Reviewerly/exHarmony huggingface dataset:
Mobile Application for Agribusiness Crops Transport Vehicle Reservation with First Come First Served Algorithm

In Indonesia, Agribusiness is the most important and biggest economic sector in national economic scale. The vital role of agribusiness in national economy has a significant influence in future economic development. In managing agribusiness system, one of the issues encountered is crops transport issue. No integration of crops transport vehicle data causes a problematic agribusiness crops transport process, even more so for the agribusiness doer who has no transport vehicle or during harvest that naturally needs a lot of crops transport vehicles. Therefore, a mobile-based computer application used to reserve agribusiness crops transport vehicles that can be accessed in real time is needed. The application was developed within 25 days using scrum framework. The reservation system adopted FCFS Algorithm implemented in the mobile application, so it optimally serves transportation reservation with priority of first come first served by the service provider's system.
Violence Detection in Real Life Videos using Deep Learning

These days, it is essential to avoid or identify violence as soon as possible because it is spreading in an unpredictable way. Violence must be identified on real-time films taken by numerous surveillance cameras at all times and in all locations, which makes it difficult to perform. It might be time-consuming to continuously monitor CCTV surveillance cameras, thus it is essential to automatically spot any unusual activity. As soon as violent behaviours take place, it should be able to make a trustworthy real-time detection and notify the appropriate authorities. The dataset contains both violence and non-violence videos from real life situations. A methodology for detecting violence has been presented by us that uses a network similar to the U-NET with the encoder mobilenetv2 to extract spatial features before moving on to an LSTM block for the extraction of temporal features and binary classification. The results of the trial revealed that the precision is 95% and the accuracy is 94% utilising a dataset based on real life situations. The recommended model uses minimal computer resources while yet producing useful results.
Unipolar and Bipolar Mathematical Inference of Weight Adjustment Mode of Single Layer Perceptron on AND Logic Gate

In order to ensure that electrical terminals only “switch on” after the proper logic process has been applied, logic gates are employed to make judgments. Each logic gate has a name that explains how various inputs will affect the potential outcomes. To perform logical functions on one or more binary sequence of inputs and produce a single digital output, logic gates are utilized. Embedded systems, microcontrollers, electrical and electronics circuit boards, and programmable logic applications generally make use of logic gates. When the sensor receives no information, it generates low Output impedance for logic 0. It has been determined that using logic gates to create an edge avoider robotics that lowers project budget and speeds up the robot. With the growth of technology, the neural network could also be used for implementing the logic gate using Single Layer Perceptron neural network often termed as Linear Threshold Gate. This paper attempt to analyze the mathematical inference on the number of execution steps for the weight adjustment in the activation function of AND Logic gate. This inference carried out by varying the input of Single layer Perceptron Neural Network in the form of unipolar and bipolar input. The activation function weight adjustment had carried out using batch and sequential mode of execution. Mathematical implementation of AND logic gate exhibits that Bipolar sequential and Bipolar batch mode weight adjustment had less number of execution steps reaching 100% accuracy by matching the desired and predicted output when compared to the unipolar. The detailed execution steps are produced herewith for the validation of outcome.
Securing Medical Data Records using Blockchain in a Cloud Computing Environment

In the current age of Information technology and Smart systems, where data is the new oil, it has become highly crucial to effectively store and share any sort of digital data. In recent years the high volumes of Medical Health Data have been under the data breaches by the third parties in the current centralized storage systems, raising concerns for patient data privacy and also the inconvenience for patients to access their own Health records. Blockchain technology with the integration of cloud storage for medical records provides enhanced security of data over the network through the immutable nature of blocks in blockchain and an effective decentralized storage system can be achieved by cloud computing. The model discussed in this paper gives the architecture and implementation of Medical Health Records systems secured through Blockchain and cloud technologies with various tools used. The working prototype which has been implemented is discussed with the demonstration of accessing and sharing of medical data by the patients with the doctors and insurance agencies. The proposed system meets the requirements of patient data privacy by giving greater control of medical data to patients.
Classification of Malaria cell images using Deep Learning Approach

Malaria is a disease caused by the bite of infected female Anopheles mosquitos. Symptoms of malaria are fever, vomiting, headache and in extreme cases, it may lead to death. In this research paper, we used patients' red blood cell images and deep learning techniques to build a machine-learning model for blood smear image classification. CNN (Convolutional Neural Network) is one of the deep learning methods which is used for image classification. The previous related works mostly used Support Vector Machine (SVM) for smear image classification which is quite complex. The pre-trained CNN extracts the infected and uninfected blood cell images. In this proposed method, we used deep learning combined with VGG to perform the classification of parasitized and uninfected blood smear cell images. To validate our proposed method, we used datasets from the NIH repository which consists of parasitized and uninfected cell images. In this research, our proposed approach achieved an accuracy of 96.02%.
Long-Short Range Adaptive Transformer With Dynamic Sampling for 3D Object Detection

3D object detection in point cloud aims at simultaneously localizing and recognizing 3D objects from a 3D point set. However, since point clouds are usually sparse, unordered, and irregular, it is challenging to learn robust point representations and sample high-quality object queries. To deal with the above issues, we propose a Long-short rangE Adaptive transformer with Dynamic sampling (LeadNet), including a point representation encoder, a dynamic object query sampling decoder, and an object detection decoder in a unified architecture for 3D object detection. Specifically, in the point representation encoder, we combine an attention layer and a channel attentive kernel convolution layer to consider the local structure and the long-range context simultaneously. In the dynamic object query sampling decoder, we utilize multiple dynamic prototypes to adapt to various point clouds. In the object detection decoder, we incorporate a dynamic Gaussian weight map into the cross-attention mechanism to help the detection decoder focus on the proper visual regions near the object, further accelerating the training process. Extensive experimental results on two standard benchmarks show that our LeadNet outperforms the 3DETR baseline by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$11.6\%~\text {mAP}_{50}$ </tex-math></inline-formula> on the ScanNet v2 dataset and achieves the new state-of-the-art results on ScanNet v2 and SUN RGB-D benchmarks for the geometric-only approaches.
Threshold Attention Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images is essential for various applications, including vegetation monitoring, disaster management, and urban planning. Previous studies have demonstrated that the self-attention mechanism (SA) is an effective approach for designing segmentation networks that can capture long-range pixel dependencies. SA enables the network to model the global dependencies between the input features, resulting in improved segmentation outcomes. However, the high density of attentional feature maps used in this mechanism causes exponential increases in computational complexity. Additionally, it introduces redundant information that negatively impacts the feature representation. Inspired by traditional threshold segmentation algorithms, we propose a novel threshold attention mechanism (TAM). This mechanism significantly reduces computational effort while also better modeling the correlation between different regions of the feature map. Based on TAM, we present a threshold attention network (TANet) for semantic segmentation. TANet consists of an attentional feature enhancement module (AFEM) for global feature enhancement of shallow features and a threshold attention pyramid pooling module (TAPP) for acquiring feature information at different scales for deep features. We have conducted extensive experiments on the ISPRS Vaihingen and Potsdam datasets. The results demonstrate the validity and superiority of our proposed TANet compared to the most state-of-the-art models.
MTHM: Self-Supervised Multitask Anomaly Detection With Hard Example Mining

It is still challenging to detect and locate anomalies by models trained only with normal samples. Methods using image reconstruction as a pretext task can provide precise localization but suffer from harnessing the reconstruction capability on unseen anomalies. This paper proposes a new framework of Multi-Task and Hard example Mining (MTHM) for anomaly detection and localization. The self-supervised multi-task setting creatively takes advantage of the competition among different tasks to learn more compact and efficient representations for detection tasks. Moreover, introducing other semantic tasks allows the shared encoder to learn beyond the pixel-to-pixel mapping of only a single image reconstruction task. Subsequent analysis experiments demonstrate that the proposed method can achieve a more suppressive reconstruction capability for anomalies. During the test process, the outputs of the other tasks can also provide valuable information for anomaly detection and localization. Furthermore, in combination with a novel hard example mining strategy, the byproducts of the image reconstruction task are inexpensively exploited as hard-to-detect samples for enhancing models’ detection ability. And as the model capability increases during training, the detection difficulty of these samples is able to increase adaptively. Our experiments show that hard samples generated in the later training stages can better approximate the real data distribution. With the help of the multi-task framework and hard example mining strategy, our method surpasses many state-of-the-art methods.
PDR: Progressive Depth Regularization for Monocular 3D Object Detection

Accurately predicting object depth is a key challenge in monocular 3D detection task. The perspective projection principle used by most state-of-the-art approaches demands a complex balance between the ratio-form depth estimation and 2D-3D geometric regularizations, and thus can lead to sub-optimal solutions. In this paper, we propose a novel synergistic scheme that can achieve better trade-off among these competing objectives. Our main proposal is a progressive depth regularization (PDR) architecture that splits the overall training process into three sequential depth estimation steps to gradually remove the unwanted deviations induced by the over-regularization. Specifically, our model first learns the coarse depth with the conventional perspective projection and combines the coarse-to-fine generation to reduce the search space of 2D projection height prediction. We then deactivate individual supervision on 2D projection height prediction and introduces a new auxiliary 3D physical height prediction to relax the 2D and 3D regularizations, respectively. Consequently, our PDR leads to more precise depth estimation by mitigating the inherent ambiguities in the geometric priors of perspective projection through progressive regularization relaxation. Extensive experiments on both KITTI and Rope3D benchmark show that our PDR delivers strong performance gains as compared to the previous methods.
Multimodal Mutual Attention-Based Sentiment Analysis Framework Adapted to Complicated Contexts

Sentiment analysis has broad application prospects in the field of social opinion mining. The openness and invisibility of the internet makes users’ expression styles more diverse and thus results in the blooming of complicated contexts in which different unimodal data have inconsistent sentiment tendencies. However, most sentiment analysis algorithms only focus on designing multimodal fusion methods without preserving the individual semantics of each unimodal data. To avoid misunderstandings caused by ambiguity and sarcasm in complicated contexts, we propose a multimodal mutual attention-based sentiment analysis (MMSA) framework adapted to complicated contexts, which consists of three levels of subtasks to preserve the unimodal unique semantics and enhance the common semantics, to mine the association between unique semantics and common semantics and to balance decisions from unique and common semantics. In the framework, a multiperspective and hierarchical fusion (MHF) module is developed to fully fuse multimodal data, in which different modalities are mutually constrained and the fusion order is adjusted in the next step to enhance cross-modal complementarity. To balance the data, we calculate the loss by applying different weights to positive and negative samples. The experimental results on the CH-SIMS multimodal dataset show that our method outperforms existing multimodal sentiment analysis algorithms.The code of this work is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://gitee.com/viviziqing/mmsacode</uri> .
Unsupervised Framework for Evaluating Structural Node Embeddings of Graphs

An embedding is a mapping from a set of nodes of a network into a real vector space. Embeddings can have various aims like capturing the underlying graph topology and structure, node-to-node relationship, or other relevant information about the graph, its subgraphs or nodes themselves. A practical challenge with using embeddings is that there are many available variants to choose from. Selecting a small set of most promising embeddings from the long list of possible options for a given task is challenging and often requires domain expertise. Embeddings can be categorized into two main types: classical embeddings and structural embeddings. Classical embeddings focus on learning both local and global proximity of nodes, while structural embeddings learn information specifically about the local structure of nodes' neighbourhood. For classical node embeddings there exists a framework which helps data scientists to identify (in an unsupervised way) a few embeddings that are worth further investigation. Unfortunately, no such framework exists for structural embeddings. In this paper we propose a framework for unsupervised ranking of structural graph embeddings. The proposed framework, apart from assigning an aggregate quality score for a structural embedding, additionally gives a data scientist insights into properties of this embedding. It produces information which predefined node features the embedding learns, how well it learns them, and which dimensions in the embedded space represent the predefined node features. Using this information the user gets a level of explainability to an otherwise complex black-box embedding algorithm.
NGO Portal - A Platform to connect NGOs with prospective members

NGO Portal is a platform where various NGOs can register themselves and post their day to day activities and events organized by the NGO. Users can also follow other NGOs and keep updated about NGOs they follow. NGOs can post photos, videos, text related to an event or future event. Users can like, comment, and share those posts. Users can also watch the history of the NGO they are interested in. We used tools like ReactJs, ReduxJs, Javascript, HTML, CSS, NodeJs, Express Js to make a web application which can provide all the features to make an NGO portal. We used MongoDB and Mongoose to handle database requirements.
Performance Comparison of Various Features for Human Face Recognition using Machine Learning

Facial features are one of the most vital biometrics and are used to identify an individual. Facial recognition is a technology having the capacity to distinguish a specific individual. This technology mainly concentrates on machine learning techniques to learn, acquire, store, and examine facial features to fit them with a database. In this project, the features are extracted in transform domain. Discrete Wavelet Transform (DWT) is applied on images. In transform domain, features like mean, energy, Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), Gabor Filters are explored. Appropriate features are extracted from LL, LH, HL, and HH bands to build a machine learning model. Database is divided into training and testing. Model is built based on the 80% of images from the database. Model is tested with 20% images of test data. Three important machine learning algorithms are popular. These are Decision Tree (DT), Support Vector Machines and Naive Bayes (NB). NB is used for probability-based inferences. DT is simpler than SVM. Hence, Decision Tree-based Machine Learning is employed to recognize the faces. Accuracy is used to test the performance of various features.
VizOPS: A data-driven ontology to represent public place surveillance data

Large-scale knowledge graphs such as Wikidata and DBpedia have become a powerful asset for semantic search, question answering and extraction of semantic information. However, most of the data in such knowledge-base(s) is based on the textual data with very emphasis on the visual data. Moreover, due to rapid deployment of surveillance systems in public places, massive amount of video data is being produced. Storage and analysis of this data in real time is a substantial challenge. Therefore, there is massive potential in representing the visual data in knowledge graph due to presence of hidden semantics present in images and videos. However, constructing knowledge graph requires ontology, which needs expertise and domain knowledge. In this paper, we present a novel data-driven ontology (“VizOPS”) to represent the visual information of public-place surveillance data to automate ontology engineering process. The ontology is constructed by using the existing public domain benchmark datasets and its metadata. The Class for the ontology is obtained by the existing class from the various public place surveillance data. The relation between two classes is extracted using NLP-based algorithm image captioning datasets. This way the ontology is able to capture the most of the salient surveillance information of real-world public places. The proposed ontology is evaluated by representing the concepts of public places using Visual Genome dataset.
DEFEAT: A decentralized federated learning against gradient attacks

As one of the most promising machine learning frameworks emerging in recent years, Federated learning (FL) has received lots of attention. The main idea of centralized FL is to train a global model by aggregating local model parameters and maintain the private data of users locally. However, recent studies have shown that traditional centralized federated learning is vulnerable to various attacks, such as gradient attacks, where a malicious server collects local model gradients and uses them to recover the private data stored on the client. In this paper, we propose a DEcentralized FEderated learning Against aTtacks (DEFEAT) framework and use it to defend the gradient attack. The decentralized structure adopted by this paper uses a peer-to-peer network to transmit, aggregate, and update local models. In DEFEAT, the participating clients only need to communicate with their single-hop neighbors to learn the global model, in which the model accuracy and communication cost during the training process of DEFEAT are well balanced. Through a series of experiments and detailed case studies on real datasets, we evauate the excellent model performance of DEFEAT and the privacy preservation capability against gradient attacks.
Research on Human Behavior Feature Recognition and Intelligent Early Warning Methods in Safety Supervision Scene Video based on Yolov7

Abstract With the continuous improvement of infrastructure construction in China, the construction of various supporting power facilities is constantly followed up. A large number of ultra-high voltage or high-voltage power facilities, such as high-voltage transmission towers, transformers, or substations, are gradually emerging in populated areas, farmland, or fishponds where people still pass by sometimes. In such circumstances, people who do not have the qualification to contact power facilities at times approach, contact, or even climb these dangerous high-voltage transmission and distribution facilities illegally for various reasons, which not only poses a huge hidden peril or even threat to the safety of people’s lives and property but also cause a huge negative impact on the smooth operation of power infrastructure. To solve this intractable problem, this topic proposes a pedestrian detection and recognition algorithm, which can accurately distinguish between ordinary pedestrians and power grid maintenance inspectors based on YOLOv7, providing a reliable method for timely detection and early warning of pedestrians who may contact power facilities.
Design and Implementation of Multi-Mode Surveillance System for Several Applications

With the rise in crimes, thefts, stealing, burglary, and offensive violations in today's technological age, it is critical that we install cameras in every conceivable public place to discover accused / criminals / fled persons. Security agents, such as cops, use public camera footage to track down the accused's movements. People are assigned to this responsibility of watching the public camera recordings. These individuals must constantly watch the detected faces and compare them to the accused faces. However, because this is a continual process, there is a high risk of manual errors when utilizing this method. The accused person's face can also be discovered using this method, but by the time the assigned person tells the nearest police station, the accused person may have left/escaped. With all of these considerations in mind, it is necessary to avoid these blunders. Manual mistake in determining the perpetrator's face and relaying the information to officials swiftly are the main problems. Installing a camera with an already supplied data collection of suspected people can help cross-check the detected faces with the uploaded data set, which is the first issue. This improves the precision of the accused's detection. The second issue is telling officials about the suspected criminal or accused in a timely manner. If the installed camera has a transmitting unit, with the receiver on the official side and the transmitter on the camera end, this will work. The information is swiftly transmitted to the authorities, and there is a good possibility that the perpetrator will be apprehended. A frequency band is fundamental for any transmission to actually occur. The frequency ranges essential for the above-mentioned transferring data are 400–500 Hz and 900–1000 Hz. The LoRa module will be used to use these bandwidths.