Reviewerly/exHarmony huggingface dataset:
Exploring Memory-Oriented Design Optimization of Edge AI Hardware for Extended Reality Applications

Low-power edge AI capabilities are essential for on-device extended reality (XR) applications to support the vision of the metaverse. In this work, we investigate two representative XR workloads, 1) hand detection and 2) eye segmentation, for hardware design space exploration. For both applications, we train deep neural networks and analyze the impact of quantization and hardware-specific bottlenecks. Through simulations, we evaluate a CPU and two systolic inference accelerator implementations. Next, we compare these hardware solutions with advanced technology nodes. The impact of integrating state-of-the-art emerging nonvolatile memory (NVM) technology (STT-/SOT-/VGSOT-MRAM) into the XR–AI inference pipeline is evaluated. We found that significant energy benefits ($ \geq $≥24%) can be achieved for hand detection [inferences per second (IPS) = 10] and eye segmentation (IPS = 0.1) by introducing NVM into the memory hierarchy for designs at the 7-nm node while meeting minimum IPS values. Moreover, we can realize a substantial reduction in area ($ \geq $≥30%) owing to the small form factor of MRAM.
A Leader Election Algorithm Using Blockchain for IoT

Nowadays, the demand for Internet of Things (IoT) applications is continuously increasing. It is observed that the future scope of IoT is vivid. Therefore, the research industries have focused kind attention towards development of the innovative solutions. IoT is a group of heterogeneous devices and networks. More commonly, the IoT components work under the control of centralized access point or node. IoT uses access point for better coordination, data aggregation, data exchange, etc. Access point or node help to make better decisions and improve overall system efficiency. The access point may have an immense challenge to control diverse range of devices equipped with uncommon features. Also, security becomes a major concern in IoT. In this paper, we propose a blockchain based leader election algorithm for IoT. Here, access points act as full nodes in the blockchain. Full nodes participate in the election process. Under the control of an access point, one device will be selected which will be designated as a leader for access points. Our propose algorithm guarantee a leader, once election process terminates.
A Stable Deep Learning Model for Recognizing Human Actions in Noisy Videos

The widespread application and development of technology have significantly improved people's lives. In recent years, deep learning has emerged as a powerful branch of artificial intelligence, enabling the creation of intelligent software and robots that assist with various daily tasks. Deep Learning, a subtype of Complex Artificial Neural Networks (ANNs) based on machine learning, is commonly used for accurate human behavior detection from smartphone data. In this paper, we propose a model to identify human activity using ConvLSTM, DenseNet, and LRCN. The statistical model is built on a substantial dataset of videos sourced from publicly accessible datasets UCF50 and HMDB51, which cover a wide range of activity variables. Our results demonstrate that the DenseNet-based model outperforms other models in terms of accuracy.
Efficient 1D Grouped Convolution for PyTorch a Case Study: Fast On-Device Fine-Tuning for SqueezeBERT

Grouped convolution has been observed to be an effective approximation for convolution in many DNN applications. For example, SqueezeBERT, which is a light and fast BERT language processing model, utilizes 1D grouped convolutions. Though SqueezeBERT is well-optimized for inference on edge devices, it suffers from poor memory management during fine-tuning (training). This results in longer fine-tuning time on resource-limited GPUs compared to the original BERT model, BERT-base, despite being specifically designed for edge devices. We study this behavior and show that this poor memory management originates from the use of 1D grouped convolutions in SqueezeBERT. We re-implement 1D grouped convolutions using fully-connected layers, addressing the poor memory allocation and data locality of 1D grouped convolutions. We show that our method is well-suited for edge devices with limited memory; further, it has a negligible effect on inference speed. When utilizing our method, we observe a 42 % reduction in fine-tuning time for SqueezeBERT on edge devices.
An Extraction Aggregation Strategy Using a Hierarchical Representation Model Based on Longformers and Transform

The quantity of written content about medical information is staggering, and it keeps on increasing daily. Think about the internet, which is full of websites, news, constantly updated status, blogs, and a great deal more. Utilizing search to swiftly scroll through key findings is the most effective method for navigating material mainly because the results are not arranged in any specific fashion. A considerable percentage of this stuff needs to be summarized to emphasize the most significant information. Creating a custom summary with our own two hands of everything that can be effectively done by text summarization techniques. There is a critical need for this automated method. This project incorporates technologies that are capable of summarizing the text automatically. It implies paraphrasing phrases into a single thought. We propose a hierarchical document that represents an architecture for extraction aggregation that utilizes “Longformer” as a set encoder and “Transformer” as a text encoder to represent text documents properly. This model is referred to as the Long-Trans-Extr model. One of the benefits of using Longformer as a set encoder is that the model can take in lengthy documents with a maximum of 4096 tokens, which only adds a little amount of additional processing. This quick summarization tool can be used by doctors or medical practitioners for quickly analysing emergency or critical patients.
Unconstrained Face Recognition from Image Sequence

This paper presents the breakdown of the Unconfined Experience Detection Quality inside an unimpeded face recognition environment. It provides the output from matched image face with recognition from image sequence. It also gives the capabilities similar inequity, light, and expression modification approach. It might be useful for both proof and identity. At this point in time, it will find plenty of way of front view face-recognition. Beforehand few years, for computer-vision, numbers of face recognition methods have been organized. But, real-planet face-detection tranquil credits an interesting works. The fascination with unconstrained suitable face-recognition is increasing utilizing the explosion of online media for sample community schemes, and video investigation video somewhere come upon assessment is of great meaning. Inside this analysis, it appears to handle popularity inside the predicament of data supposition. It is able to discover a secret expertise by using a Diversified Technique. This work introduces alternatives suggested for unconstrained face recognition quality place and promoting the solution for being used by RFG based face-recognition. Goal of research is RFG concentrated unconstrained face recognition to enhance the exhibition quality. The Method and Simulation of proposed sensing techniques will probably be completed with the use of MATLAB and output with matched query image from save directory.
Adaptive Training System

In this paper, we propose an adaptive training algorithm that accelerates the training process based on a parametric model of trainees and training scenarios. The proposed approach makes trial-by-trial recommendations on optimal scenario difficulty selections to maximize improvements in the trainee's absolute skill level.
In-Memory Database Query Energy Estimation: Modeling &amp; Green Strategy Support

The miniaturization of electronic components, coupled with falling acquisition prices and increasing capacities, has led to the availability of many types of main memory data storage systems called In-Memory databases. Despite their inability to handle large volumes of data in memory, these systems offer considerable performance in query processing. This is due to the latency optimization of loading data from secondary memory. Nevertheless, their operation requires an execrable use of the main memory and also high energy consumption. With the torch of environmental sustainability being waved and the exorbitant energy cost, the development and application of energy reduction techniques within these systems is more urgent than ever. In this paper, we model the cost of energy consumption during query plan execution in an In-Memory database to develop energy-efficient approaches for query processing and benchmark tool designing.
COIN: Combinational Intelligent Networks

We introduce Combinational Intelligent Networks (COIN), a machine learning technique that targets edge inference using low-resourced FPGAs or ASICs. COIN is an improvement on LogicWiSARD, a recent weightless neural network that achieves low power, small area, and high throughput. We convert the LogicWiSARD model into a binary neural network, train it using backpropagation, and then convert it to a COIN model. As a result, COIN can achieve higher accuracy than LogicWiSARD or it can require significantly fewer hardware resources when comparing models with similar accuracies. In comparison to a BNN implementation, FINN, small and large COIN models are more energy efficient demonstrating up to 11.5x higher inferences/Joule at similar accuracy. Our tool executes the complete flow, from training to RTL. and is publicly available.
Bioinspired Sensory Gating in the Artificial Vision System

When viewed as an enterprise, the healthcare system has important security and privacy requirements, such as protecting patient medical records from unauthorized access, tracking protected drugs, connecting to transportation such as ambulances securely, and conducting secure and intelligent e-health surveillance. Block chain has introduced new ideas in medical data security and safety, and with the right security measures, it may eliminate the tension between sharing data and maintaining anonymity. In this study, we integrate the benefits of cloud computing with block chain to offer a secrecy technique for block chain and IoT. In addition to incorporating IoT and providing Iot solutions to block chain nodes, this method also collects, analyzes, uses, and maintains identity authentication for health information. Interface and overcomes the insufficient computer power of particular blockchainchain routers to verify the data's feasibility and authenticity order to verify the feasibility and authenticity of the data. The simulation experiment shows that the suggested technique is effective. It may handle problems like high computer intricacy, data interchange, and privacy protection while maintaining and confirming the quality of medical data.
Exploiting Subword Permutations to Maximize CNN Compute Performance and Efficiency

Neural networks (NNs) are quantized to decrease their computational demands and reduce their memory foot-print. However, specialized hardware is required that supports computations with low bit widths to take advantage of such optimizations. In this work, we propose permutations on subword level that build on top of multi-bit-width multiply-accumulate operations to effectively support low bit width computations of quantized NNs. By applying this technique, we extend the data reuse and further improve compute performance for convolution operations compared to simple vectorization using SIMD (single-instruction-multiple-data). We perform a design space exploration using a cycle accurate simulation with MobileNet and VGG16 on a vector-based processor. The results show a speedup of up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.7\times$</tex> and a reduction of up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.9\times$</tex> for required data transfers. Additionally, the control overhead for orchestrating the computation is decreased by up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.9\times$</tex> .
Effects of Avatar Transparency on Social Presence in Task-Centric Mixed Reality Remote Collaboration

Despite the importance of avatar representation on user experience for Mixed Reality (MR) remote collaboration involving various device environments and large amounts of task-related information, studies on how controlling visual parameters for avatars can benefit users in such situations have been scarce. Thus, we conducted a user study comparing the effects of three avatars with different transparency levels (Nontransparent, Semi-transparent, and Near-transparent) on social presence for users in Augmented Reality (AR) and Virtual Reality (VR) during task-centric MR remote collaboration. Results show that avatars with a strong visual presence are not required in situations where accomplishing the collaborative task is prioritized over social interaction. However, AR users preferred more vivid avatars than VR users. Based on our findings, we suggest guidelines on how different levels of avatar transparency should be applied based on the context of the task and device type for MR remote collaboration.
Gestures vs. Emojis: Comparing Non-Verbal Reaction Visualizations for Immersive Collaboration

Collaborative virtual environments afford new capabilities in telepresence applications, allowing participants to co-inhabit an environment to interact while being embodied via avatars. However, shared content within these environments often takes away the attention of collaborators from observing the non-verbal cues conveyed by their peers, resulting in less effective communication. Exaggerated gestures, abstract visuals, as well as a combination of the two, have the potential to improve the effectiveness of communication within these environments in comparison to familiar, natural non-verbal visualizations. We designed and conducted a user study where we evaluated the impact of these different non-verbal visualizations on users' identification time, understanding, and perception. We found that exaggerated gestures generally perform better than non-exaggerated gestures, abstract visuals are an effective means to convey intentional reactions, and the combination of gestures with abstract visuals provides some benefits compared to their standalone counterparts.
Comparing Gaze, Head and Controller Selection of Dynamically Revealed Targets in Head-Mounted Displays

This paper presents a head-mounted virtual reality study that compared gaze, head, and controller pointing for selection of dynamically revealed targets. Existing studies on head-mounted 3D interaction have focused on pointing and selection tasks where all targets are visible to the user. Our study compared the effects of screen width (field of view), target amplitude and width, and prior knowledge of target location on modality performance. Results show that gaze and controller pointing are significantly faster than head pointing and that increased screen width only positively impacts performance up to a certain point. We further investigated the applicability of existing pointing models. Our analysis confirmed the suitability of previously proposed two-component models for all modalities while uncovering differences for gaze at known and unknown target positions. Our findings provide new empirical evidence for understanding input with gaze, head, and controller and are significant for applications that extend around the user.
Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN.
Anomaly Detection and Time Series Analysis

Anomaly detection and time series analysis are essential techniques in data science, with numerous applications in various domains. Anomaly detection involves identifying patterns in data that deviate from the norm, while time series analysis involves analyzing data that changes over time. Combining these two techniques allows for detecting abnormal patterns in time-varying data, which can be used for various purposes, such as identifying equipment failures, detecting fraud, and predicting future trends. However,s several challenges are associated with anomaly detection and time series analysis, including the complexity of data, the need for accurate labelling, and the difficulty of detecting rare events. This paper reviews different types of anomalies, and the standarmethodsds used for anomaly detection and time series analysis, and the challenges and future directions for this field. We also propose potential solutions for improving the efficient anomaly detection and time series analysis efficiency and accaccuracyvanced algorithms and parallel processing. Ultimately, this paper provides an overview of the current state-of-the-art techniqanomaly detection and time series analysis techniques and highlights the potential for future research in this field.
Immune Neuro-Fuzzy Network Based System for Collision Free Motion Control of Unmanned Electrical Vehicles

This paper presents current results of the research, aimed at the developing of motion control algorithms and systems for autonomous electric vehicles , and discusses one of the goals of the research – collision probability minimization and traffic flow optimization. The task of the current research is to develop a computer model and a laboratory prototype, which would analyze the input data, calculate and minimize the collision probability by changing motion parameters and optimize traffic flow. Computer simulations prove the workability and advantages of the developed model.
Machine learning for mortality risk prediction with changing patient demographics

Over the last 25-30 years there has been significant work carried out in producing risk prediction models for patients admitted to intensive care units. The most recent of these models in widespread use is the Intensive Care National Audit and Research Centre (ICNARC) model developed in 2007 which uses data from more than 230,000 admissions to UK intensive care units to develop and validate a UK based model outperforming other approaches. However, as with the majority of risk prediction models, the ICNARC model struggles with changing patient cohort demographics (such as the aging populations seen currently in the western world) and requires periodic recalibration.This paper introduces a machine learning pipeline for developing mortality prediction models and uses it to train a variety of ML models. The top performing of these outperform current commonly used mortality risk prediction models such as APACHE-II, SAPS-II, and the ICNARC model. This machine learning pipeline is then extended to allow continuous retraining via online learning. The results show that it is possible to retrain our model at different intervals to deal with varying patient demographics - improving model performance across a range of different patient cohort scenarios.
Resource-Constrained Encryption: Extending Ibex with a QARMA Hardware Accelerator

The increasing prevalence of IoT devices calls for the need for strong, but efficient cryptography. In this paper we present two instruction set extensions for the lightweight encryption cipher QARMA-64 to the RISC-V instruction set, implemented for the Ibex core. The first extension performs the entire algorithm in hardware, divided over ten instructions. The second extension takes a more granular approach and instead implements the basic operations that the algorithm uses as custom instructions. The first extension achieves a speedup of ~600x over the software implementation and a binary size reduction of over 2x. It achieves these results at the cost of an added field-programmable gate array (FPGA) utilization over the base Ibex design of 43.9% and 18.7% for, respectively, the number of lookup tables (LUTs) and flip-flops (FFs). The application-specific integrated circuit (ASIC) area for synthesis is increased by 92.4% over the base design. The second extension achieves a speedup of ~19x over the software version while roughly maintaining the same binary size. This extension increases the number of utilized LUTs and FFs respectively by only 0.1% and 4.9%. The ASIC area for this design is increased by only 5.1%. The power consumption for the first extension is estimated at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$543\mu \mathrm{W}$</tex> and for the second extension at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$468\mu \mathrm{W}$</tex> .
Reconnoitering the Essentials of Image and Video Processing: A Comprehensive Overview

Recent years have shown immense potential for image and video processing. Several fields in health, engineering, and science have used them to enhance functioning. In image processing, a number of algorithms and techniques are used to receive inputs in the form of images, such as pictures or video frames, and then produce an output which can either be a photo or a parameter that can be adjusted to enhance the image. When a video is processed, each frame is submitted to operations individually. A frame is a snapshot of the video at a certain point in time. Multiple frames may be taken in one second. Similar to photos, frames may be compared and contrasted based on their similarities and differences. To identify asteroids that could pose a threat and events like supernovae that are temporary, we need to use image processing techniques that work in real-time. Digital images are often compressed using a widely used method called the Discrete Cosine Transform (DCT). By using this technique, you can considerably decrease the size of your file without compromising the quality of the image that is visible to the human eye. Throughout this paper, we have examined several aspects of image and video processing and how they are used in scientific and technical fields.
Comparative Performance Analysis of Genetic Algorithm Variants on Solving 0/1 Knapsack Problem

Practical optimization issues are primarily and commonly addressed by EAs(Evolutionary Algorithms). A wellliked subset of EA, the Genetic Algorithm (GA), is renowned for its capacity to solve various optimization problems. This research work examines the possibility of the Genetic Algorithm (GA) to resolve the 0/1 Knapsack problem. In this study, a number of mutation and crossover types are used to solve 0/1 Knapsack problem. The effectiveness of GA is compared by utilizing various crossover and mutation combinations. Positively, GA may solve the problems substantially faster and with a reasonable degree of accuracy as compared to traditional methods. In a careful assessment of the stated behavior of GA, combination on three mutations and three crossover methods are explored in this study.