Reviewerly/exHarmony huggingface dataset:
Design and implementation of traffic police hand gesture recognition system based on surface electromyographic signals

In the event of traffic congestion, unexpected traffic accidents, or severe weather, it is difficult to guarantee traffic safety and smoothness merely through traffic lights. Consequently, the traffic police are required to execute on-site command. However, it can be challenging for pedestrians to accurately notice and comprehend the hand gestures of traffic police in a complicated environment using only their eyes, which will result in incorrect judgments of the traffic situation. In order to minimize the influence of complex external environment on traffic police gesture recognition, we design and implement a traffic police hand gesture recognition system based on surface electromyography (sEMG) signals in this paper. In addition to establishing eight traffic police standard gesture datasets (TPSG) by the Arduino UNO development board and sEMG sensor, we also propose TSE-GRU, a novel neural network for accurate and robust traffic police gesture recognition. TSE-GRU incorporates the improved temporal convolutional network (TCN) and the gated recurrent unit (GRU). More specifically, the improved TCN employs the Squeeze-and-Excitation Networks (SE) that is modified to strengthen the representational power of temporal features from each TCN layers for extracting more advanced spatial features among multiple channel data, and the GRU captures long-term dependencies from time-series data. The experimental results show that TSE-GRU performs well and achieves 97.89% accuracy in the dataset TPSG under various experiment settings. The GUI interface of the recognition system can also show the current recognition results in real-time and timely provide feedback to the user on the traffic police gesture.
Prototyping Smart Eyewear

Eye is one of the most vital and significant organs in the human body. Among the different senses that a human uses, vision is the one which accounts for 80% of the perceived experience. In the recent years due to the effect of the pandemic multiple working professionals and students are forced to view computer screens for a longer duration which is leading to multiple eye health problems such as Dryness syndrome, Cataract and Blindness in long term effect. All these problems lead to a requirement of a system which is capable of detecting and keeping a count of the human blink and notifying the user when and if required. This would also help in instigating the habit of eye blink to the user which is mostly taken for granted as a part of human involuntary action. The objective is to develop a system which is capable of detecting the blinks of the human eye in real time. The number of blinks would be recorded and stored in the database. Further an app is being developed to monitor the health status of the eye on a regular basis which instigates users to develop the habit of blinking. The app alerts the user when the frequency of blinking has decreased. An added benefit from the system is achieved by a certain modification in the firmware which is now capable of detecting drowsiness.
Sinhala Part of Speech Tagger using Deep Learning Techniques

Natural Language Processing (NLP) is a sub-field of Artificial Intelligence (AI) that consists of a collection of computational methods motivated by theory for the automated classification and reflection of human languages. The foundation for many sophisticated applications of NLP, including named entity recognition, sentiment analysis, machine translation, in-formation retrieval, and information processing, is laid by Part of Speech (POS) tagging, which is part of the lexical layer of NLP systems. In contrast to English, French, German, and other languages from the same geographical region, the development of high-accuracy, stable POS taggers for the Sinhala language is still in its early stages. Hence, Sinhala is identified as a low-resource language. The main objective of this research is to create a POS tagger for the Sinhala language to solve this issue. An innovative and novel strategy that has never been used with the Sinhala language has been designed. This approach has been suggested specifically to evaluate the possibility of enhancing the accuracy compared to other methodologies. So, deep learning algorithms have been applied in this study, which has a significant impact on improving tagger performance. First, highly accurate individual classifiers for primary POS tags were implemented, and then they were combined into one composite model. As expected, all individual classifiers and the final composite model have achieved a higher accuracy level. Thus, it demonstrates that the proposed solution using deep learning algorithms outperformed other methods, such as rule-based and stochastic, in terms of accuracy.
An Autonomic Deep Learning Artificial Neural Network based Algorithm for Effective Re-generation

Corruption of data due to data transmission errors from sensors and malicious users, accidental data loss is a very critical issue while applying the data set for ML algorithms. Most of the work in this field is concerned with data pre-processing and related activities. Our present work lay emphasis on the completeness of the data by analyzing data corruption and recovery from the same dataset using Deep learning techniques. Deep learning algorithms are known to have higher degree of prediction accuracy as compared to traditional ML algorithms. Further deep learning algorithms capabilities are boosted when the amount of training proportionally increases. The present study extends the capabilities of deep learning algorithms to recover corrupted or compromised data with high degree of accuracy. Additionally it generalizes by testing the algorithm across range of data sets. The application of deep learning algorithm resulted in recovery of data with average accuracy of 2%. Also the replaced values had an error rate of 2–3% as compared to the original values.
Object Cluster Position Using Reinforcement Learning

Object Detection has become a key technology in many applications. However, we need to locate the object cluster region rather than an object itself in many scenarios, such as crowd flow estimation, road congestion estimation, site identification, etc. The object cluster region is always not clearly marked and the regional feature are not obvious, the existing object detection method cannot be extended to the cluster region detection. Different from the traditional object detection method, we innovatively propose a hierarchical background cutting method using deep reinforcement learning. The agent gradually narrows its vision to filter the background, intelligently learns the feature of the object cluster region, can effectively focus object cluster position. We conducted experiments on the VisDrone aerial dataset, the results show that our method can effectively identify the object cluster region, and the object hit rate is over 80%.
Hierarchical Weighting Network with Depth Cues for Real-Time Video Salient Object Detection

Video salient object detection (VSOD) is an essential component in computer vision tasks, and depth information can provide critical spatial contour information. However, there is a lack of methods to integrate depth information into VSOD tasks. In this paper, a real-time hierarchical weighting network with depth cues is proposed to extract the features and temporal information efficiently, and depth estimation model is used to generate depth maps. In order to fuse the features of different modalities, a cross-modal weighting (CMW) module based on three-dimensional convolution is proposed, which enables the powerful aggregation capability. Furthermore, an efficient temporal information query module (TQM) with Transformer attention mechanism is designed to extract crucial temporal information from video sequences. Experiments on public VSOD datasets show that our method is excellent in detection speed and the results are competitive in the state-of-the-art methods.
Modified POX Controller for Enhancing Quality of Experience of Multimedia Streaming in a Realistic Software Defined Vehicular Ad Hoc Networks

In today's digital era, multimedia streaming is one of the major sources of internet traffic and it is also expected to grow significantly in upcoming years. Even though traditional Vehicular Ad Hoc Networks (VANET) provide solutions for multimedia content delivery through best effort, the Quality of Experience (QoE) of these techniques is crucially damaged by dynamic network changes, fluctuations in bandwidth and congestion in the network due to lack of global view. However, all these limitations can be substantially reduced by the introduction of Software Defined Network (SDN) architecture. Hence this hybrid network called Software Defined Vehicular Network (SDVN) introduces a global view, flexibility, scalability and smartness in multimedia streaming. The proposed work uses a POX controller with an OpenFlow Spanning Tree topology discovery unit-based SDN framework for the real-world vehicular network. This modified POX was compared with the traditional vehicular network and a normal POX for QoE parameters like Multi Scale Structural Similarity, Three Component Structural Similarity, Blurring, Delta, Mean sum of absolute difference and Noise estimation. For testing, a Standard Definition (SD) video was streamed between vehicular nodes under dense and real-world traffic scenarios. The QoE Metrics of Modified POX improved between 1 % to 42%. Hence the proposed Modified POX presents an enhanced end-to-end QoE for multimedia streaming service.
Comparative Analysis of K-means and Hierarchical Clustering in Bigdata Environment

As data is increasing with every single day and traditional database systems such as DBMS and RDBMS are facing a hard time to manage terabytes to petabytes of data, Bigdata comes to our savior. With Bigdata techniques not only store huge amount of data but can also process real time data. Once such huge amount of data is available, one can use various data mining techniques to gain insights from data. Clustering techniques helps us to extract more information by dividing the main data set into small cluster or group of clusters, making it easy for the algorithm to understand the data and provide more relevant results. K-means and hierarchical clustering are popular clustering algorithms. Big Data provide number of frameworks to take benefit of data mining techniques such as Map Reduce and Spark. In this paper, focusing on analyzing the performance analysis of K-means and hierarchical clustering algorithms using Spark and concluding which clustering algorithms are performing much better. The findings of the experiment shows that when employing a large dataset framework, K-Means and Hierarchical clustering provide promising results and performance, demonstrating Spark's capacity to cooperate with machine learning algorithms.
Research on Image Classification Combining Wavelet Analysis and Spiking Neural Network

Wavelet analysis is a variant of Fourier analysis, which can be used for time-frequency analysis of signal processing, and has achieved remarkable results in the field of image processing. The spike neural network is called the third-generation neural network, which is different from the previous generation, the neural network of the spike neural network is more inspired by neuroscience, and this neural network is constructed in a way closer to the human brain mechanism, which can be applied to many machine learning tasks. Image classification is one of the basic tasks in the field of computer vision. We explore the application of wavelet analysis to the training process of the spiking neural network, before the original data is input into the neural network, we process it with wavelet transform, so that the characteristics of the input data are easier to be learned by the neural network.
Topic Modeling Based on Attributed Graph

News data analysis plays an important role in the development, early warning and management of events. People often use topic modeling for analysis, which helps the government managers to have a macro control of the development of events. However, the existing technologies often obtain topics through LDA-like models or document clustering, and neither of these two methods can study the higher-order semantic relationships among a group of topic-related keywords. Few studies start from the perspective of graph, but they are only based on the simple graph structure. In this paper, we propose to construct a keyword-keyword network based on the structure of attributed graph by using word embedding as attribute, in which the keywords with close edges are regarded as topics. Compared with existing methods, Attributed Graph Topic Model (AGTM) aims to capture deeper topic semantics by introducing word embedding as attributes, capturing topics better than simple graph structures. A large number of experiments show that we achieve good results compared with the baseline model.
The Design of a Soccer Robot Game Strategy Based on Fuzzy Decision Algorithms

Based on the robot's coordinates and angles, the decision-making system determines what the robot should do in response to the current field situation. The decision-making system of a soccer robot is its brain. Important duties of the football robot system's decision-making subsystem include deploying the players based on the current situation on the field, providing instructions to the players, and assuming the responsibilities of a coach. In this paper, we propose a strategy for decision-making systems competition based on fuzzy decision-making algorithms. A fuzzy comprehensive evaluation is utilized to determine the court's formation and role assignment. This method is capable of integrating a variety of court-related data, does not require the development of a precise mathematical model, and can make sound decisions in real time.
AW-PCNN: Adaptive Weighting Pyramidal Convolutional Neural Network for Fine-Grained Few-Shot Learning

Due to the unique challenges of high intra-class variation and low inter-class variation, Fine-Grained Visual Classification (FGVC) tasks extremely need to extract multilevel semantic features of fine-grained images for classification. Moreover, labor-intensive annotations and the existence of long-tailed distribution of fine-grained images in real life make fine-grained few-shot learning an urgent problem to be solved. In this paper, we propose an Adaptive Weighting Pyramidal Convolutional Neural Network (AW-PCNN) for fine-grained few-shot learning. Our AW-PCNN consists of a PCNN module and a AW module, which are improved in two aspects. First, our PCNN module extracts the features of each layer in CNN to obtain both high-level global and low-level local subtle features of images to overcome the challenge of FGVC tasks. Second, We employ the metric learning approach for few-shot learning, and our AW module improves it by selecting decisive pairs and adaptively weighting the pairs based on their similarity scores to mitigate the challenge of FGVC tasks and learn a better embedding space. Our AW-PCNN achieves state-of-the-art performance on three benchmark fine-grained datasets, which proves the effectiveness and superiority of our model.
Enhancing the Adversarial Transferability of Vision Transformers Through Perturbation Invariance

Vision Transformers (ViTs) have recently made great improvements in numerous vision tasks. To safely deploy in real life, it is crucial to investigate the vulnerability of ViTs. Model augmentation is an effective way to improve cross-model transferability. However, recent works mainly focus on deep neural networks (DNNs) but present a low transferability to ViTs due to the lack of usage of their properties. Inspired by the fact that ViTs are insensitive to spatial structure, we conduct experiments and demonstrated that ViTs have similar cross entropy for disrupted images, which we called perturbation invariance. Therefore, we propose our perturbation invariance method to improve transferability. Specifically, we craft transformed images by randomly shuffling the input image patches, and average the gradients of these transformed images each iteration. Besides, we also add Gaussian noise in the iterative process to further boost attack. Extensive experiments prove the effectiveness of our method. In particular, our method obtains a 79.5% attack success rate on average when against four types of ViTs, which outperforms other state-ofthe-art methods by 3.8%.
Deep Weighted Graph Embedding for Link Weight Prediction

Graph structure is a widely existing data structure in real life. It has good modeling ability. Social networks, physical particles, texts, and so on, all can be represented as graph structure. Link prediction is a classical task in graph mining. This task uses the information of the observed network to predict missing links or possible new links between node pairs. Most of the existing studies are based on unweighted graphs. But in fact, lots of scenarios should be abstracted as weighted graphs, and the weight of links can reflect the strength of the ties between nodes. In this paper, we propose a deep weighted graph embedding method based on autoencoder and contrastive learning for link weight prediction on weighted graphs. Experiments on five real-world graph datasets demonstrate the effectiveness of our method.
EVA: Evolving Giant Pretrained Model for Few-Shot Conditional Image Generation

Few-shot conditional image generation, which refers to synthesizing images from new conditions based on few training samples, is challenging. Previous works either stick to learning from scratch with enlarged training samples by data augmentation or transferring fixed knowledge grasped from source data to describe target data distribution. They often result in unsatisfied performance due to the overfitting or underfitting brought by rare samples. To address the above issue, we propose a simple yet effective approach, namely EVolving giAnt (EVA), to make the pretrained giant generative model evolve to properly fit target data distribution with few samples (e.g., 1-shot, 10-shot and 50-shot). Specifically, we maintain most prior knowledge stored in model parameters to prevent overfitting, add a new module to accept target conditions, and adapt few parameters for out-bounded features extracted from the target data to avoid underfitting. Extensive experiments confirm the high diversity and quality of our synthesized samples as well as the practicality of our approach in the extreme 1-shot generation.
Privacy Preservation for Distance Based Data Mining in Distributed Data

Data collected from vast, diverse devices for data mining purposes must be preserved from privacy and security issues. This research aims to improve the privacy preservation for distributed data in data mining without each participant knowing the other participant’s data and address the concern of information loss at every data processing stage. We designed an algorithm for preserving privacy for distributed data mining by combining existing privacy-preserving techniques with the orthogonal random transformation matrix. We introduced the homomorphic encryption process to avoid a third party’s involvement in the encryption computation process. We re-formulated the algorithm and recalculated the distance of the inner product for any two records; therefore, we could apply the privacy-preserving model to the traditional data mining processes. Our results achieved the same accuracy for both the traditional and privacy-preserving models, showing that our approach was practical and efficient. However, our results of the time of our privacy-preserving model exhibited a higher computational time but which was, however, a trade-off for a more secure system.
Self-Supervised Representation Learning for Motion Control of Autonomous Vehicles

To address the generalization problem of imitation learning-based end-to-end driving methods in unseen environments, we propose a self-supervised representation learning framework for motion control of autonomous vehicles (SRLMC). SRLMC first efficiently learns spatiotemporal representations invariant across different driving views using attentive Siamese 3D-CNNs and contrastive loss and thereby improving the generalization capability of multi-task leaning networks for vehicle motion control. SRLMC is trained on a large-scale driving dataset and evaluated with discrete and continuous control command prediction tasks. The experimental results and comparison to prior works confirm that the proposed SRLMC is effective and generalizable to unseen environments.
An Adaptive Hardware Accelerator For Convolution Layers With Diverse Sizes

Convolution is the most important operation in convolutional neural networks (CNN). FPGA-based CNN accelerators need to fully consider the optimization of convolution loops to get ideal performance. This work analyzes convolution loop optimization in detail, exploiting loop tiling, loop unrolling, and loop interchange to design the dataflow of accelerator. This work quantitatively evaluates strategies for data reuse and resource utilization, combining fixed and dynamic parallelism to design a high-performance adaptive accelerator. The proposed accelerator is evaluated on ZCU102 FPGA by implementing a five-layer CNN with large differences in convolution layer sizes. It achieves more than 1.14x improvement in throughput efficiency over prior accelerators. And the consumption of logic resources is less than half of prior accelerators while the computing resources are similar.
I<sup>2</sup>CU: A Dedicated Im2col Hardware Unit

For Convolution Neural Network (CNN), the convolution operation for feature map and weight map usually implemented by im2col + GEMM method. However, for conventional method need expand feature map to a large feature matrix during a single kernel function based on convolution parameters (i.e. filter size, padding, and stride), then multiplication for matrixes took place in another function. Thus the conventional method will generate tons data transfer and the large feature matrix requires enormous storage space, it is hardware unfriendly.We design a hardware unit, I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CU (Im2Col Unit), a dedicated hardware unit to implement im2col in hardware friendly way. I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CU dynamically expand loaded 4D-Block return from texture unit and write back destination matrix to shared memory. I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CU can decrease the feature matrix storage space and implement im2col + GEMM in one kernel function.
Lattice-Based Anonymous Signcryption For Smart Grid

Smart grid utilizes intelligent metering devices that transmit the energy usage data of an end user to a service provider. During the transmission’ an adversary could eavesdrop’ intercept’ modify and forge the data. Therefore’ it is critically important to make sure that such data is confidential and unforgeable. Signcryption is an effective technique to satisfy the requirements. However’ existing signcryption schemes are generally based on conventional hardness assumptions’ which are vulnerable to adversaries equipped with quantum computers in the near future. Additionally, they fail to consider identity leakage that allows an adversary to target a special end-user group of smart grid for attacks. To solve the problems’ an anonymous signcryption scheme for smart grid, dubbed AS4Smd, is proposed. AS4Smd is based on lattice cryptography’ which is post-quantum secure. In AS4Smd’ an end-user can sign and encrypt data in a logically single step to produce a signcryptext. Additionally’ the end-user’s identity is embedded in the signcryptext to accomplish end-user anonymity. The performance evaluation on AS4Smd shows that it is efficient in terms of computation and communication efficiency.