that fit the bridge data situation are constructed using high-frequency acceleration data, and anomaly detection is performed using a pattern recognition LSTM neural network that is sensitive to time series, and run in real time by Flux in the Ganjiang Special Bridge monitoring system. The experimental results show that this scheme achieves a high level of accuracy for each category of anomaly detection, with a 4.53% improvement in accuracy compared to the PRNN neural network scheme. The overall detection time of real-time samples in practice is about 1.10s, and the overall anomaly detection accuracy reaches 99.65%, which meets the need for timeliness and accuracy of bridge anomaly detection system in practice.
CANet: Channel Extending and Axial Attention Catching Network for Multi-structure Kidney Segmentation

Renal cancer is one of the most prevalent cancers worldwide. Clinical signs of kidney cancer include hematuria and low back discomfort, which are quite distressing to the patient. Some surgery-based renal cancer treatments like laparoscopic partial nephrectomy relys on the 3D kidney parsing on computed tomography angiography (CTA) images. Many automatic segmentation techniques have been put forward to make multi-structure segmentation of the kidneys more accurate. The 3D visual model of kidney anatomy will help clinicians plan operations accurately before surgery. However, due to the diversity of the internal structure of the kidney and the low grey level of the edge. It is still challenging to separate the different parts of the kidney in a clear and accurate way. In this paper, we propose a channel extending and axial attention catching Network(CANet) for multi-structure kidney segmentation. Our solution is founded based on the thriving nn-UNet architecture. Firstly, by extending the channel size, we propose a larger network, which can provide a broader perspective, facilitating the extraction of complex structural information. Secondly, we include an axial attention catching(AAC) module in the decoder, which can obtain detailed information for refining the edges. We evaluate our CANet on the KiPA2022 dataset, achieving the dice scores of 95.8%, 89.1%, 87.5% and 84.9% for kidney, tumor, artery and vein, respectively, which helps us get fourth place in the challenge.
A Universal Deduplication Architecture for Secure and Efficient Cloud Storage

Users now produce data at a rate that exceeds their ability to securely store and manage it all, provoking them to entrust their private files to Cloud Storage Providers (CSPs). These companies discreetly inspect users’ files to undertake deduplication, which stores only a single instance of files that are redundant across their user base. By undertaking deduplication in this way, the CSP acquires low-cost storage at the expense of user privacy. This paper proposes universal deduplication, an alternative approach which shifts the advantage of deduplication from the CSP to the users, while ensuring semantic security of the users’ transmitted data. Universal deduplication leverages indications of the trustworthiness of data availability on the Internet, paired with a format to automatically combine client-side deduplication and end-to-end encryption. By referencing data that is publicly available on the Internet, user files can be privately deduplicated without the need to transmit sensitive user data, while simultaneously reducing storage and encryption costs. An architecture for the implementation of universal deduplication is proposed in this paper, along with a preliminary investigation into the feasibility of the proposed concepts.
Тhe development of personal creativity as a scientific and practical problem

The modern society needs individuals who can create something new, who can solve assigned tasks creatively. Thus, along with the acquisition of knowledge, through education students develop their creative abilities. The concept of creativity for the most part refers to the areas of psychology, pedagogics, and art. However, the concept is often found in other fields as well. The problem of creativity was considered by many foreign and domestic scientists in the field of psychology and pedagogics. In these sciences, creativity is defined as a complex, ambiguous phenomenon. Currently, there is no single generally accepted definition of creativity. There are many different views on the concept of creativity, its content and structure. Keywords: creativity, education, creative abilities, creative competence, creative person.