Reviewerly/exHarmony huggingface dataset:
Perbaikan Instalasi Pipa Saluran Distribusi Air dan MCK Di Desa Takofi

Takofi Village is one of the sub-districts in Moti District. It has an area of 111.39 Km² and is divided into 6 RTs. Because this village is located on the coast, access to clean water and toilet facilities is minimal and unsupportive in terms of health. PKM 2022 activities carried out by the mechanical engineering study program at the Khairun University Faculty of Engineering, one of which is the repair of water distribution pipe installations and toilets, where the current condition of MCK in Takofi village is still inadequate. As for the implementation of repairs to the structure of water distribution channel pipes and toilets, repairs to the installation of water distribution channel pipes and toilets are carried out as well as advice on how to utilize clean water supply and be able to provide a place for waste disposal from toilets so as not to pollute the water, air, and soil in the Takofi village neighborhood. The results of the identification of problems in the implementation of this activity include; a lack of knowledge about the behavior of using MCK, Lack of knowledge about the use of clean water, damage to MCK distribution channel pipes, and improper arrangement of pipe installations, resulting in the malfunction of the water distribution network in MCK..Keywors; Pipeline Installation, Clean Water, MCK, Takofi Village
Law enforcement and position locatings in LTE network (status and challenges)

Since the advent of cellular networks represented by the first-generation GSM to the current generations (4G, 5G) represented by the LTE network. This brought about a qualitative revolution in cellular networks in terms of quality, speed, and efficiency. The emergence of cellular networks accompanied by the need to locate the user for commercial and marketing purposes, therefore emerge need for the concept of location-based services (LBS). However, with the increased reliance on cellular networks by users, there appeared a second trend to locate the user for security purposes and emergency. This was the first system established in the United States of America, called federal communication commission (FCC) 911. Position locating has also penetrated the military, security, and police investigations fields to locate criminals. Therefore, we review the methods of determining location in LTE networks. We evaluate each method and identify its weaknesses. In addition, we determine the most appropriate method for law enforcement. Based on the evaluation that showed the most effective way to determine the location used by law enforcement authorities is the radio frequency (RF) fingerprint. Then employment of artificial intelligence (AI)/machine learning (ML) to obtain an optimal method.
Ontology Integration for Building Systems and Energy Storage Systems

A building ontology defines the concepts and organization of building data. Such knowledge can be assistance with automatic data access and support data-driven applications in buildings. With technological advances in batteries and energy storage, an increasing number of data-driven building applications now involve both building systems and energy storage systems (ESS), e.g., peak load shaving (PLS). However, existing building ontologies, e.g., Brick, are not designed to include concepts from ESS systems. Given the emergence of building-ESS applications, it has become important to develop ontologies that can cover knowledge about both building and ESS systems.
ALZO: an outdoor Alzheimer's patient tracking system using internet of things

Alzheimer's patients have an abnormal brain that affects some functionalities such as memory and motoric function. Some patients experience disorientation, such as losing their way back home, and impaired lower body motoric function, leading to stumbling. To overcome these problems, we propose a wearable device called Alzo (Alzheimer locator) for tracking Alzheimer patients during outdoor activities. Alzo can detect the patient's location and is also equipped with a fall detection algorithm. The sensor produces an accelerometer and quaternion value, which are used for calculating alpha (represents activity acceleration) and theta (represents body orientation). The location and the patient's fall condition could be monitored using a mobile-based application. The experiments were conducted by operating the Alzo system to detect the patient's location and fall condition. The results showed that Alzo worked for about 3 hours and sent location data 1-5 times if lost or fall detected. Furthermore, thresholds for the fall detection algorithm were 235 m/s 2 (lower-alpha), 8,108 m/s 2 (higher-alpha), and 70 ⸰ (theta). These thresholds were determined based on the experiment which includes standing up, walking, jumping, sitting down, cycling, jogging, bowing, and squatting. From the experiment, the fall detection algorithm achieved 93.33% of accuracy.
Enhancing of coverless image steganography capacity based on image block features

The idea of coverless information hiding has seen a great deal of development since it was initially introduced due to its effectiveness in defeating steganalysis tools. However, the capacity for general coverless information hiding methods to conceal information is limited, as well as no previous methods worked at the other requirements such as robustness. In this paper, a coverless image steganography (CIS) method for increasing capacity is proposed while retaining the robustness. The proposed method consists of several steps. Firstly, the secret data is segmented into segments of the length. Secondly, a suitable image that has features similar to the secret message is selected and divided into non-overlapping blocks. Thirdly, these blocks are transformed into the frequency domain by applying discreet wavelet transform (DWT). Fourthly, building a hash sequence table using a suggested hashing algorithm. Fifthly, to reduce search time an indexing table is built based on the generated hash sequence. Sixthly, match each segment with the generated hash sequences and save the auxiliary information for each matched segment in the image in a file. Lastly, send the stego image and the auxiliary information file to the receiver. The experimental results show that the CIS method produces high capacity compared with previous CIS methods.
Evaluation of Generic Deep Learning Building Blocks for Segmentation of Nineteenth Century Documents

Although the field of computer vision has grown significantly due to the advent of convolutional neural networks (CNNs), electronic analysis of historical documents has experienced scant research and development attention. Recently, however, computer vision has matured to the point where it can be applied to outperform existing, specialized tools for document analysis. This paper demonstrates empirically how state-of-the-art results can be produced by implementing, training, and evaluating generic computer vision models on historical document segmentation tasks. We show the generality of our approach to document analysis and explain how innovation in this domain can arise from combining generic building blocks for computer vision.
Pendampingan Komunikasi Bahasa Inggris dalam Pemasaran Hasil UMKM di Dusun Pamulung, Kabupaten Sumbawa

This program aims to provide experience, information and new knowledge related to the use of basic English during marketing results by the Gita Tambora Tourism Ambassador UMKM sector group, in Dusun Pamulung. The institutions participating in the community service activities are the Kencana Beach Cottages Travel Agency, the Postgraduate School of the Sumbawa University of Technology (UTS), the Sumbawa Islamic College (STAIS), and the UMKM Group Gita Tambora Tourism Ambassador with assistance for a certain period. The activity was carried out 5 February 2023, 24 February 2023 and 10 March 2023. There were five stages, namely; 1) survey activities; 2) interviewing; 3) setting up the sales area; 4) providing basic English communication; and 5) getting ready to serve guests. The results of these activities are: 1) travel agencies gain the trust of the tourism market; 2) Lecturers and students can implement their knowledge and experience; and 3) MSME groups can increase new knowledge regarding how to communicate using basic English with foreign guests. In addition, it will be increasing the economic value from sales suvenirs they sell.
細径管路内走行のための移動ロボット脚駆動機構の小型化検討

Infrastructure facilities have been deteriorating and require a vast number of inspections conducted by skilled engineers. Robots for inspecting pipe and wall surfaces are required because of a shortage of skilled engineers. However, inspection robots are developed for a single environment, not multiple environments such as inside pipes and on walls. Therefore, we focus on both pipe and wall movement and investigate a miniaturization design method for flat-surface and in-pipe mobile robots. From various types of robots, we adopt a leg-type robot that can move regardless of the inspection environment. Furthermore, we combine a wire and a winding take-up mechanism to enable their legs to move up and down. This mechanism reduces the number of actuators used for their legs. We fabricate a compact prototype robot equipped with the devised leg mechanism. The size of robot is 175 mm length, 113 mm wide and 80 mm height. It was indicated that the robot could move forward and turn on a flat surface and forward in a pipe.
Metaverse in Medical Education: A Paradigm Shift

doi: https://doi.org/10.12669/pjms.40.1.8752&#x0D; How to cite this: Farrukh K. Metaverse in Medical Education: A Paradigm Shift. Pak J Med Sci. 2024;40(1):---------. doi: https://doi.org/10.12669/pjms.40.1.8752
A Cluster-Aware Transfer Learning for Bayesian Optimization of Personalized Preference Models

Obtaining personalized models of the crowd is an important issue in various applications, such as preference acquisition and user interaction customization. However, the crowd setting, in which we assume we have little knowledge about the person, brings the cold start problem, which may cause avoidable unpreferable interactions with the people. This paper proposes a cluster-aware transfer learning method for the Bayesian optimization of personalized models. The proposed method, called Cluster-aware Bayesian Optimization, is designed based on a known feature: user preferences are not completely independent but can be divided into clusters. It exploits the clustering information to efficiently find the preference of the crowds while avoiding unpreferable interactions. The results of our extensive experiments with different data sets show that the method is efficient for finding the most preferable items and effective in reducing the number of unpreferable interactions.
Pixel-Wise Detection of Road Obstacles on Drivable Areas by Introducing Maximized Entropy to Synboost Framework

Semantic segmentation plays a crucial role in understanding the surroundings of a vehicle in the context of autonomous driving. Nevertheless, segmentation networks are typically trained on a closed-set of inliers, leading to misclassification of anomalies as in-distribution objects. This is especially dangerous for obstacles on roads, such as stones, that usually are small and blend well with the background. Numerous frameworks have been proposed to detect out-of-distribution objects in driving scenes. Some of these frameworks use softmax cross-entropy measurements as an attention mechanism for a dissimilarity network to find anomalies. However, a significant limitation arises from the segmentation network’s tendency toward overconfidence in its predictions, resulting in low cross-entropy in regions where anomalies are present. This suggests that normal cross-entropy is a low-quality prior for anomaly detection. Therefore, for the task of detecting stones on roads, we propose utilizing a fined-tuned segmentation network with a changed target, from semantic segmentation to maximize the cross-entropy in anomalous areas. With this, we feed the dissimilarity network with a better prior image. Furthermore, due to the lack of datasets with enough samples of stones for pixel-wise detection, we synthetically added stones on images of driving scenes to create a dataset for fine-tuning and training. The results of our comparative experiments showed that our model attains the highest average precision while having the lowest false positive rate at 95% true positive rate when evaluating on a real-stone image dataset.
A Few-Shot Approach to Sign Language Recognition: Can Learning One Language Enable Understanding of All?

Sign language is a unique form of communication in which hand or other body part gestures are used to express oneself. A large proportion of the world’s population has speech and hearing impairments and communicates through sign language. Sign language, like verbal language, varies from country to country. Recent researches on automatic recognition focus on specific sign language of a country and require a large dataset. However, a prevalent issue arises when there is plenty of data available for some sign languages, while other sign languages suffer from data scarcity or non-existence of resources. To tackle this issue, our study presents a novel solution by proposing a few-shot learning approach for automatic sign language recognition. This approach involves training the model using data from a single sign language and then leveraging the acquired knowledge to recognize other sign languages, even when limited data is available for those languages. By bridging the gap between limited data availability and accurate recognition of new Sign Languages via employing this few-shot learning technique, our approach contributes to enhancing communication accessibility for the global sign language community. Our experimental results demonstrate promising performance, showcasing the potential of our model in overcoming the challenges of cross-lingual sign language recognition.
An Automated Framework for Accurately Classifying Financial Tables in Enterprise Analysis Reports

Despite significant advancements in artificial intelligence technology, image processing (classification) has not made as much progress as other fields, especially natural language processing. Slowly, technologies such as Transformers are being implemented in the field of image processing, but due to disparities in data format, they are not widely utilized. Moreover, the size of image data is typically quite large, limiting the amount of data that can be used for training. Therefore, we wish to optimize existing models and proposes a framework for the automatic detection, extraction, and classification of report table images from financial analysis. The framework is designed to address the difficulties encountered by the financial industry, where table data shapes can vary, making it difficult to extract and utilize the data and necessitating extensive manual labor. Detected tables with 99.9% accuracy using Cascade-TabNet and filtered out unnecessary data with 97.93% accuracy using DenseNet. For classification of tables, we developed a mix model that combines various features to achieve 95.2% precision. The overall framework achieved an average precision of 97.37%, making it suitable for automated environments requiring minimal human intervention.
SGFNeRF: Shape Guided 3D Face Generation in Neural Radiance Fields

NeRF (Neural Radiance Fields), as an implicit 3D representation, has demonstrated the capability to generate highly realistic and dynamically consistent images. However, its hierarchical sampling approach introduces a significant amount of redundant computation, leading to erroneous geometric information, particularly in high-frequency facial details. In this paper, we propose SGFNeRF, a novel 3D face generation model by integrating a 2D CNN-based generator and face depth priors optimization method in the same framework. We employ a Gaussian distribution for sampling to extract facial surface information. Additionally, we design a feature decoder to incorporates depth uncertainty into our method, enabling the method to explore regions further away from face surfaces while preserving its ability to capture fine-grained details. We conduct experiments on the FFHQ dataset to evaluate the performance of our proposed method. The results demonstrate a significant improvement compared to previous approaches in terms of various evaluation metrics.
Discriminative Region Enhancing and Suppression Network for Fine-Grained Visual Categorization

Attention mechanisms are intensively devoted to local feature abstraction for fine-grained visual categorization. A limitation of attention-based methods is that they focus on salient region mining and feature extraction, while ignoring the ability to incorporate discriminative and complementary features from other parts of the image. In order to address this issue, we introduce a novel network known as the Discriminative Region Enhancing and Suppression Network (DRESNet). This network efficiently extracts a wide range of diverse and complementary features, thereby enhancing the final representation. Specifically, a plug-and-play salient region diffusion (SRD) module is proposed to explicitly enhances the salient features extracted by any backbone network. The SRD module can adaptively adjust the weights of regions and redirect attention to other non-discriminative regions to generate different complementary features. The proposed discriminative region enhancing and suppression network is free from bounding boxes or part annotations and can be trained end-to-end. Our proposed method demonstrates competitive performance on three fine-grained classification benchmark datasets, as supported by extensive experimental results. Additionally, it is compatible with widely used frameworks currently in use.
A Simple Method to Directly Determine Novel Class Weight of CNN for Generalized Few-Shot Learning

This paper addresses the problem of machine learning for image classification, where a convolutional neural network (CNN) that has already learned the base classes learns novel classes from a small number of examples. Machine learning from a small number of examples is called few-shot learning (FSL). FSL that focuses on the classification performance of both base and novel classes in the joint space of these two classes is a challenging problem called generalized few-shot learning (GFSL). We propose a simple method for GFSL for image classification that adds the output unit of a novel class to the final layer of a CNN that has already trained on base classes and directly sets the weight vector of the output unit. Compared to existing methods, the proposed method has the advantage that the norms of the weight vectors of the final layer of the CNN need not be normalized during the training process on the base classes. We demonstrate the effectiveness of the proposed method on miniImageNet and tieredImageNet, two representative benchmark datasets for FSL.
A New Contrastive Learning Based Model for Estimating Degree of Multiple Personality Traits Using Social Media Posts

Estimating the degree of multiple personality traits in a single image is challenging due to the presence of multiple people, occlusion, poor quality etc. Unlike existing methods which focus on the classification of a single personality using images, this work focuses on estimating different personality traits using a single image. We believe that when the image contains multiple persons and modalities, one can expect multiple emotions and expressions. This work separates given input images into different faces of people, recognized text, meta-text and background information using face segmentation, text recognition and scene detection techniques. Contrastive learning is explored to extract features from each segmented region based on clustering. The proposed work fuses textual and visual features extracted from the image for estimating the degree of multiple personality traits. Experimental results on our benchmark datasets show that the proposed model is effective and outperforms the existing methods.
VITS, Tacotron or FastSpeech? Challenging Some of the Most Popular Synthesizers

The paper presents a comparative study of three neural speech synthesizers, namely VITS, Tacotron2 and FastSpeech2, which belong among the most popular TTS systems nowadays. Due to their varying nature, they have been tested from several points of view, analysing not only the overall quality of the synthesized speech, but also the capability of processing either orthographic or phonetic inputs. The analysis has been carried out on two English and one Czech voices.
Increasing Diversity of Omni-Directional Images Generated from Single Image Using cGAN Based on MLPMixer

This paper proposes a novel approach to generating omni-directional images from a single snapshot picture. The previous method has relied on the generative adversarial networks based on convolutional neural networks (CNN). Although this method has successfully generated omni-directional images, CNN has two drawbacks for this task. First, since a convolutional layer only processes a local area, it is difficult to propagate the information of an input snapshot picture embedded in the center of the omni-directional image to the edges of the image. Thus, the omni-directional images created by the CNN-based generator tend to have less diversity at the edges of the generated images, creating similar scene images. Second, the CNN-based model requires large video memory in graphics processing units due to the nature of the deep structure in CNN since shallow-layer networks only receives signals from a limited range of the receptive field. To solve these problems, MLPMixer-based method was proposed in this paper. The MLPMixer has been proposed as an alternative to the self-attention in the transformer, which captures long-range dependencies and contextual information. This enables to propagate information efficiently in the omni-directional image generation task. As a result, competitive performance has been achieved with reduced memory consumption and computational cost, in addition to increasing diversity of the generated omni-directional images.
Active Semantic Localization with Graph Neural Embedding

Semantic localization, i.e., robot self-localization with semantic image modality, is critical in recently emerging embodied AI applications such as point-goal navigation, object-goal navigation and vision-language navigation. However, most existing works on semantic localization have focused on passive vision tasks without viewpoint planning, or rely on additional rich modalities such as depth measurements. Thus, this problem largely remains unsolved. In this work, we explore a lightweight, entirely CPU-based, domain-adaptive semantic localization framework called Graph Neural Localizer. Our approach is inspired by two recently emerging technologies, including (1) scene graphs, which combine the viewpoint- and appearance-invariance of local and global features, and (2) graph neural networks, which enable direct learning and recognition of graph data (i.e., non-vector data). Specifically, a graph convolutional neural network is first trained as a scene graph classifier for passive vision, and then its knowledge is transferred to a reinforcement-learning planner for active vision. The results of experiments with self supervised learning and unsupervised domain adaptation scenarios with a photo-realistic Habitat simulator validate the effectiveness of the proposed method.