Reviewerly/exHarmony huggingface dataset:
FPGA -Based Implementation of Convolutional Layer Accelerator Part for CNN

Convolutional Neural Network (CNN) is considered one of the significant technological innovations in computer vision tasks. With the popularization of CNN, research on CNN has been aimed at increasing the accuracy performance of the network by expanding the number of layers. But on the other hand, as the number of layers increases, the time required to implement the network also increases. Furthermore, the computational load is also increased, and the biggest challenge is getting the network to work efficiently. In addition to success, speed and power consumption have become important parameters. Thereby, dedicated platforms are needed for the implementation of deep learning algorithms. Reconfigurable Field Programmable Gate Arrays (FPGAs) have been recently adopted to implement and accelerate CNN algorithms. This paper has been fully studying the convolutional layer, which contains nested loops, optimization techniques, and generated variations of the hardware implementation. The most important contribution of this study is to perform optimization on the convolution layer step by step and examine the effects on latency and hardware sources. The convolution layers of CNNs have computational density, and convolution operation relies on nested loops. Therefore, loops have the highest impact on performance/latency. CNN architecture has been developed through high-level language C. Accelerator designs have been built by HLS (High-Level Synthesis) and rely on 32-bit floating-point arithmetic. Performance is determined as regards latency and resource utilization. This study presents the CNN inference accelerator with various optimization to exploit FPGA parallelism for the inference phase.
Traffic Image Dehazing Based on HSV Color Space

Aiming at the problems of severe degradation, blurredness and low contrast in traffic images in severe weather scenes, this paper proposes a traffic image enhancement algorithm in color space, which converts images into HSV space and then performs S and V components. Constrained adaptive histogram operations and then convert the image to RGB space. Our experiments show that the proposed algorithm can better restore the fogging effect.
Few-Shot Batch Incremental Road Object Detection via Detector Fusion

Incremental few-shot learning has emerged as a new and challenging area in deep learning, whose objective is to train deep learning models using very few samples of new class data, and none of the old class data. In this work we tackle the problem of batch incremental few-shot road object detection using data from the India Driving Dataset (IDD). Our approach, DualFusion, combines object detectors in a manner that allows us to learn to detect rare objects with very limited data, all without severely degrading the performance of the de4tector on the abundant classes. In the IDD OpenSet incremental few-shot detection task, we achieve a mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> score of 40.0 on the base classes and an overall mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> score of 38.8, both of which are the highest to date. In the COCO batch incremental few-shot detection task, we achieve a novel AP score of 9.9, surpassing the state-of-the-art novel class performance on the same by over 6.6 times.
Assessment of Slime Mould Algorithm Based Real PID Plus Second-order Derivative Controller for Magnetic Levitation System

This paper aims to demonstrate the success of a recently developed metaheuristic algorithm, named slime mould algorithm (SMA), for efficient operation of a magnetic levitation system. A real proportional-integral-derivative (PID) plus second-order derivative (PIDD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) controller was utilized to demonstrate the implementation. This paper is also the first report on SMA based real PIDD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> controller tuning for control of a magnetic levitation system. The transient response analysis was performed to demonstrate the promise of the proposed approach. Besides, a comparative performance analysis in terms of transient stability was also performed by using other available and good performing approaches. The results have shown the SMA based real PIDD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> controller to be a better choice for efficient operation of a magnetic levitation system.
Dialogue Management for Interactive API Search

API search involves finding components in an API that are relevant to a programming task. For example, a programmer may need a function in a C library that opens a new network connection, then another function that sends data across that connection. Unfortunately, programmers often have trouble finding the API components that they need. A strong scientific consensus is emerging towards developing interactive tool support that responds to conversational feedback, emulating the experience of asking a fellow human programmer for help. A major barrier to creating these interactive tools is implementing dialogue management for API search. Dialogue management involves determining how a system should respond to user input, such as whether to ask a clarification question or to display potential results. In this paper, we present a dialogue manager for interactive API search that considers search results and dialogue history to select efficient actions. We implement two dialogue policies: a hand-crafted policy and a policy optimized via reinforcement learning. We perform a synthetics evaluation and a human evaluation comparing the policies to a generic single-turn, top-N policy used by source code search engines.
Medical Image Enhancement using Guided Filtering and Chaotic Inertia Weight Black Hole Algorithm

In this study, a new hybrid approach is suggested for medical image enhancement. The main idea is based on the hybrid of the guided filter and chaotic inertia weight black hole algorithm (GFCBH) to enhance and highlight the image information using a new objective function. GFCBH is a two-stage approach that, first, applies the guided filter to the input image which performs as an edge-preserving smoothing operator, and then, uses the CBH algorithm to automatically find optimal parameters for transformation function based on the objective function. In the proposed objective function, universal image quality index (Q), entropy, edge pixels, and gray level cooccurrence matrix (GLCM) based contrast and energy are considered to achieve the best-enhanced image. The experimental results are verified by comparison with ten well-known image enhancement techniques using entropy and peak signal-to-noise-ratio (PSNR) measurement criteria. The extensive experiments along with qualitative and quantitative evaluations show that the suggested method can successfully enhance images and performs better than most state-of-art techniques.
An Ensemble Technique to Classify Multi-Class Textual Emotion

Classifying textual emotion plays a critical role in several HCI applications where the text is utilized as a central means of communication such as messages, reviews, blogs and other Web 2.0 platforms. The extensive usage of the Internet has emerged as an unprecedented means for people to express their feelings or emotion on blogs, social media, and e-commerce sites in recent years. Most of the emotions displayed on the online platforms are in textual forms (such as posts, tweets, comments and reviews), which are unorganized and time-consuming to structured due to their disordered forms. Although several emotion analysis tools are available in high-resource languages, it is critical to developing an automatic emotion classification system for low-resource languages, including Bengali, due to its constrained resources. This paper presents an ML-based ensemble method to classify six primary textual emotions (anger, fear, disgust, sadness, surprise and joy) from Bengali texts. An emotion corpus containing 8047 Bengali texts is developed to perform the textual emotion classification task. This work investigates eight standard ML-based techniques such as logistic regression (LR), multinomial naive Bayes (MNB), support vector machine (SVM), random forest (RF), decision tree (DT), K-nearest neighbour (KNN) and adaptive boosting (AdaBoost) and an ensemble method (a combination of LR, RF, SVM) with Bag of words (BoW) and tf-idf feature extraction techniques. The experimental result demonstrates that the ensemble with tf-idf achieved the highest weighted f1-score of 62.39% compared to other methods.
Designing Meaningful, Beneficial and Positive Human Robot Interactions with Older Adults for Increased Wellbeing During Care Activities

This research explores the benefits of introducing humanoid robots into different active ageing and aged care settings. We visited active ageing groups with a focus on dementia, knitting and a men’s shed. We also took the robot to a residential care home to set up engaging activities. Exploring assumptions of older adults and staff about the capabilities, purpose and intelligence of the robot played a large role in understanding how robots should be introduced. We found that implementation and interactions need to be carefully crafted in advance for developing trust and interest, and for creating a shift in feelings of control in older adults as well as staff. Benefits, meaning, and comfortable interactions are created through building on existing skills, familiarity, and past experiences. When done successfully, older adults were seen to engage in playful and empowering ways, enjoying the interactions with both the robot and the wider group with positive effects beyond the time the actual interactions took place. The article summarizes the findings across the different settings. It presents recommendations for introducing older adults to interact with humanoid robots, supported by motivational goal modelling and technology probe techniques. We consider our research in group settings to be relevant for the wider acceptance of the use of robots. We discuss that researchers should set clear goals for the interactions between the robot and older adults and gradually introduce the technology to older adults in a participatory way in group settings before attempting one-on-one scenarios with them.
Image Data Encryption Using DES Method

Data encryption has been considered as a way to secure data that is stored either on personal computers or on the internet such as cloud storage and cloud computing. Because advances in data storage technology, the main problem that is often faced is data security, so we apply the concept of data encryption using the DES method. by analyzing using the DES method is the safest data encryption process for safeguarding data stored on personal computers and on the internet. Based on the application that has been designed using the DES Algorithm with input in the form of an image, it can successfully encrypt the image.
A machine learning-based image processing approach for robotic assembly system

Due to the boost of machine learning research in recent years, advanced technologies bring new possibilities to robotic assembly systems. The machine learning-based image processing methods show promising potential to tackle the challenges in the assembly process, e.g. object recognition, locating and trajectory planning. Accurate and robust methodologies are needed to guarantee the performance of the assembly tasks. In this research, a machine learning-based image processing method is proposed for the robotic assembly system. It is capable of detecting and locating assembly components based on low-cost image inputs, and manipulate the industrial robot automatically. A geometry library is also developed, which is an optional hybrid method towards accurate recognition results when needed. The proposed approach is validated and evaluated via case studies.
Detection and Recognition of Hand Gestures for Indian Sign Language Recognition System

People with disabilities (hearing and listening impaired) have a vast social circle all over the world. For deaf and mute people, the sign language paves a way for the better communication which is the method that everyone understands and leads to effective communication among our diverse population. This study provides a comparative analysis for this complex task problems which are out of reach for a simple machine. As a result, there is a urgent need for the solution for such a problem, so the solution is integrating this problem with Machine Learning algorithms like Support Vector Machine, Convolutional Neural Network, K- Nearest neighbors. The results produced from Convolutional Neural Network are more accurate than those which is achieved by other several classifiers.
Application Value of Artificial Intelligence System in Music Education

As a computer science industry, artificial intelligence is a comprehensive technical problem involving many disciplines such as thought science, information technology, science and technology, biological science, psychology, and brain science. This article aims to study the application value of artificial intelligence system in music education. This article introduces the application of artificial intelligence technology and artificial intelligence system in music education. Based on Internet technology, the application of artificial intelligence in music teaching makes traditional concert halls continue to tend to their online teaching methods. In addition, the use of clustering algorithm to analyze the intelligent teaching system, to provide students with targeted and personalized teaching strategy learning and analysis based on learning results. The experimental results in this paper show that the artificial intelligence system has a positive effect on music education. The performance of students in traditional music education is compared with the performance of students in artificial intelligence systems. The results show that the music performance of students under the artificial intelligence system is better than that under the traditional artificial intelligence system. An increase of 17.5% reflects the practicality of artificial intelligence systems.
Next Generation Lab puts Denmark's past in the hands of its future

Next Generation Lab turns large and hitherto unstudied urban assemblages of archaeological leather and bone into a laboratory learning experience for high school students. The students, in turn, provide species identifications and thus increase knowledge on medieval and Renaissance livestock exploitation and material selection by craftsmen.
Path planning model for UAV collaborative search task Based on NGA

To solve the problem of large search demand and insufficient search ability of UAVs, this article has proposed a task planning method of UAV group based on nested learning algorithm. According to the characteristics of the target path, the method combines task allocation strategy and path planning algorithm to make decision. This method can give reasonable suggestions on the number of UAVs, and effectively improve the efficiency of UAV group search task. The algorithm uses the elite individual selection strategy based on tournament selection method to improve the optimization efficiency. And the algorithm uses neighborhood method to avoid local optimum. Finally, the algorithm is verified by the data of a search task. The experimental results show that the planning method used in this paper is suitable for the UAVs' cooperative search problem. Compared with other planning methods, it has faster solution speed and is conducive to emergency decision-making. This method also has reference value for the cooperative planning of search tasks.
Explainable Face Recognition based on Accurate Facial Compositions

With impressive advances made in face recognition, the explainability has attracted more and more attentions in the community, which delves into traceable and well-founded clues behind the identifications in addition to the confidence scores. However, the current Explainable Face Recognition (XFR) methods are difficult to balance the explain-ability and the recognition performance. In this paper, we propose a framework based on Accurate Facial Compositions, namely AFC-XFR. The framework consists of three modules: the Backbone for feature extraction, the Local Feature Refine Module (LFRM) for semantic feature refining, and the Self-Attention based Reconstruction Module (SARM) for serialized feature interaction. Fifteen semantic features, which are accurately captured from local facial components via the proposed acquisition scheme, are conveyed in the latter two modules. Moreover, the LFRM allows us to verify three significant insights experimentally, obtaining the explainability from the perspective of model decisions. Inspired by the insight "Facial features are processed holistically", the SARM’s internal feature interaction mechanism facilitates performance increase. Extensive experiments on varying loss functions and network architectures accomplish consistent advances on evaluation benchmarks.
The geology of the Capiru Group, Curitiba Terrane, Southern Ribeira Belt (Brazil)

The Capiru Group is a Neoproterozoic metasedimentary unit that crops out in the Curitiba terrane, Southern Ribeira Belt, Brazil. A detailed geological mapping of this unit was performed during 2016 to 2020. Original interpretations and the new geological map were compiled and integrated with the past field data of the state (MINEROPAR) and national (CPRM) geological surveys, and to unpublished and recent published studies. This compilation represents the most updated synthesis of the Capiru Group, and provides the basis for recognition and individualization of lithostratigraphic units. The lithostratigraphic units reflect depositional/tectonic events, which involved: a) passive continental margin deposition on the Curitiba microplate, b) compressional episodes during the Brasiliano - Pan African Orogeny (Late Neoproterozoic), c) extensional processes that followed the collision stage (Cambrian), and finally, the d) extensional tectonics related to the Gondwana break-up during the Lower Cretaceous, that culminated to the Proto-Atlantic Ocean opening.
Airborne quantum key distribution with boundary layer effects

Abstract With the substantial progress of terrestrial fiber-based quantum networks and satellite-based quantum nodes, airborne quantum key distribution (QKD) is now becoming a flexible bond between terrestrial fiber and satellite, which is an efficient solution to establish a mobile, on-demand, and real-time coverage quantum network. However, the random distributed boundary layer is always surrounded to the surface of the aircraft when the flight speed larger than 0.3 Ma, which would introduce random wavefront aberration, jitter and extra intensity attenuation to the transmitted photons. In this article, we propose a performance evaluation scheme of airborne QKD with boundary layer effects. The analyzed results about the photon deflection angle and wavefront aberration effects, show that the aero-optical effects caused by the boundary layer can not be ignored, which would heavily decrease the final secure key rate. In our proposed airborne QKD scenario, the boundary layer would introduce ∼3.5 dB loss to the transmitted photons and decrease ∼70.9% of the secure key rate. With tolerated quantum bit error rate set to 8%, the suggested quantum communication azimuth angle between the aircraft and the ground station is within 55 ∘ . Furthermore, the optimal beacon laser module and adaptive optics module are suggested to be employed, to improve the performance of airborne QKD system. Our detailed airborne QKD performance evaluation study can be performed to the future airborne quantum communication designs.
Categorization of Faulty Nonce Misuse Resistant Message Authentication

A growing number of lightweight block ciphers are proposed for environments such as the Internet of Things. An important contribution to the reduced implementation cost is a block length n of 64 or 96 bits rather than 128 bits. As a consequence, encryption modes and message authentication code (MAC) algorithms require security beyond the \(2^{n/2}\) birthday bound. This paper provides an extensive treatment of MAC algorithms that offer beyond birthday bound PRF security for both nonce-respecting and nonce-misusing adversaries. We study constructions that use two block cipher calls, one universal hash function call and an arbitrary number of XOR operations. We start with the separate problem of generically identifying all possible secure \(n\)-to-\(n\)-bit pseudorandom functions (PRFs) based on two block cipher calls. The analysis shows that the existing constructions EDM, SoP, and EDMD are the only constructions of this kind that achieve beyond birthday bound security. Subsequently we deliver an exhaustive treatment of MAC algorithms, where the outcome of a universal hash function evaluation on the message may be entered at any point in the computation of the PRF. We conclude that there are a total amount of nine schemes that achieve beyond birthday bound security, and a tenth construction that cannot be proven using currently known proof techniques. For these former nine MAC algorithms, three constructions achieve optimal n-bit security in the nonce-respecting setting, but are completely insecure if the nonce is reused. The remaining six constructions have 3n/4-bit security in the nonce-respecting setting, and only four out of these six constructions still achieve beyond the birthday bound security in the case of nonce misuse.
Segmental compensation of FOG temperature error based on ELM prediction model

Aiming at the complex nonlinear relationship between temperature and the zero bias of fiber optic gyro (FOG), combining the prediction model of extreme learning machine (ELM) with the idea of piecewise modeling. A segmented compensation method based on ELM prediction model is proposed, improving the temperature performance of FOG. Analyzing the influence of temperature on the optical fiber gyroscope zero bias. Studying the effect of the ELM model's parameters on the prediction precision, Giving the ELM neural network method for determining the number of hidden layer neurons. The simulation analysis results to the collected measured data of FOG show that compared with linear regression model and single ELM neural network model, the segmented compensation method based on ELM prediction model has more significant effects. And has good temperature applicability. After compensation, the RMSE of gyro offset data is reduced by more than 90%.
A Unified Treatment of Partial Stragglers and Sparse Matrices in Coded Matrix Computation

The overall execution time of distributed matrix computations is often dominated by slow worker nodes (stragglers) over the clusters. Recently, different coding techniques have been utilized to mitigate the effect of stragglers where worker nodes are assigned the task of processing encoded submatrices of the original matrices. In many machine learning or optimization problems the relevant matrices are often sparse. Several coded computation methods operate with dense linear combinations of the original submatrices; this can significantly increase the worker node computation times and consequently the overall job execution time. Moreover, several existing techniques treat the stragglers as failures (erasures) and discard their computations. In this work, we present a coding approach which operates with limited encoding of the original submatrices and utilizes the partial computations done by the slower workers. Our scheme continues to have the optimal threshold of prior work. Extensive numerical experiments done in AWS (Amazon Web Services) cluster confirm that the proposed approach enhances the speed of the worker computations (and thus the whole process) significantly.