Reviewerly/exHarmony huggingface dataset:
Attention multilayer feature fusion network for 3D object detection

The 3D object detection based on the fusion of 3D point cloud and 2D image is becoming a hot research topic in the field of 3D scene understanding. The key to fusion research is how to effectively fuse these two modes without information loss and interference from different sensor data. To solve this problem, we propose a multi-layer feature fusion framework based on attention mechanism, which takes 3D point cloud and 2D image as inputs for 3D object detection. In order to comprehensively consider the detection of objects of different sizes, we propose a depth fusion module, which extracts local and global features based on the summing fusion of features point by point. Based on this, we propose an attention-based fusion module to effectively fuse multilayer features by estimating the importance of three-level features through attention mechanism, thus achieving adaptive fusion of multi-layer features. Our evaluation experiments were conducted on the KITTI 3D object detection dataset. The proposed AMFF-Net performs consistently well compared to other state-of-the-art methods, particularly in terms of 3D Average Precision (AP) for the "Car" category. It also outperforms most fusion methods in detecting small targets like pedestrians in complex 3D environments. These results have been validated on the KITTI online testing dataset as well.
The readiness to implement digital humanities data curation of four institutional repositories in Indonesia

Purpose This study aims to identify the readiness of institutional repositories in Indonesia to implement digital humanities (DH) data curation. Data curation is a method of managing research data that maintains the data’s accuracy and makes it available for reuse. It requires controlled data management. Design/methodology/approach The study uses a qualitative approach. Data collection was carried out through a focus group discussion in September–October 2022, interviews and document analysis. The informants came from four institutions in Indonesia. Findings The findings reveal that the national research repository has implemented data curation, albeit not optimally. Within the case study, one of the university repositories diligently curates its humanities data and has established networks extending to various ASEAN countries. Both the national archive repository and the other university repository have implemented rudimentary data curation practices but have not prioritized them. In conclusion, the readiness of the national research repository and the university repository stand at the high-capacity stage, while the national archive repository and the other university repository are at the established and early stages of data curation, respectively. Research limitations/implications This study examined only four repositories due to time constraints. Nonetheless, the four institutions were able to provide a comprehensive picture of their readiness for DH data curation management. Practical implications This study provides insight into strategies for developing DH data curation activities in institutional repositories. It also highlights the need for professional development for curators so they can devise and implement stronger ownership policies and data privacy to support a data-driven research agenda. Originality/value This study describes the preparations that must be considered by institutional repositories in the development of DH data curation activities.
SOAda-YOLOR: Small Object Adaptive YOLOR Algorithm for Road Object Detection

Nowadays, road object detection has become a challenging topic and a long-standing problem. In previous algorithms, traditional convolutional neural networks (CNNs) have almost dominated computer vision and achieved considerable successes in different detection tasks. However, Transformer-based models in the past two years have shown impressive performance, and some of which even surpass CNN-based methods and become the state-of-the-art for advanced vision tasks. In this paper, we propose a detection model named SOAda-YOLOR, which uses the current mainstream Bi-FPN concept with the innovative Optimized-NMS. We also propose a novel ShuffleSimAM module to the backbone, and apply some bag-of-freebies such as SIoU loss to road object detection, and finally achieve competitive results.
VLSI Design of Near-Lossless Image Compression using Improved LZW

An improved encoding method based on Lempel-Ziv-Welch (LZW) is proposed, utilizing the advantages of VLSI parallel processing to overcome the drawback of lengthy encoding time in LZW. The improved LZW encoding method enhances the encoding efficiency by 90% and simultaneously improves the compression ratio by 30%. The near - lossless image compression process in this paper incorporates integer DCT, Huffman coding, run-length encoding, and the improved LZW encoding. Compression experiments were conducted on a Kodak image dataset, resulting in an average bit rate of 7.13 bpp (Bits Per Pixel) and a PSNR of 44.5.
A print-scan-resilient watermarking scheme for trademark images

Currently, print-scan-resilient watermarking algorithms remain a challenging research direction. In the industrial field, there is a huge demand for copyright protection of trademark images, which are particularly vulnerable to attacks such as printing and scanning for counterfeiting purposes. Therefore, this paper proposes a print-scan-resilient watermarking scheme based on the human eye’s insensitivity to changes in image brightness, using circular masks and linear interpolation to modify the brightness component and achieve local gradients in colour images for embedding copyright information. Then the information is extracted by comparing the mean brightness values of the modified local region and its concentric outer ring. Because the watermark information is distributed across various local regions of the image and integrated with local brightness gradients, it is less susceptible to damage or forgery even after printing and scanning, ensuring the robustness of the algorithm. Experimental results demonstrate that this method shows excellent robustness when the embedding strength surpasses 5 and the modified area radius is well-suited.
Exploring the Benefits of Data Augmentation in Math Word Problem Solving

Math Word Problem (MWP) is a challenging Natural Language Processing (NLP) task. Existing MWP solvers have shown that current models need to generalize better and obtain higher performances. In this study, we aim to enrich existing MWP datasets with high-quality data, which may improve MWP solvers’ performances. We propose several data augmentation methods by applying minor modifications to the problem texts and equations of English MWPs datasets which contain equations with one unknown. Extensive experiments on two MWPs datasets have shown that data created by augmented methods have considerably improved performance. Moreover, further increasing the training samples by combining the samples generated by the proposed augmentation methods provides further performance improvements.
KEMAMPUAN BERBAHASA ANAK DENGAN SINDROM ASPERGER

This study analyses the case of a child 6 years old with Asperger Syndrome with a particularly high specific cognitive, linguistic, and metalinguistic profile. The method of the study is using qualitative methods. The instrument used to measure the child’s ability is the Wechsler Intelligence Scale for Children (WISC). The test places a high value on answer justification, allowing the interviewer to capture different levels of metalinguistic awareness. Unlike those assessing sonority and sonography, the child provided poor language-linguistic responses on the sonority subtests. These different results are explained in terms of this child's specific difficulties with "open" language systems, such as semantics, despite his excellent "closed" language abilities. The discussion emphasizes the importance of assessing language skills at the aggregate level of gifted children with Asperger syndrome.
Datetime Feature Recommendation Using Textual Information

Analysis to gain new knowledge from huge amounts of data is called data science, and its widespread use is now socially important. Feature engineering, the process of extracting features from data, is one of the main tasks in data science, and since this task relies on the experience of experts, research is being conducted to automate it. In this paper, we propose a novel approach to automate feature identification from textual information in data column names. Specifically, we use techniques of natural language processing and source code analysis, for data descriptions and source codes in Python notebooks to create a knowledge database with a particular focus on datetime features. We develop a recommendation system of datetime features for newly given text information based on that knowledge database. In experiments, we confirmed the classification accuracy of the knowledge database, applied the database to actual forecasting tasks such as home price forecasting, and achieved 5.76% on average for accuracy gain.
Sensor Data Simulation for Anomaly Detection of the Elderly Living Alone

With the increase of the number of elderly people living alone around the world, there is a growing demand for sensor-based detection of anomalous behaviors. Although smart homes with ambient sensors could be useful for detecting such anomalies, there is a problem of lack of sufficient real data for developing detection algorithms. For coping with this problem, several sensor data simulators have been proposed, but they have not been able to model appropriately the long-term transitions and correlations between anomalies that exist in reality. In this paper, therefore, we propose a novel sensor data simulator that can model these factors in generation of sensor data. Anomalies considered in this study were classified into three types of \textit{state anomalies}, \textit{activity anomalies}, and \textit{moving anomalies}. The simulator produces 10 years data in 100 min. including six anomalies, two for each type. Numerical evaluations show that this simulator is superior to the past simulators in the sense that it simulates well day-to-day variations of real data.
Postdigital Settler Capitalism

Embedded as a societal structure, settler colonialism has only intensified in our postdigital age. Under a free-market capitalist economy, digital consumerism undermines Indigenous rights when tech companies have interest in large-scale extraction of raw materials like lithium, gold, and copper on Indigenous lands. While private tech companies extract natural resources from Indigenous lands, this increases socio-cultural conflict, causes harm to the environment, exploits workers, and dispossesses Indigenous Peoples of their rights and their lifeways on their sacred lands. Private interests in the mass production of digital technology, under a settler capitalist system, continues to threaten the welfare of people and lands locally and globally.
Health-Seeking Behaviour and the use of Artificial Intelligence-based Healthcare Chatbots among Indian Patients

Artificial Intelligence (AI) based healthcare chatbots can scale up healthcare services in terms of diagnosis and treatment. However, the use of such chatbots may differ among the Indian population. This study investigates the influence of health-seeking behaviour and the availability of traditional, complementary and alternative medicine systems on healthcare chatbots. A quantitative study using a survey technique collects data from the Indian population. Items measuring the awareness of chatbot’s attributes and services, trust in the chatbots, health-seeking behaviour, traditional, complementary and alternative medicine, and use of chatbots are adapted from previous scales. A convenience sample is used to collect the data from the urban population. 397 samples were fetched, and statistical analysis was done. Awareness of the chatbot’s attributes and services impacted the trust in the chatbots. Health-seeking behaviour positively impacted the use of chatbots and enhanced the impact of trust of a chatbot on the use of a chatbot. Traditional, complementary and alternative medicine was not included in the chatbot, which negatively impacted the use of chatbots. At the same time, it dampened the impact of trust in chatbots on the use of chatbots. The study was limited to the urban population and a convenience sampling because of the need to use the Internet and a smart device for accessing the chatbots. The results of the study need to be used cautiously. The results can be inferred from the relationships’ existence rather than the impact’s magnitude. The study’s outcome encourages the availability of chatbots due to the health-seeking behaviour of the Indian urban population. The study also highlights the need for creating intelligent agents with knowledge of Traditional, complementary and alternative medicine. The study contributes to the knowledge of using chatbots in the Indian context. When earlier studies focus mainly on the chatbot features or user characteristics in the intention studies, this study looks at the healthcare system and the services unique to India.
Data Protection

Forrester places data at the center of their Zero Trust eXtended (ZTX) framework and for good reason: valuable data exists in every organization, and it must be protected. From our Zero Trust perspective, data, which is often the primary target of attackers, is a key enterprise resource that must be secured by PEPs through integration with a PDP, via an identity and metadata-centric policy model.
Normalized class coherence change-based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:mi>k</mml:mi></mml:math>NN for classification of imbalanced data

kNN is a widely used machine learning algorithm in many different domains because of its fairly good performance in actual cases and its simplicity. This study aims to enhance the performance of kNN for imbalanced datasets, a topic that has been relatively ignored in kNN research. The proposed kNN algorithm, called normalized class coherence change-based k-nearest neighbor (NCC-NN) algorithm, determines the label of a test sample by computing the normalized class coherence changes at class and sample levels for every possible class and assigning the sample to the class with the maximum value. It considers the tendency that the minority classes usually show the lower-class coherence than the majority class. NCC-kNN also utilizes the adaptive k for the class coherence, which is calculated in a weighted manner to reduce the sensitivity to the selection of k. NCC-kNN was applied to 20 benchmark datasets with varying class imbalance and coherence, and its performance was compared with that of five kNN algorithms, SMOTE and MetaCost with standard kNN as a base classifier. The proposed NCC-kNN outperformed the other kNN algorithms in classification of imbalanced data, especially for imbalanced data with low positive class coherence.
Hide my Gaze with EOG!

Smart glasses allow for gaze gesture passwords as a hands-free form of mobile authentication. However, pupil movements for password input are easily observed by attackers, who thereby can derive the password. In this paper we investigate closed-eye gaze gesture passwords with EOG sensors in smart glasses. We propose an approach to detect and recognize closed-eye gaze gestures, together with a 7 and 9 character gaze gesture alphabet. Our evaluation indicates good gaze gesture detection rates. However, recognition is challenging specifically for vertical eye movements with 71.2%-86.5% accuracy and better results for opened than closed eyes. We further find that closed-eye gaze gesture passwords are difficult to attack from observations with 0% success rate in our evaluation, while attacks on open eye passwords succeed with 61%. This indicates that closed-eye gaze gesture passwords protect the authentication secret significantly better than their open eye counterparts.
Cybersecurity Teaching Expert Development Project by KOSEN Security Educational Community

Threats of cyber attacks are rampant all over the world. The threats are getting stronger year by year. In 2015, the National Institute of Technology (KOSEN) launched the KOSEN Security Educational Community (K-SEC) and started cyber security training for KOSEN students. This project has two main purposes that one is outstanding cyber security human resource development which objects for improving quality and other one is fostering many KOSEN students who have systematically acquired security knowledge which objects for quantitative expansion. As part of K-SEC, research of cyber security human resource development education has been promoted. A new project ”Highly Advanced Cybersecurity for KOSEN (HACK) ” has been launched within K-SEC to accelerate the achievement of the two main purposes since 2019. HACK is a project based on the simple idea of strengthening KOSEN technical staff in order to foster strong students who have acquired practical cyber security skills. The plan is a three-year plan. HACK has three main objectives which are improving faculties ' and technical staffs' skills, the development excellent teaching materials based on the skills and to create both skills and human resources network. The success of HACK is important to contribute not only to security education but also to the examination of KOSEN or engineering education centered on practical education. In this paper, we will evaluate the effectiveness and legal theory of cyber security practice education for faculty and technical staff who participate the IT course in the first year. In addition, we will verify the effect of improving the skills of each faculty member in the off-season until the start of the second year. Furthermore, we will also consider whether the cyber security skills of the entire technical college have improved in the fiscal year. This year, we are proceeding with the project for the second year. In the second year, based on the results of the first year, we will evaluate, consider and examine for the improvement of the educational content toward the third year, which is the completion year. The evaluation includes the validity of the schedule. We also achieved a certain level of success in the development of teaching materials. This project is in good condition for the continuation of the plan and consideration of new items for the next year and after next year. In this paper, we report on the process of building this sustainable and effective cybersecurity education methodology suitable for KOSEN faculty and staff, and thus for students.
Sentiment Analysis on YouTube Videos for kids: Review

With the increasing emergence of social network applications, people now use social media to share their opinions on daily issues and analyse people's interactions and reactions toward daily activities such as buying a product, watching videos, community services and other viral situations. A significant number of children watch videos using web platforms such as YouTube. However, such platforms can contain useful or harmful videos to children. Many attempts have been conducted in order to rank the quality of YouTube Videos based on the sentiment analysis approach. However, few attention has been given to Kids' YouTube videos. Therefore, this paper investigates existing methods and techniques that concentrate on YouTube video sentiment analysis. The methods classify them based on machine, deep learning approaches. This paper can be beneficial to many researchers in data mining, sentiment analysis area and more related research areas.
On a Chatbot Conducting Virtual Dialogues

We present a demo of the chatbot that delivers content in the form of virtual dialogues automatically produced from the plain texts extracted and selected from the documents. This virtual dialogue content is provided in the form of answers derived from the found and selected documents split into fragments, and questions are automatically generated for these answers.
Image Denoising Method Based on Improved Wavelet Threshold Transform

An improved denoising algorithm based on a new threshold adjusted by a coefficient considering noise variance, and a new threshold function is proposed against the problems existing in current wavelet transform, such as that the fixed threshold formula does not follow the changes of the decomposition scale, the hard threshold function is not continuous at the threshold point, the constant deviation between the original wavelet coefficients and their estimates always exists in soft threshold function, and so on. This paper firstly layered the noisy images with wavelet functions. Then, threshold is calculated with the new threshold formula and the noise is removed through the new threshold function. The threshold is dynamically adjusted in the time-frequency domain and the deviation between the practical and the estimated wavelet coefficients is decreased by adding an adjustable parameter in the new threshold function. Finally, the processed image is reconstructed. Simulation results show that the denoising effect of the proposed algorithm is better than that of traditional wavelet threshold denoising.
Certification of Semantic Perturbations via Randomized Smoothing.

We introduce a novel certification method for parametrized perturbations by generalizing randomized smoothing. Using this method, we construct a provable classifier that can establish state-of-the-art robustness against semantic perturbations including geometric transformations (e.g., rotation, translation), for different types of interpolation, and, for the first time, volume changes on audio data. Our experimental results indicate that the method is practically effective: for ResNet-50 on ImageNet, it achieves rotational robustness provable up to $\pm 30^\circ$ for 28% of images.
Vulnerability Analysis on Noise-Injection Based Hardware Attack on Deep Neural Networks

Despite superior accuracy on most vision recognition tasks, deep neural networks are susceptible to adversarial examples. Recent studies show that adding carefully crafted small perturbations on input layer can mislead a classifier into arbitrary categories. However, most adversarial attack algorithms only concentrate on the inputs of the model, effect of tampering internal nodes is seldom studied. Adversarial attack, if extends to deployed hardware system, can perturb or alter intermediate data during real time processing. To investigate the vulnerability implication of deep neural network hardware under potential adversarial attacks, we comprehensively evaluate 10 popular DNN models by injecting noise into each layer of these models. Our experimental results indicate that more accurate networks are more prone to disturbance of selective internal layers. For traditional convolutional network structures (AlexNet and VGG family), the last convolution layer is most assailable. For state-of-the-art architectures (Inception, ResNet and DenseNet families), as little as 0.1% or one element per channel of perturbations can subvert the original predictions, and over 65% of computational layers suffer from this vulnerability. Our findings reveal that optimization of accuracy, model size and computational efficiency can unconsciously sacrifice the robustness of deep learning system.