Reviewerly/exHarmony huggingface dataset:
Dynamic Sign Language Recognition Using Mediapipe Library and Modified LSTM Method

Hand gesture recognition (HGR) is a primary mode of communication and human involvement. While HGR can be used to enhance user interaction in human-computer interaction (HCI), it can also be used to overcome language barriers. For example, HGR could be used to recognize sign language, which is a visual language expressed by hand movements, poses, and faces, and used as a basic communication mode by deaf people around the world. This research aims to create a new method to detect dynamic hand movements, poses, and faces in sign language translation systems. The Long Short-Term Memory Modification (LSTM) approach and the Mediapipe library are used to recognize dynamic hand movements. In this study, twenty dynamic movements that match the context were designed to solve the challenge of identifying dynamic signal movements. Sequences and image processing data are collected using MediaPipe Holistic, processed, and trained using the LSTM Modification method. This model is practiced using training and validation data and a test set to evaluate it. The training evaluation results using the confusion matrix achieved an average accuracy of twenty words trained, which was 99.4% with epoch 150. The results of experiments per word showed detection accurateness of 85%, while experiments using sentences only reached 80%. The research carried out is a significant step forward in advancing the accuracy and practice of the dynamic sign language recognition system, promising better communication and accessibility for deaf people.
User-Centric Context-Aware Location-Based Service for ATM’s Users

The article discusses a context-aware system designed to help Automated Teller Machine (ATM) users quickly locate a working ATM with cash. Many people rely on ATMs for quick cash withdrawals, but often waste time searching for a working machine. The proposed system takes into account the user’s environmental context, such as their activity, the availability of cash in the ATM, the on/off status of the machine, and the presence of a line or crowd at the ATM booth. The objective of the system is to enhance the ATM locator according to the user’s specific needs, utilizing advanced features to recommend the best option for ATM customers based on their current situation. This user-centric approach aims to provide a more efficient and effective system for ATM users.
Mobility and Health Monitoring in People with Different Abilities: A Prototype Enhancing Independence: Innovating an IoT-Integrated Wheelchair for

Wheelchair is an essential tool for people with disabilities, enabling them to move around independently and participate fully in society. They come in different types, such as manual wheelchairs, power wheelchairs, sports wheelchairs, and pediatric wheelchairs among others. Certain types of disabilities such as Monoplegia, Hemiplegia, Paraplegia, and Quadriplegia pose difficulties in using conventional power wheelchairs. To overcome these hurdles and provide ease to differently-abled individuals, an Advance Monitoring and Assistive Wheelchair (AMAW) is proposed in this work. The Prototype includes a voice-controlled system for controlling the movement of a wheelchair, an IoT-based real-time health monitoring system to monitor the vitals of the patient remotely, a fall detection system for detecting falls, a tracking system for position and location, and an alarm system to alert caretaker in case of a fall. The real-time embedded monitoring system allows the monitoring of the user’s vital signs like temperature, pulse rate and oxygen saturation and the assistive part allows the wheelchair to move around electronically either through voice or through mobile application. With the assistance of various sensors, the data can easily be monitored remotely by the caretaker at regular intervals of the time. The data display on the LCD fitted onto the wheelchair and in the designed mobile application. Furthermore, the whereabouts of the user are sent via the alert system that notifies the caretaker through GSM in case of changes in parameters and if the user has lost the balance. The vitals through the sensors on the prototype has undergone testing on number of individuals with precise outcomes. In comparison to typical joystick-controlled wheelchairs, this project excels in several aspects, such as its ability to stop or turn using voice commands and avoid collisions with people, furniture, fixed objects, and walls. The user friendly AMAW prototype with real-time monitoring, assistance and alert system may serve as a cost-effective solution in maintaining and providing an independent quality life to differently-abled individuals.
An Efficient approach for Firearms Detection using Machine Learning

Each year, there is a significant number of people impacted by gun-related violence globally. To address this issue, we have created a computer-based system that can automatically identify firearms, specifically pistol. Recent advancements in machine learning has shown success in the fields of recognition and object detection. Our system utilizes the You Only Look Once (YOLO V3) object detection model, which was trained on a personalized dataset. Our training results indicate that YOLO V3 outperforms both traditional convolutional neural network (CNN) models and YOLO V2. Notably, our approach did not require high computation resources or intensive GPUs to train our model. By incorporating this YOLO V3 model into our security system, we hope to rescue lives and decrease the occurrence of manslaughter or mass killings. Moreover, detecting weapons or other dangerous materials and preventing harm or risk to human life could be accomplished by integrating this system into sophisticated surveillance and security robots.
Design of a Substitution Box using a Novel Chaotic Map and Permutation

Information security plays a vital role in the security of data, because data is the most important asset for any organization. So, the first and foremost importance in the functioning of any organization, is the security of its data.To secure data, cryptographic techniques play a vital role in securing data. For that thing, chaos plays a vital role in cryptography. For producing chaos, we have implemented a chaotic map, in which we have implemented linear equation along with exponential equation based on conditions, which integrates chaos in the plain text which makes it less open to crypt-analysis. To make a chaotic map, more robust, it must have some properties, which include High non-linearity, SAC values must be nearer to 0.5 and and it should also satisfy the property of Linear probability, Differential uniformity and BIC value that is Bit Independence Criterion. Moreover the values, we achieved in our S-Box were proved to be competitively better from many S-Boxes which have been designed by now.
Pervasive Computing Issues, Vision an Exclusive Relationship Of Pervasive And Cloud Computing

Abstract Pervasive computing also known by the name of ubiquitous computing is a computer paradigm which focuses the integration of self-communicating and intelligent devices into user’s environment and daily life. Its major principle is to infuse computing into real world environment in such way that user cannot even realize it is existing. One of the best available source is mobile devices which can help with everything happening in real life. Mobile devices are not the only sources but they can be use to perform all the operations like downloading, uploading, computing the data, processing any type of data including audio video formats. The major issue could be the storage of such devices and the load taking to perform complex tasks. To tackle such issues, there is a need of cloud computing. Virtual Cloud computing could be the helpful as it provides the environment in which the larger data can be access pervasively. In this paper we will discuss how pervasive computing and cloud computing are associated with each other and how they can be beneficial to each other.
Systematic performance, and Security evaluation of .NET models for accessing database

In .NET, Object Relational Mapping (ORM) is a programming technique used for accessing the database, which has many frameworks, like Entity Framework, LINQ to SQL, NHibernate, Tele rick Open Access, Light Speed. The LINQ to SQL and Entity Framework usability has increased. This is because of the reason that in these two frameworks full CRUD (Create, Read, Update and Delete) operations can be implemented in short time as compared to Transact Queries, which require more time. In case of multiple projects on various models; Transact Query, LINQ to SQL, and Entity Framework, it becomes difficult to decide which model is the best in terms of performance and security. Therefore, in this article, we provide a comprehensive comparison between Entity Framework, LINQ to SQL and Transact Queries in terms of performance and security. For this purpose, we implemented eleven different types of queries on the selected three frameworks. Subsequently, we quantified and evaluated the execution time and memory usage of all the queries. Furthermore, all types of SQL injection attacks have been applied on three separate applications for security evaluation. Our results show that, the Transact Query is more vulnerable to SQL injection attacks as compared to LINQ to SQL and Entity Framework. Our results show that Transact Query outperforms in terms of memory and CPU usage. Our results also help the practitioner in adopting a framework on the basis of query level performance in terms of memory and CPU usage.
ANALISIS EKONOMI RANCANGAN PLTS UNTUK KAWASAN WARISAN BUDAYA DUNIA JATILUWIH

Bali is a province in Indonesia that is progressive in utilizing renewable energy, by making a policy to create Bali Clean Energy, which aims to develop renewable energy including solar energy. Even though several Solar Power Plant developments have been carried out in Bali, it still needs to be accelerated in its application to achieve the set targets. In this study, the Jatiluwih Heritage Area was chosen as the location for designing a Rooftop solar power plant. Jatiluwih is a Village that became a World Cultural Heritage designated by UNESCO in 2012. The methods used in this research are interviews, literature studies, and scientific studies based on related journals. This study uses two scenarios, namely the load usage scenario and the roof area scenario. There are 45 buildings that have tourism activities in the Jatiluwih DTW area, with a daily electricity requirement of 225.37 kWh/day. Based on the criteria for installing a Rooftop Solar Power Plant, there were five buildings that met the criteria, namely Dukuh Batur Villa, Teras Subak Homestay, Warung Dhea, Iga Lawar Restaurant, and Bhuana Agung Restaurant. Based on the investment feasibility study using the Net Present Value, Profitability Index, and Payback Period methods, it shows that the investment in designing a Rooftop Solar Power Plant with a scenario based on load requirements has 2 feasible buildings, namely the Iga Lawar Restaurant and Bhuana Agung Restaurant, and based on the roof area scenario it shows the design investment Solar Power Plant Jatiluwih World Cultural Heritage Area is not feasible to implement.
Conceptual Design of Smart Network Adaptive Traffic Light in Creating Low-Carbon City

The research is led to the development of a smart sensory network adaptive traffic light to optimise traffic flow and reduce congestion on Jalan Persisiran Seri Alam in Pasir Gudang. With variable time management, this system can reduce the time spent at traffic junctions and carbon emitted by vehicles, thus supporting the SDG 11 for low carbon smart city. By integrating the dynamic duration of each traffic signal on each dense pathway, this system can help to reduce traffic congestion, fuel consumption, and CO2 emissions. It can also ensure that traffic is managed in a more efficient manner, thus improving the quality of life for the people of Pasir Gudang. The success of this system will be a major step towards achieving the goal of a low carbon smart city, as it will help to reduce air pollution, noise pollution, and improve road safety. Additionally, it can help to improve the efficiency of traffic flow, leading to better traffic management and reduced congestion.
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking

The existing one-stage multi-object tracking (MOT) algorithms have achieved satisfactory performance benefiting from a large amount of labeled data. However, acquiring plenty of laborious annotated frames is not practical in real applications. To reduce the cost of human annotations, we propose Heterogeneous Diversity driven Active Multi-Object Tracking (HD-AMOT), to infer the most informative frames for any MOT tracker by observing the heterogeneous cues of samples. HD-AMOT defines the diversified informative representation by encoding the geometric and semantic information, and formulates the frame inference strategy as a Markov decision process to learn an optimal sampling policy based on the designed informative representation. Specifically, HD-AMOT consists of a diversified informative representation module as well as an informative frame selection network. The former produces the signal characterizing the diversity and distribution of frames, and the latter receives the signal and conducts multi-frame cooperation to enable batch frame sampling. Extensive experiments conducted on the MOT15, MOT17, MOT20, and Dancetrack datasets demonstrate the efficacy and effectiveness of HD-AMOT. Experiments show that under 50% budget our HD-AMOT can achieve similar or even higher performance as fully-supervised learning.
DenseShift : Towards Accurate and Efficient Low-Bit Power-of-Two Quantization

Efficiently deploying deep neural networks on low-resource edge devices is challenging due to their ever-increasing resource requirements. To address this issue, researchers have proposed multiplication-free neural networks, such as Power-of-Two quantization, or also known as Shift networks, which aim to reduce memory usage and simplify computation. However, existing low-bit Shift networks are not as accurate as their full-precision counterparts, typically suffering from limited weight range encoding schemes and quantization loss. In this paper, we propose the DenseShift network, which significantly improves the accuracy of Shift networks, achieving competitive performance to full-precision networks for vision and speech applications. In addition, we introduce a method to deploy an efficient DenseShift network using non-quantized floating-point activations, while obtaining 1.6× speed-up over existing methods. To achieve this, we demonstrate that zero-weight values in low-bit Shift networks do not contribute to model capacity and negatively impact inference computation. To address this issue, we propose a zero-free shifting mechanism that simplifies inference and increases model capacity. We further propose a sign-scale decomposition design to enhance training efficiency and a low-variance random initialization strategy to improve the model’s transfer learning performance. Our extensive experiments on various computer vision and speech tasks demonstrate that DenseShift outperforms existing low-bit multiplication-free networks and achieves competitive performance compared to full-precision networks. Furthermore, our proposed approach exhibits strong transfer learning performance without a drop in accuracy. Our code was released on GitHub.
A Unified Continual Learning Framework with General Parameter-Efficient Tuning

The "pre-training → downstream adaptation" presents both new opportunities and challenges for Continual Learning (CL). Although the recent state-of-the-art in CL is achieved through Parameter-Efficient-Tuning (PET) adaptation paradigm, only prompt has been explored, limiting its application to Transformers only. In this paper, we position prompting as one instantiation of PET, and propose a unified CL framework with general PET, dubbed as Learning-Accumulation-Ensemble (LAE). PET, e.g., using Adapter, LoRA, or Prefix, can adapt a pre-trained model to downstream tasks with fewer parameters and resources. Given a PET method, our LAE framework incorporates it for CL with three novel designs. 1) Learning: the pre-trained model adapts to the new task by tuning an online PET module, along with our adaptation speed calibration to align different PET modules, 2) Accumulation: the task-specific knowledge learned by the online PET module is accumulated into an offline PET module through momentum update, 3) Ensemble: During inference, we respectively construct two experts with online/offline PET modules (which are favored by the novel/historical tasks) for prediction ensemble. We show that LAE is compatible with a battery of PET methods and gains strong CL capability. For example, LAE with Adaptor PET surpasses the prior state-of-the-art by 1.3% and 3.6% in last-incremental accuracy on CIFAR100 and ImageNet-R datasets, respectively. Code is available at https://github.com/gqk/LAE.
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence

Deep neural networks are vulnerable to adversarial attacks. In this paper, we take the role of investigators who want to trace the attack and identify the source, that is, the particular model which the adversarial examples are generated from. Techniques derived would aid forensic investigation of attack incidents and serve as deterrence to potential attacks. We consider the buyers-seller setting where a machine learning model is to be distributed to various buyers and each buyer receives a slightly different copy with the same functionality. A malicious buyer generates adversarial examples from a particular copy ${\mathcal{M}_i}$ and uses them to attack other copies. From these adversarial examples, the investigator wants to identify the source ${\mathcal{M}_i}$. To address this problem, we propose a two-stage separate-and-trace framework. The model separation stage generates multiple copies of a model for the same classification task. This process injects unique features into each copy so that adversarial examples generated have distinct and traceable features. We give a parallel structure which pairs a unique tracer with the original classification model in each copy and a variational autoencoder (VAE)-based training method to achieve this goal. The tracing stage takes in adversarial examples and a few candidate models, and identifies the likely source. Based on the unique features induced by the tracer, we could effectively trace the potential adversarial copy by considering the output logits from each tracer. Empirical results show that it is possible to trace the origin of the adversarial example and the mechanism can be applied to a wide range of architectures and datasets.
Creative Birds: Self-Supervised Single-View 3D Style Transfer

In this paper, we propose a novel method for single-view 3D style transfer that generates a unique 3D object with both shape and texture transfer. Our focus lies primarily on birds, a popular subject in 3D reconstruction, for which no existing single-view 3D transfer methods have been developed. The method we propose seeks to generate a 3D mesh shape and texture of a bird from two single-view images. To achieve this, we introduce a novel shape transfer generator that comprises a dual residual gated network (DRGNet), and a multi-layer perceptron (MLP). DRGNet extracts the features of source and target images using a shared coordinate gate unit, while the MLP generates spatial coordinates for building a 3D mesh. We also introduce a semantic UV texture transfer module that implements textural style transfer using semantic UV segmentation, which ensures consistency in the semantic meaning of the transferred regions. This module can be widely adapted to many existing approaches. Finally, our method constructs a novel 3D bird using a differentiable renderer. Experimental results on the CUB dataset verify that our method achieves state-of-the-art performance on the single-view 3D style transfer task. Code is available at https://github.com/wrk226/creative_birds.
Sparse Sampling Transformer with Uncertainty-Driven Ranking for Unified Removal of Raindrops and Rain Streaks

In the real world, image degradations caused by rain often exhibit a combination of rain streaks and raindrops, thereby increasing the challenges of recovering the underlying clean image. Note that the rain streaks and raindrops have diverse shapes, sizes, and locations in the captured image, and thus modeling the correlation relationship between irregular degradations caused by rain artifacts is a necessary prerequisite for image deraining. This paper aims to present an efficient and flexible mechanism to learn and model degradation relationships in a global view, thereby achieving a unified removal of intricate rain scenes. To do so, we propose a Sparse Sampling Transformer based on Uncertainty-Driven Ranking, dubbed UDR-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Former. Compared to previous methods, our UDR-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Former has three merits. First, it can adaptively sample relevant image degradation information to model underlying degradation relationships. Second, explicit application of the uncertainty-driven ranking strategy can facilitate the network to attend to degradation features and understand the reconstruction process. Finally, experimental results show that our UDR-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Former clearly outperforms state-of-the-art methods for all benchmarks.
HDG-ODE: A Hierarchical Continuous-Time Model for Human Pose Forecasting

Recently, human pose estimation has attracted more and more attention due to its importance in many real applications. Although many efforts have been put on extracting 2D poses from static images, there are still some severe problems to be solved. A critical one is occlusion, which is more obvious in multi-person scenarios and makes it even more difficult to recover the corresponding 3D poses. When we consider a sequence of images, the temporal correlation among the contexts can be utilized to help us ease the problem, but most of the current works only rely on discrete-time models and estimate the joint locations of all people within a whole sparse graph. In this paper, we propose a new framework, Hierarchical Dynamic Graph Ordinary Differential Equation (HDG-ODE), to tackle the 3D pose forecasting task from 2D skeleton representations in videos. Our framework adopts ODE, a continuous-time model, as the base to predict the 3D joint positions at any time. Considering the structural-property of the skeleton data in representing human poses and the possible irregularity caused by occlusion, we propose the use of dynamic graph convolution as the basic operator. To reduce the computational complexity introduced by the sparsity of the pose graph, our model takes a hierarchical structure where the encoding process at the observation timestamp is done in a cascade manner while the propagation between observations is conducted in parallel. The performance studies on several datasets demonstrate that our model is effective and can out-perform other methods with fewer parameters.