Reviewerly/exHarmony huggingface dataset:
Massive Connectivity and Low-Latency for Next-Generation Internet of Things: A Filtered OFDM-based Deep Learning Approach

Abstract The Internet of Things (IoT) is one of the numerous services offered by sixth-generation (6G) mobile communications. It is necessary to meet heterogeneous criteria for large connectivity and low latency to serve the various kinds of IoT applications. In order to concurrently provide enormous connectivity and meet the low-latency conditions in the uplink IoT network, we proposed filtered orthogonal frequency division multiplexing (FOFDM) based on a service group adopting the deep learning (DL) technique. The proposed FOFDM-DL platform focuses on two key areas: first, it works for the concurrence of different time-frequency granularity appropriate for distinct service-based grouping, and secondly, it facilitates low latency and massive connectivity to deliver dependable communications. The suggested FOFDM-DL architecture may accommodate the needs of massive machine-type communication referred as mMTC and ultra-reliable and low-latency communications known as uRLLC concurrently for the next-generation communication systems. However, the uRLLC and mMTC requirements can only be supported separately by the new radio (NR)-5G, beyond 5G (or 6G). In comparison to the traditional scheme, simulation results demonstrate that the suggested FOFDM-DL platform works surprisingly well in the next-generation IoT network.
Efficient Data Management with Indexing and Searching in Cloud Storage

With the rapid increase of data from various sources, traditional file-based methods are inadequate to manage the sheer volume, diversity, and speed of information. Efficiently managing and retrieving large amounts of data stored in the cloud requires the crucial ability to index and search within cloud storage. The management and retrieval of data have been revolutionized by indexing and searching in cloud storage. Cloud storage providers utilize advanced indexing techniques to provide users with robust search capabilities, allowing for efficient data retrieval from large repositories. Integrating advanced search algorithms and features significantly enhances the precision of searching and users can locate information quickly and accurately. The evolution of cloud storage is critical to maintaining efficient indexing and searching capabilities that ensure smooth data management and accessibility. This paper introduces a novel approach to indexing and searching encrypted cloud data using a backtracking technique. The suggested approach is simple and effective for conducting searches. It selects only potential keywords from the search query. According to the numerical analysis, the scheme has effectively reduced the computational expenses of search results. The proposed scheme is highly efficient and secure compared to other existing works.
If Human Can Learn from Few Samples, Why Can’t AI? An Attempt On Similar Object Recognition With Few Training Data Using Meta-Learning

An age-old problem that persists with deep learning is that it requires thousands of training samples to learn. Transfer learning is an advancement to deep learning through which learned models can be adapted to recognize new data. But, learning new data with transfer learning depends on the properties of the previous data the model was trained on. In cases where the properties of the new data are different and all the classes of the new data have very few contrasting properties, transfer learning does not produce reliable results. Therefore, we require a method to learn properties from the dataset having low variation among classes using very few samples. To achieve this, we have proposed the usage of a Few-Shot Meta-Learning algorithm and tested this on two open-source datasets having similar-looking classes. The results from the experiment show that the proposed method can reach up to 100% classification accuracy with only 30 samples. Using the same number of samples, transfer learning obtained up to 41.9% accuracy, and general convolution neural network obtained up to 32.0% accuracy. The model can be implemented to reduce the requirement of a large number of annotated training samples to train deep learning models.
Travel Route Recommendation System Based on Weather Prediction and Geolocation Technology

Information regarding weather conditions is quite challenging to obtain and predict. People who in their daily lives have to travel to a destination, such as workers or traveling salesman, cannot predict the weather they will encounter during their journey. The impact is that people often experience some losses when traveling. This research produces an Android-based application used by the general public to avoid travel losses caused by weather. The application provides travel recommendation information in the form of routes obtained from the Google Directions API and weather predictions obtained from the Dark Sky API. This application helps users predict weather predictions on their path to reduce the risk of rain loss. The results of black-box testing and user acceptance found that Android-based applications managed to get a level of 84% in providing travel recommendations, achieving 80.6% success in predicting the weather and obtaining 76.6% in reducing rain risk.
Comparative Analysis of Interactive Map Data Loading Using the GeoJSON Method for West Java New Entrepreneurs

The purpose of this study is to analyze interactive maps using the GeoJSON method and without using GeoJSON for new entrepreneurs in West Java. The flow of the stages of this research consists of three parts, namely analyzing the code, implementing the code, and testing the code that has been created using GeoJSON and without GeoJSON. The results obtained in this study are using GeoJSON to display data on an interactive map faster than without using GeoJSON. The downside of using GeoJSON on an interactive map is that the app developer is more likely to write down the source code on loading the interactive map.
Implementation of Science Learning Application Two Seasons Weather Change Material Based of multimedia For Deaf Students

This research is a continuation of previous research, namely the Design of Multimedia Based Two Season Weather Change Science Learning Application For Deaf Students Part B for Deaf. This study will discuss how to implement programs that have been made into applications that can be used by user with prototype approach. In addition, a measurement of user satisfaction with the application was carried out. To measure user satisfaction with the application, descriptive and quantitative methods were used, using a questionnaire distributed to seven respondents, namely six students of grade VI and one science teacher at SLBN Cicendo Bandung, with seven questions. From the results of the questionnaire distribution, the satisfaction level of the respondents was 95%, which means that the user is satisfied with the multimedia application, where this application can help in the process of teaching science, especially the material of two seasons weather change. The results of this research contribute to how the learning model is more interesting for deaf students and makes it easier for teachers to explain the material compared to conventional teaching methods.
A Low-complexity Max Unpooling Achitecture for CNNs

In the training of Convolutional Neural Networks (CNN), the conventional max unpooling architecture introduces significant sparsity for zero-filling during upsampling, leading to additional resource costs in adjacent deconvolution layers. In order to leverage the high sparsity of the recovery matrix, this paper proposes a low-complexity hardware architecture. This architecture solely stores the input matrix in the max unpooling layer, eliminating the need for the upsampling recovery process, while mapping the stored relative indices from the forward inference to deconvolution. Moreover, the deconvolution process exclusively involves non-zero elements, reducing computational and storage requirements. This paper derives the distribution of the maximum number of non-zero values corresponding to the deconvolution window for different layers, demonstrating its compatibility with networks of various sizes. Synthesizing the proposed architecture on Arria 10 AX115N2F45E1SG, for the VGG16, it can save 60.7% of ALMs and 68.8% of storage resources, Moreover, the proposed architecture enhances the frequency by 8.0%.
A Unifying Tensor View for Lightweight CNNs

Despite the decomposition of convolutional kernels for lightweight CNNs being well studied, existing works that rely on tensor network diagrams or hyperdimensional abstraction lack geometry intuition. This work devises a new perspective by linking a 3D-reshaped kernel tensor to its various slice-wise and rank-1 decompositions, permitting a straightforward connection between various tensor approximations and efficient CNN modules. Specifically, it is discovered that a pointwise-depthwise-pointwise (PDP) configuration constitutes a viable construct for lightweight CNNs. Moreover, a novel link to the latest ShiftNet is established, inspiring a first-ever shift layer pruning strategy which allows nearly 50% compression with < 1% drop in accuracy for ShiftResNet.
Research on Interaction Design of Snow Shan Shui Game Based on VR Technology

This paper proposes a research on Interaction design of snow Shan shui game based on VR technology. First of all, it summarizes the virtual reality (VR) technology and its application in game design, focusing on the analysis of the manifestation of snow Shan shui and its use in game design. This paper deeply studies the VR rendering technology of snow Shan shui, including the system requirements and specification definition, the 3D modeling technology of snow Shan shui, the development of rendering algorithm and calculation formula, and the technical realization of light and texture simulation. The article also studies the Interaction design of VR games, discusses in detail the design and implementation of user interaction mode, the design and implementation of interaction awareness algorithm, and the technical strategy of UI/UX design. In addition, the article also explores the key stages of system implementation and testing, including programming and integration of game systems, design and execution of functional and non functional tests, evaluation and optimization of system performance, as well as user testing and feedback collection <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> .
QR Code Secret Sharing Algorithm Based on CRC

QR codes, as a widely employed technology for information transmission, have gained significant popularity in diverse fields owing to their convenient means of transfer, robust error correction mechanisms, rapid decoding capabilities, and substantial information storage capacities. In this study, we present a secret sharing algorithm for QR codes that leverages the computational mechanism of cyclic redundancy check code, thereby offering a clandestine and high-capacity secret payload. Throughout the sensitive message-sharing process, the cover QR codes exhibit no discernible distinguishing features, thereby considerably diminishing the likelihood of arousing suspicion among potential adversaries. Successful reconstruction of the secret information necessitates the collaborative involvement of all participants, ensuring the joint decoding of the shared sensitive message. Our proposed solution retains the error correction capabilities inherent in QR codes while offering additional advantages, including excellent concealment, strong robustness, low computational complexity, and a high capacity for data embedding.
An Efficient Anti-Tampering Image Watermarking Algorithm with Low Distortion

The existing anti-tampering image digital watermarking algorithms are difficult to balance the performance of watermarked image quality, tampering detection and localization. The paper proposes an efficient anti-tampering image watermarking algorithm with low distortion. The proposed algorithm dualizes the efficients of the DWT transformed image, and then modifies the DWT efficients corresponding to the sequence of embedding positions using a low-loss strategy, with the result that the values of the dualized DWT efficients are exactly equal to the corresponding watermark bits. To improve the quality of the watermarked image, the chaotic key segmentation adaptive watermarking algorithm is further proposed. The experimental results show that the proposed algorithm can not only calculate the tampering rate, but also locate the tampering location, and the quality of the watermarked image is improved.
Machine learning can reliably predict malignancy of BI-RADS 4a and 4b breast lesions based on clinical and ultrasonographic features

Objective: The objectives of this study were to establish the most reliable machine learning model to predict malignancy in BI-RADS 4a and 4b breast lesions and optimize the negative predictive value to minimize unnecessary biopsies. Methodology: We included clinical and ultrasonographic attributes from 1,250 breast lesions from four institutions classified as BI-RADS 3, 4a, 4b, 4c, 5, and 6. We selected the most informative attributes to train the models in order to make inferences about the diagnosis of BI-RADS 4a and 4b lesions (validation dataset). Using the best parameters and hyperparameters selected, we tested the performance of nine models and 1,530 ensemble models. Results: The most informative attributes were shape, margin, orientation, and size of the lesions, the resistance index of the internal vessel, the age of the patient, and the presence of a palpable lump. The highest mean NPV was achieved with XGBoost (93.6%). The final performance of the best ensemble model was NPV=96.4%, sensitivity=81.5%, specificity=84.1%, PPV=46.8%, f1-score=59.5%, and the final accuracy=83.7%. Age was the most important attribute to predict malignancy. The use of the final model associated with the patient’s age would reduce by 51% the number of biopsies in women with BI-RADS 4a or 4b lesions. Conclusion: Machine learning can predict malignancy in BI-RADS 4a and 4b breast lesions identified by ultrasonography, based on clinical and ultrasonographic features. Our final prediction model would be able to avoid 51% of the 4a and 4b breast biopsies, without missing any cancers.
化学反応槽における入槽作業不要な固着物洗浄の実現性検証

This study proposes a novel cleaning method using originally developed ”cleaning balls” to clean a polymerization reaction vessel. By introducing this method, an operator do not need to enter the reaction vessel and then the safety improves. The newly developed cleaning balls move along with the water flow inside the vessel, and can collide with and remove polymer debris adhered to the wall and other components of the vessel. The cleaning ball has a simple structure consisting of five sheet metal parts and a rubber ball and is easy to assemble. An experiment conducted in an actual vessel reveals that the behaviors of the cleaning balls change depending on their specific gravity. The experiment also demonstrates that cleaning balls can remove polymer debris from the vessel that previously difficult to clean.
Influence of Augmanted Reality Text Learning Media Description Of The Learning Independence of Class Students VII SMP in Central Lampung

This research aims to develop Augmented Reality learning media descriptive text material for class VII SMP and testing the effect of using Augmented Reality learning media for descriptive text on the learning independence of class VII SMP students in Central Lampung. The method used to develop Augmented Reality learning media is method development of Borg and Gall. The population in this study were class VII students of SMP Negeri 3 Terbanggi Besar for the 2023/2024 academic year with a sample size of 75 students. This research uses four data collection techniques, namely interviews, observation, questionnaires and tests. The results of development research show two things. First, the Augmented Reality learning media developed with the Borg and Gall concept was declared feasible by validators with an average score of 82,11%. Second, based on the feasibility test of the developed Augmented Reality learning media , an average score of 90.8 was obtained which is included in the very feasible category; The effectiveness test using N-Gain obtained a value of 0.74 which is included in the high category. This means that Augmented Reality learning media is suitable for use in learning descriptive text material and has been proven to be effective in improving students' reading comprehension abilities of descriptive text.
Optimal path navigation for ground robots based on dynamic search of drone occupancy grid map

This study deals with a cooperative route guidance for a ground mobile robot with visually assisted by an autonomous drone. The cooperation between the ground robot and drone is generated within a symbolic motion plannar using behavior tree. The drone observing the ground envrionment generates the occupancy grid map to find the passable routes. The map infromation is shared with the ground robot which also configures the occupancy grid map based on the its own ovservation, and both of the map is integrated to find the optimal route for the ground robot. Experimental results illustrate the feasibility of cooperative navigation strategy.
Land monitoring system: Comparison of traditional machine learning and U-Net convolutional neural networks approaches applied to semantic segmentation of drone imagery

This paper uses traditional machine learning techniques, including supervised learning (SVM, Random Forest, KNN) as well as the U-net algorithm via transfer learning, for the segmentation of aerial images taken by drones. Drones are evolving rapidly and are increasingly being used for a variety of professional missions, going beyond their initial use for audiovisual or leisure purposes. Drones are playing an active role in the digitization process, and are now being used for highly advanced technical digital content. Their versatility in day-to-day use means that they can replace costly helicopter and airplane operations, as well as the purchase of satellite images, by offering superior image quality over a well-defined area. In this image segmentation process, images of flooding in Dakar collected by a drone over a well-defined area were used to construct georeferenced orthophotos. Then, the second step consists in creating labels for our images and augmenting the data to broaden our training base through data augmentation, in order to assess the advantages and limitations of the different methods to guide future choices between the model derived from transfer learning with Resnet50, the SVM algorithm, Random Forest and KNN. A comparison of these different methods shows that the Resnet50 algorithm performs best, with an average IoU = 0.8232403. These results will be used to track changes in the classes defined, enabling us to efficiently monitor land use, flooding, identify logged areas and so on.
Collation of Face Recognition Techniques: Image Registration and Dimension Reduction Approaches

Faces must be present in images for intelligent vision-based human computer interaction to work. Since many years ago, face recognition research has been ongoing and significant. This procedure entails facial tracking, expression recognition, and many other things. It is crucial to first register the positions of the photos in this process. However, maintaining such a database is highly challenging because of pose invariance, illumination invariance, shift invariance, scale and noise invariance. The Image Registration Algorithm will be used to register all of the unknown photos that are currently in the database. With the use of a reference, this registration technique tries to align a pattern image. This algorithm will produce features that are independent of translation, scaling and rotation. High dimension space, which must be decreased using a dimension reduction algorithm, is the primary challenge in face recognition. This low dimension subspace can be produced using dimension reduction methods like as Linear Discriminant Analysis, Principal Component Analysis and Locality Preserving Projection. In order to find the needed image from the given database, this study will first minimize the dimensions of every image that is now there.
Machine Learning Heuristics in Cloud Computing: An In-Depth Analysis of VM Migration and Job Scheduling Strategies

Cloud computing has ushered in a transformative era of scalable and flexible IT resource management. Within this paradigm, virtual machine (VM) migration plays a pivotal role in resource optimization, load balancing, and ensuring robust fault tolerance. Recently, machine learning (ML) heuristics have emerged as a disruptive force in VM distribution, empowering dynamic and intelligent allocation of computational resources. This research review paper presents an in-depth and sophisticated analysis of diverse migration techniques that leverage ML heuristics to facilitate VM distribution in complex cloud computing environments. The paper thoroughly examines pertinent literature, performs an intricate comparative analysis, and elucidates the manifold advantages, nuanced limitations, and potential applications of these cutting-edge methodologies.
Artificial Intelligence methods to help measure sensory disorders : approaches and perspectives

The objective of this article is to present an approach aimed at aiding and automating the diagnosis of sensory particularities. We hypothesize that observing patient reactions using connected objects, combined with artificial intelligence, can objectively measure these particularities over time. Based on the comparison of questionnaires traditionally used to establish a sensory profile, we propose a meta-model that covers all possible sensory particularities in an individual. Using this meta-model, a device composed of connected objects enabled us to identify each action and associate them with a signature in the sensor signals. The benefits are numerous: contributing to the development of regular and frequent profiles, assisting practitioners in considering the temporal variation of sensory profiles in their decision-making, and objectifying the measurements.
MODELING OF TRANSFORMATION PROCESSES OF NATURAL-MAN-MADE LANDSCAPES OF THE MIDDLE REGIONS OF THE SOUTHERN URALS

The article examines the results of studies of landscapes formed by the activities of technical structures - quarries and overburden rocks, terricons, in which anthropogenic landscapes are formed - called technogenic. The authors presented the classification of technogenic landscapes of the Southern Urals depending on the forms of relief. Career and spoil-extractive landscapes were highlighted, in which multidirectional dynamics were observed. In turn, the quarry subtype is classified by the type of containing rocks into: dolomitic, sandy-clayey and dolomite-clayey, clayey complexes of ore deposits, sandstone. The processes of changing these complexes were modeled by compiling series of self-regulation and transformation of landscapes, which made it possible to track their dynamics and prediction of their state. As a result of the analysis, conclusions were made about the restoration processes in the South Ural natural and man-made landscapes. In almost all investigated technogenic complexes, a transformation of landscapes was observed - their modification from the original state. In particular, natural complexes at the site of extraction of ferrous metal ores and at dolomite complexes, the smallest - in sandy ones, are most affected by the most severe transformation.