Reviewerly/exHarmony huggingface dataset:
Quantum-resistant Cryptography in FPGA

The use of cryptography techniques to guarantee confidentiality, authenticity and integrity of sensitive data has become mandatory. Besides, advancements in quantum computers are gradually posing a threat to public key encryption technology. Without proper security measures, fraudsters may easily gain access to one's personal and sensitive data. The result of the third round of NIST's Post-Quantum Cryptography (PQC) process for standardization of public-key cryptography systems brought CRYSTALS-Kyber as the first mechanism selected for key encapsulation. The aim of this study is to provide the specification of a reconfigurable CRYSTAL-Kyber accelerator using High-Level Synthesis (HLS) technology. Our architecture requires about 2200 LUTs, 3001 FFs and 28 DSP on a low-cost Zynq FPGA (XC7Z020-1 CLG400C). The total time spent by the accelerator in a key exchange simulation is approximately 0.84 ms, operating at 100 MHz, and the estimated power consumption in this process is 1.695W.
Vehicle Detection and Tracking from UAV Imagery via Cascade Classifier

Traffic monitoring plays a vital role in the current world. Previously, stationary data collectors such as video cameras and induction loops were employed for this task. However, the availability of unmanned aerial vehicles (UAV) has opened up new horizons for this task and numerous research projects are being conducted in this field. But object detection and tracking become a challenging task in the case of aerial images due to the presence of high density of objects, challenging view angles, different illumination changes, and varying altitudes of the drone. In this paper, we propose a method for detecting vehicles and also tracking them through the use of cascade classifier and centroid tracking. We have also incorporated georeferencing and coregistration of acquired images and then proceeded on to extract lanes. After segmenting out the region of interest, we proceeded with the detection and tracking tasks.
Leveraging Structured Information from a Passage to Generate Questions

Question Generation (QG) is the task of utilizing Artificial Intelligence (Al) technology to generate questions that can be answered by a span of text within a given passage. Existing research on QG in the educational field struggles with two challenges: the mainstream QG models based on seq-to-seq fail to utilize the structured information from the passage; the other is the lack of specialized educational QG datasets. To address the challenges, a specialized QG dataset, reading comprehension dataset from examinations for QG (named RACE4QG), is reconstructed by applying a new answer tagging approach and a data-filtering strategy to the RACE dataset. Further, an end-to-end QG model, which can exploit the intra- and inter-sentence information to generate better questions, is proposed. In our model, the encoder utilizes a Gated Recurrent Units (GRU) network, which takes the concatenation of word embedding, answer tagging, and Graph Attention neTworks(GAT) embedding as input. The hidden states of the GRU are operated with a gated self-attention to obtain the final passage-answer representation, which will be fed to the decoder. Results show that our model outperforms baselines on automatic metrics and human evaluation. Consequently, the model improves the baseline by 0.44, 1.32, and 1.34 on BLEU-4, ROUGE-L, and METEOR metrics, respectively, indicating the effectivity and reliability of our model. Its gap with human expectations also reflects the research potential.
PENGEMBANGAN E-MODUL BERBASIS CASE METHOD DAN PROJECT BASE MATA KULIAH KETERAMPILAN BERBAHASA INDONESIA DAN APRESIASI SASTRA SEKOLAH DASAR

The purpose of this research is to create a substantial Case Strategy and Venture Base E-Module item for Indonesian Dialect Abilities and Writing Appreciation Courses for Rudimentary Schools. This investigate employments investigate and advancement strategies or frequently called Investigate and Improvement (R&amp;D). The area of the investigate was carried out at the Rudimentary School Educator Instruction Ponder Program, Workforce of Instruction, Medan State College. The population of this study were all students of the Stambuk 2021 Elementary School teacher program, a total of 225 students. The advancement of Case Method-Based E-Modules and Extend Base Dialect Abilities and Writing Appreciation Basic Schools has been approved by media specialists and gotten a possibility rate of 92%, whereas fabric specialists within the to begin with organize the feasibility percentage was 68.6% and after that expanded within the moment arrange, which was 85 %. The item was tried within the field on the normal lesson J 2021 understudies, totaling 22 individuals with a achievability rate rate of 96.2%. Based on the clarification over, it can be concluded that the Advancement of Case Method-Based E-Modules and Extend Base Dialect Abilities Addresses and Appreciation of Basic School Writing is suitable to be utilized to make strides learning results for Rudimentary School Instructor Instruction, Workforce of Instruction, State College of Medan.
Communication for Real-Time Music Systems: An Overview of O2

Abstract Message passing between processes and across networks offers a powerful method to integrate and coordinate various music programs, facilitating software reuse, modularity, and parallel processing. Networking can integrate components that use different languages and hardware. In this article we describe O2, a flexible protocol for communication ranging from the thread level up to the level of global networks. Messages in O2 are similar to those of Open Sound Control, but O2 offers many additional features, including discovery, clock synchronization, a reliable message delivery option, and routing based on services rather than specific network addresses. A bridge mechanism extends the reach of O2 to web browsers, shared memory threads, and small microcontrollers. The design, implementation, and applications of O2 are described.
An efficient breast cancer detection with secured cloud storage &amp; reliability analysis using FMEA

Breast cancer is considered as a most dangerous type of cancer found in women among all the cancers. Around 2.3 million women in the world are affected by this cancer and there is no cure if it is left untreated at an earlier stage. Therefore, early diagnosis of this disease is an important consideration to save the life of millions of women. Many machine learning models have been evolved in the recent years for breast cancer detection. However, all the currently available works focused only on improving the prediction accuracy, they need more attention on providing reliable services. This work presents an efficient breast cancer detection mechanism using deep learning strategies. The various assortments like breast image shapes, the intensity of images, regions of an image, illuminations, and contrast are the conceivable factors that define breast cancer identification. This study offers a strong image detection process for breast cancer mammography images by considering the whole slide image. Here, the input process for the preprocessing stage will remove the noise present in the image using Gaussian Filter (GF). The preprocessed image moves to the image segmentation and then forward to the feature extraction for extracting the features of the images using Cauchy distribution-based segmentation and Shearlet based feature extraction. Then the specialized features can be isolated using the Entropy PCA based feature selection. Finally, the breast cancer area is to be detected as benign or malignant accurately by using the Unified probability with LSTM neural network classification (UP-LSTM) for whole slide image (WSI). The attained outcomes and the detected outcomes were stored in cloud using a security mechanism for further monitoring purposes. To provide an efficient security, a Bio-inspired Iterative Honey Bee (BI-IHB) encryption is employed which is decrypted on user request. The reliability of the stored data is then found using FMEA (Failure mode and effective analysis) approach. From the experimental analysis, it is observed that UP-LSTM classifier model offers accuracy of 99.26% , sensitivity of 100% , and precision value of 98.59% which is better than the other state of the art techniques.
Human Pose Estimation: Multi-stage Network Based on HRNet

Abstract Multi-stage network uses stacked networks to enhance the feature extraction capability, and can gradually refine the keypoints with the information of previous stages’ output. Obviously, multi-stage networks are more suitable for human pose estimation. However, most current multi-stage networks use a codec structure as the backbone in which downsample will cause information loss. HRNet maintains high-resolution features to supply the information which is lost in down-sampling stage. In this regard, we propose a novel two-stage network with HRNet as the backbone and stacked codec structure. HRNet has more efficient feature extraction capability, and the stacked codec network can utilize the multi-scale features generated by HRNet more effectively. This method obtains a 1.2AP improvement compared to HRNet and a significant improvement compared to other two-stage networks.
Refinement Bilateral Segmentation Network for Semantic Segmentation in Traffic Scenes

Abstract Semantic segmentation technology in traffic scenes can help vehicles make accurate analyses and positioning of the road ahead. In traffic scenarios, the trade-off between real-time performance and the accuracy of semantic segmentation is particularly important. This paper proposes a lightweight deep convolutional network, which can be applied to traffic scenes to complete accurate semantic segmentation tasks, considering both real-time performance and accuracy. The Cross Channel Attention Fusion Mechanism proposed in this paper can better integrate the context information and improve accuracy. The Depth-wise Separable Pyramid Module proposed based on the feature pyramid idea can improve the segmentation accuracy and effectively trade off the real-time performance.
APIGAN: Learning to Recommend Java API in Real-Time Using Generative Adversarial Networks

Abstract In order to improve the accuracy of API recommendations, this paper proposes a novel API recommendation approach of APIGAN fully leveraging structural and textual information based on Generative Adversarial Networks, in which a Long Short-Term Memory (LSTM) is used as a generator while a Convolutional Neural Network (CNN) is used as a discriminator. The structural and semantic information of a source code extracted from an abstract syntax tree is used to construct a program dependence graph (PDG) which is also the input of the generator and the discriminator. By evaluating the difference in outputs of them, LSTM is evolved gradually till an optimized program dependence network is obtained to recommend top-k APIs. The results of the experiments show that APIGAN outperforms state-of-the-art research such as APIREC, GraLan, and n-gram in the aspect of top-k accuracy.
Land cover classification of remote sensing images based on improved DeeplabV3+ network

Abstract For land survey and land planning, the precise segmentation of land cover based on remote sensing images is extremely important. Based on the high-resolution satellite remote sensing images and the corresponding land cover GIS vector data in Hangzhou, for the categorization and change detection of urban land cover, a unique convolutional neural network is suggested. The data source is from high-resolution satellite remote sensing images, the semantic segmentation model DeeplabV3+ is improved. The original backbone network Xception is replaced by MobileNetV2 with smaller parameters, and add the channel attention module after the multi-scale module. In addition, to deal with the effect of category imbalance on model results, the focus loss function is introduced for network training. The pixel accuracy and average intersection ratio of 77.63% and 67.46% respectively, which are better than the commonly used semantic segmentation models DeeplabV3 +, UNet, and PSPNet models, and the model size is only 20.1MB. The experimental results show that the model is effective in redecing the number of model paramenters and improving the segmentation effect.
3D PathPlanning of RobotFishBased on ImprovedAntColonyAlgorithm

Abstract For the following problems:three-dimensional obstacle avoidance path selection when using robot fish to find targets in a complex coral reef geographical environment, an improved ant colony algorithm that can be applied in an environment with multiple complexities is proposed. Combined with the underwater terrain, several maps with different obstacle complexities are established with the three-dimensional environment modeling scheme. Aiming at easy stagnation, slow search speed, and easiness to produce local optimization in the conventional ant colony algorithm, this paper improved the function design and helped the robot fish carry out the early search based on three pieces of heuristic information (the global distance, traffic ability cost, and path consumption cost). Then the path is sorted according to the path length to adjust the pheromone update mode. At the same time, an elite ant system is constructed to adjust the pheromone value of elite ants separately. The simulation results show that convergence speed and searchabilityof the algorithm have been improved to some extent, which proves feasibility of the algorithm.
Human Ear Recognition Algorithm of YOLOV3 Based on Attention Mechanism

Abstract To address the problems of false detection and the lack of accurate target bounding boxes in the process of detecting human ear recognition, a SE-YOLOv3 target detection algorithm with improved YOLOv3 is proposed. Based on the YOLOv3 algorithm, the effects of embedding the SE attention module into different positions of the model on the detection performance of the algorithm are studied and analyzed separately. It is proved that embedding the SE attention module in the parallel ResNet network in the backbone of the YOLOv3 model can effectively improve the detection accuracy of the algorithm. And it is experimentally demonstrated in CCU-DE and USTB human ear datasets.
Collision Avoidance for Multi-robot Navigation

Abstract This paper introduces two representative distributed navigation methods with collision avoidance algorithms for multi-agent systems, the Reciprocal Velocity Obstacle method and the Buffered Voronoi Cell-based method. The definitions and principles of these two methods are introduced firstly by explaining the meaning of each function and making examples. Secondly, each method has a validation experiment to simulate them. By doing those simulations, the stability and performance are proven to fit the designers’ ideals. Finally, the result is shown as pictures to visualize the success of both methods. This paper may be seen as a reference to design a multi-robot system or develop a new approach to navigation algorithm.
Study on Servo Control Method of Eye Movement Intention Based on Reinforcement Learning

Abstract Eye movement servo control has the advantage of non-contact and the eye movement is not affected by other limbs. By applying eye-tracking technology to rehabilitation research of people with movement disorders, the interaction between eye-tracking technology and computers will be gradually put into practice, which is equivalent to giving patients a pair of “invisible hands” that can give instructions freely. In this paper, a servo control method based on eye-movement coding technology is proposed to solve the problems of low accuracy, poor reliability, and insufficient human intention initiative in the field of eye-movement servo control of human-computer visual interaction. The human eye movement instruction is processed by coding and integrated with human eye movement intention to realize intelligent servo control of human eye intention. In order to improve the accuracy of eye movement servo control, this paper proposes an eye movement instruction coding processing method, which uses the trained AdaBoost fusion strong classifier algorithm and the landmark detector to detect the real-time algorithm in the video sequence to complete the eye location and blink determination. In this paper, an improved pupil location method based on multi-angle joint detection is proposed. The pupil location is completed by using the information of eye image points, and the digital coding model of eye command action is constructed, which provides an instruction base for eye movement servo control. In order to improve the reliability and initiative of eye-movement servo control, a reinforcement learning method combining eye-movement coded instruction and intention was proposed. By integrating the improved DDPG continuous control strategy reinforcement learning algorithm with eye movement intention instruction, the environmental status can be sensed in the human-computer interaction process and the optimal strategy of eye movement instruction selection can be learned.
Research on FPGA-based Sine Wave Transmitter

Abstract This paper touches on a sine wave transmitting mode being designed applicable to multichannel high-power electromagnetic transmitters, well explaining the design scheme and working principle of each module. With FPGA as the core to build the hardware platform and DSP as a bridge to the cooperation between the upper computer and the FPGA lower computer, this very mode amplifies the MSK signal provided by the external interface with power and makes it featured high-power, high-energy efficiency as well as frequency accuracy, realizing the transmitting current as 0.01Hz~300Hz standard sine wave and MSK modulated wave output.
Research on Ensemble Convolutional Neural Networks in Digital Image Course experiments

Abstract In various digital image processing courses, the accuracy of traditional expression recognition algorithms cannot meet practical needs. This paper studies facial expression recognition using a convolutional neural network and ensemble learning. We develop three subnetwork models based on the classical convolutional neural network, using the BN layer to increase the network convergence rate and regularization is used to prevent subnetwork overfitting. The optimization idea used in this paper is applicable to most experimental improvements and raises the standard of the experiments.
An Improved Gesture Recognition Model Based on Mini-Xception

Abstract With the rapid development of artificial intelligence technology, gestures have become the mainstream in the field of human-computer interaction because of their simplicity, easy understanding and non-contact. Compared with the early data gloves, the vision-based non-contact gesture recognition interaction method has obvious advantages. However, the variability of the gesture itself, the complexity of the background and the influence of different lighting conditions have impacted the accuracy of gesture recognition. With the rapid development of deep learning technology, gesture recognition has achieved amazing results in accuracy. To improve gesture recognition accuracy in complex background, this paper optimizes the mini-xception network model of lightweight convolutional neural network algorithm, introduce the transfer learner and integrate YOLOV4-tiny into the mini-xception model, generate a new network model YT_ mini-Xception. After experimental verification, by YT_ Mini-xception network model, the average accuracy of 0-9 gesture recognition on complex background data set is 96.64%, the average recognition time was 39.8 milliseconds, and the expected goal was achieved.
Development of Digital Module ‘Phymology’ on Optical Instruments

Abstract One of the technological innovations in the 21st century that can be applied in the learning process is the use of digital-based instructional media. While, the learning media in the form of digital modules is still relatively little used in learning physics in schools. This study aims to produce learning media in the form of a digital module 'Phymology' on optical instrument material. This research uses Research and Development (R&amp;D) research methods and is based on the ADDIE development model. The ADDIE development model has five stages, namely the analysis stage, the design stage, the development stage, the implementation stage and the evaluation stage. The digital module in this optical instrument material was developed in the PBL (Problem Based Learning) stage and contains STEM aspects, namely science, technology, engineering and mathematics. The final result of the development of a digital module for this optical instrument material is an android application with an apk format that can be downloaded via the Google Play Store and then installed on the student's smartphone device.
Isometric Layering and Shape Optimization of Snow Sculpture Models

Abstract In this paper, the 3D model of the snow sculpture is discretized by Matlab to do isometric layering to obtain a series of height 2D model cuts on the model. The snow bricks are built into the snow sculpture based on this cut to make the snow sculpture closest to the original model to the greatest extent. According to the similarity of the snow sculpture (i.e. error area), the snow brick-filling optimization strategy is proposed. The robot model building test verifies that the contour layering and optimization strategy are feasible and effective.
Rapid Detection Method of Pipe Inner Diameter Based on Binocular Vision

Abstract With the rapid development of robotics, automatic pipeline detection has become a research hotspot in recent years. The existing pipeline diameter automatic detection method needs to calculate a large number of clouds, which is inefficient and difficult to play a role in real-time pipeline detection. Therefore, this paper proposes a fast detection method based on binocular vision to achieve real-time detection of the inner diameter of the pipeline. According to the gradient diagram of the color image after the down-sampling, adaptive double-threshold edge detection is proposed to calculate the edge points. Then an improved BM stereo matching algorithm is proposed to obtain the disparity map of edge points quickly. The least square method is finally used for fitting the edge point coordinates to calculate the inner diameter of the pipeline. The experimental results show that the system has strong accuracy and high efficiency, which can meet the needs of real-time detection of the inner diameter of the pipeline.
Research on Inverse Kinematics Solution of Manipulator Based on FGA Optimized BP Neural Network

Abstract In view of the complexity of the traditional inverse kinematics solution, it is solved by the mapping of the BP neural network, but the precision is low and the network performance is not good. By using FGA to optimize BP neural network, the shortcomings of the neural network itself can be overcome. Taking the pose matrix and joint angle of the manipulator as the input and output variables of the prediction network, the inverse solution prediction BP network of the manipulator is constructed, and then samples are selected for training. Finally, the network test and simulation results show that the optimized BP neural network has higher prediction precision and improved network performance.