Reviewerly/exHarmony huggingface dataset:
GPU-based SAR Image Lee Filtering

Aiming at the problem of suppression and removal of speckle noise in SAR images, Lee filter is considered to be a relatively good algorithm by comprehensive considerations. It has good performance in many scenes. The main disadvantage is that its calculation takes a long time and the real-time performance is too poor for large-format high-resolution SAR images. Therefore, the GPU devices can be used for acceleration in algorithm optimization. This paper first implements the classic Lee filtering algorithm in Python environment, and then combines Numba to realize the CPU acceleration of the algorithm. Then, the classical Lee filtering algorithm is repeated by C/C++. Finally, the GPU acceleration of the algorithm is implemented by CUDA. By comparison, the results show that GPU acceleration can increase the speed by nearly two orders of magnitude.
MuFASA: A Tool for High-level Specification and Analysis of Multi-factor Authentication Protocols

In recent years, the usage of online services (e.g., banking) has considerably increased. To protect the sensitive resources managed by these services against attackers, Multi-Factor Authentication (MFA) has been widely adopted. To date, a variety of MFA protocols have been implemented, leveraging different designs and features and providing a non-homogeneous level of security and user experience. Public and private authorities have defined laws and guidelines to guide the design of more secure and usable MFA protocols, but their influence on existing MFA implementations remains unclear. We present MuFASA, a tool for high-level specification and analysis of MFA protocols, which aims at supporting normal users and security experts (in the design phase of an MFA protocol), providing a high level report regarding possible risks associated to the specified MFA protocol, its resistance to a set of attacker models (defined by NIST), its ease-of-use and its compliance with a set of security requirements derived from European laws.
GAN Based Method for Labeled Image Augmentation in Autonomous Driving

Deep learning models in Autonomous Driving perception tasks commonly use supervised learning methods and thus highly depend on labeled data. Training with more labeled data tends to bring better results, which highlights the meaning of data augmentation. Currently there are two difficulties when doing data augmentation. Firstly, it is time consuming to manually label the collected raw data. The second issue is that the diversity of a dataset is limited by the collection environment and time. In this paper, we proposed to use the current state of the art Multimodal Unsupervised Image-to-Image Translation (MUNIT) to generate synthesized images from labeled data. One of the benefits is that the generated data are automated labeled since they share the same ground truth with the raw data. Then we used the augmentation dataset to do different tasks including drivable area detection and object detection to prove that the data could be used to improve the performance of convolution neural networks (CNNs). We also designed an auto labelling tool that people could do labelling with the help of the improved CNN. The whole process is like a close loop that finishes labelling tasks while making progresses by itself. Generally speaking, our approach introduces an auto labelling pipeline based on unsupervised image-to-image translation to increase the amount and diversity of labeled data.
Learning-based Adaptive Estimation for AOA Target Tracking with Non-Gaussian White Noise

In angle-of-arrival (AOA) target tracking, the target states are estimated using the noisy angular measurements. The non-Gaussian measurement noise is common in practical applications and will decrease the estimation accuracy significantly. This paper is to reduce the negative impacts of the non-Gaussian white noise (NGWN), which is modeled by multiple mixture Gaussian white noises, and guarantee the estimation accuracy. A learning-based adaptive extended Kalman filter (EKF) for AOA target tracking is developed in the NGWN environment. In the proposed method, the extreme learning machine (ELM) using a three-layer neural network is applied to identify the characters of measurement noise at each sampling time. Consequence, the EKF will consider the noise characters by the trained ELM, and make the corresponding real-time adjustments to improve the tracking performance. The effectiveness of the proposed method is verified by simulation examples.
In-Place Suffix Sorting on a Multicore Computer with Better Design

A suffix index built on the suffix array of data is fundamental for true full-text searches over data. The key problem for building the suffix index is how to compute the suffix array by sorting the suffixes of data time and space efficiently. According to the reported results up to date, pSACAK achieves the best suffix sorting performance on the parallel computing model of a multicore machine of internal memory. However, its naming procedure is time-consuming and constitutes a performance bottleneck. This article presents an optimized algorithm called pSACAK+ with a new naming procedure to attack this problem. Our experiments show that, pSACAK+ runs nearly 20% faster than pSACAK with a similar space consumption, which sets a new performance benchmark for suffix sorting on a multicore machine of internal memory.
Utilization of peltier cooling systems driven by solar power for storing vegetables and fruits

Abstract This article aimed to determine the performance of the Peltier cooler box system that utilizes solar power for storing vegetables and fruit. The advantage of the Peltier cooling system is that it does not consume electricity and environmentally friendly. The thermoelectric component is coupled with a heat sink where a small fan is placed on the outside, and another fan is set inside the cooler. Data is obtained from the measurement system and then analyzed so that the performance of the cooling system can be determined. The experiment is carried out in a location that is exposed to solar radiation. The process of measuring weather conditions is carried out by using a weather instrument. To record the temperature distribution on each component of the Peltier cooling system are used thermocouples which are connected to the data acquisition. Experimental results show that the minimum temperature that can be achieved on vegetables and fruits ranging from 17.324°C. The average temperature of vegetables and fruits during the experiments varies from 22°C. The results of this experiment indicate that the Peltier cooler box tested can be used to maintain the freshness of vegetables and fruits. The maximum COP value of the Peltier cooling system tested is 0.670, and the minimum COP value is 0.428.
Teaching Software Engineering Tools to Undergraduate Students

Today, software development is characterized by keywords such as collaborative, teamwork, distributed, agile, dynamic, qualitative and tool-supported among many others. In this paper, we present our experience in teaching three software development tools often used in industry in a software engineering course for undergraduate students: GitHub, SonarQube, and Microsoft Project. The main reasons behind the use of these tools during the development of a software project were: (1) students become familiar with examples of tools adopted in industry and academia, (2) students are enabled to collaborate in teams for the achievement of a common goal, and (3) students become aware of the management tasks needed by a project developed in teams. We exploited these tools in the software engineering course in the last three academic years. The students feedback on using these tools gathered through a questionnaire was positive. Students were enthusiastic in learning about new tools and their support for software development and management. In this paper we summarize the students feedback during three academic years and the lessons we have learned from their feedback.
Study of the influence of lexicon and language restrictions on computer assisted transcription of historical manuscripts

State-of-the-art Handwritten Text Recognition (HTR) systems allow transcribers to speed-up the transcription of handwritten text images. These systems provide transcribers an initial draft transcription that can be corrected with less effort than transcribing the handwritten text images from scratch. Currently, even the draft transcriptions offered by the most advanced HTR systems contain errors. Therefore, the supervision of this draft by a human transcriber is still necessary to obtain the correct transcription of the handwritten text images. This supervision can be eased by using interactive and assistive transcription systems, where the transcriber and the automatic system cooperate in the amending process. In this paper, the draft transcription is provided by an HTR system based on Convolutional and Recurrent Neural Networks with Bidirectional Long-Short Term Memory units, and the assistive system is fed by lattices generated by using Weighted Finite State Transducers. The influence of the lexicon and language restrictions on the performance of our computer assisted transcription system is evaluated on three historical manuscripts. The transcriptions offered by the proposed HTR system present very low error rates for the studied historical manuscripts. However, our assistive transcription system without lexicon or language restrictions is able to provide an additional reduction on the human effort required to correct the transcriptions in more than 50% over the transcriptions offered by the HTR system.
Generating Kranok patterns with an interactive evolutionary algorithm

The kranok pattern is a classical Thai pattern, often seen in the ornamentation of Thai religious artifacts such as Tripiṭaka cabinets, temple doors and coffins. All Thai trainee artists must practice drawing the kranok pattern because it is a fundamental motif in Thai traditional decorative art. Individual skilled artists and instructors have their own preferred ways of drawing the pattern, and their styles differ in ornamental details. Nevertheless, all make use of similar basic structures. Most trainees learn a range of styles by observing experienced artists. After exploring the methods of drawing used by experts and published in textbooks, we designed an algorithm for automatically generating kranok patterns. We used an interactive evolutionary algorithm (IEA) to improve the aesthetic appeal of the generated patterns in response to users’ feedback. The aim of our work was not to replace artists with machines, but to enhance human artistic expression. Specifically, the work aimed to help users without artistic skills to create kranok patterns in their own style. The algorithm facilitated the creation of a variety of personalized kranok patterns – diverse in their expressive curvature, refinement and proportions – that were satisfying to the varying preferences of a range of users. We also analyzed the proposed algorithm’s behavior in terms of its convergence to generate specific shapes. The proposed method was examined by 28 respondents (27 Thai and 1 foreign) who were selected to include representatives of both sexes as well as experts in both Thai drawing and evolutionary algorithms. The results from our questionnaires showed that all respondents were satisfied with the generated kranok patterns: one respondent was ‘completely satisfied’, seventeen ‘very satisfied’, seven ‘moderately satisfied’, and three ‘slightly satisfied’.
Learning Manifolds from Dynamic Process Data

Scientific data, generated by computational models or from experiments, are typically results of nonlinear interactions among several latent processes. Such datasets are typically high-dimensional and exhibit strong temporal correlations. Better understanding of the underlying processes requires mapping such data to a low-dimensional manifold where the dynamics of the latent processes are evident. While nonlinear spectral dimensionality reduction methods, e.g., Isomap, and their scalable variants, are conceptually fit candidates for obtaining such a mapping, the presence of the strong temporal correlation in the data can significantly impact these methods. In this paper, we first show why such methods fail when dealing with dynamic process data. A novel method, Entropy-Isomap, is proposed to handle this shortcoming. We demonstrate the effectiveness of the proposed method in the context of understanding the fabrication process of organic materials. The resulting low-dimensional representation correctly characterizes the process control variables and allows for informative visualization of the material morphology evolution.
Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy

The task of person re-identification (re-id) is to find the same pedestrian across non-overlapping cameras. Normally, the performance of person re-id can be affected by background clutters. However, existing segmentation algorithms are hard to obtain perfect foreground person images. To effectively leverage the body (foreground) cue, and in the meantime pay attention to discriminative information in the background (e.g., companion or vehicle), we propose to use a cross-learning strategy to take both foreground and other discriminative information into account. In addition, since currently existing foreground segmentation result always involves noise, we use Label Smoothing Regularization (LSR) to strengthen the generalization capability during our learning process. In experiments, we pick up two state-of-the-art person re-id methods to verify the effectiveness of our proposed cross-learning strategy. Our experiments are carried out on two publicly available person re-id datasets. Obvious performance improvements can be observed on both datasets.
Deep Learning-based Detection of Overlapping Cells

Detection and quantification of cells in general is one of the key challenges in many clinical trials for disease diagnosis and monitoring. Automation of this task enables quantitative analysis of digital images with a high processing rate, which is a support to pathologist at various kind of analyses. Recent studies have already indicated that deep learning usually yield superior accuracy in the field of digital pathology. One of the challenges tackled by the researches is to detect cells in images when cells are highly overlapped, over illuminated or partially occluded with the noise. Therefore, we focused on two conceptually different deep learning models, specifically U-Net and Mask R-CNN, in order to evaluate their capability and performance on the detection of overlapping cells. The dataset used in the study contains different types of images, possible observed under different lighting conditions, and the amount of target cells may range from tens to thousands, therefore the algorithm is required to be flexible enough.
Evaluation of Structures and Methods for Resolution Determination of Remote Sensing Sensors

Effective image resolution is an important image quality factor for remote sensing sensors and significantly affects photogrammetric processing tool chains. Tie points, mandatory for forming the block geometry, fully rely on feature points (i.e. SIFT, SURF) and quality of these points however is significantly correlated to image resolution. Spatial resolution can be determined in different ways. Utilizing bar test charts (e.g. USAF51), slanted edges (ISO 12233) and Siemens-Stars are widely accepted techniques. The paper describes these approaches and compares all in one joint experiment. Moreover, Slanted-Edge and Siemens-Star method is evaluated using (close to) ideal images convolved with known parameters. It will be shown that both techniques deliver conclusive and expected results.
A Benchmark of Modeling for Sentiment Analysis of The Indonesian Presidential Election in 2019

Researching with a machine learning method approach, the truth is to try to solve a case by using various algorithmic approaches to obtain the most suitable model for a case. In this research, we want to know which process of modelling that has the best accuracy value for classifying emotions in the text. The algorithm used is using the LSTM algorithm, while the benchmarking that we tested is the Random Forest and Naive Bayes algorithm. This research takes public opinion about the 2019 Indonesian Presidential Election by classifying it into four types of emotions: happy, sad, angry, and afraid. The data we use contains more than 1200 Indonesian tweets. In this experiment, we achieved an accuracy of 68.25% using the Random Forest model, whereas, with the Multinomial Naïve Bayes model, the accuracy was 66%.
A Non-Linear Differentiable CNN-Rendering Module for 3D Data Enhancement

In this article we introduce a differentiable rendering module which allows neural networks to efficiently process 3D data. The module is composed of continuous piecewise differentiable functions defined as a sensor array of cells embedded in 3D space. Our module is learnable and can be easily integrated into neural networks allowing to optimize data rendering towards specific learning tasks using gradient based methods in an end-to-end fashion. Essentially, the module's sensor cells are allowed to transform independently and locally focus and sense different parts of the 3D data. Thus, through their optimization process, cells learn to focus on important parts of the data, bypassing occlusions, clutter, and noise. Since sensor cells originally lie on a grid, this equals to a highly non-linear rendering of the scene into a 2D image. Our module performs especially well in presence of clutter and occlusions as well as dealing with non-linear deformations to improve classification accuracy through proper rendering of the data. In our experiments, we apply our module in various learning tasks and demonstrate that using our rendering module we accomplish efficient classification, localization, and segmentation tasks on 2D/3D cluttered and non-cluttered data.
Improvement of Whale Algorithm and Application

The whale algorithm is a kind of artificial intelligence optimization algorithm. This paper adds nonlinear convergence factor and adaptive inertia weight to the traditional whale algorithm, introduces the concept of quantum revolving door in whale position update, and uses nonlinear convergence factor to whale. The variable variation of the algorithm is modified to adjust the balance between global search and local search, and solves the problem of slow convergence in traditional whale algorithm. The concept of adaptive inertia weight in particle swarm optimization is applied. In the improvement of the local optimization ability of the whale algorithm, the concept of quantum revolving door is introduced in every position update of the whale algorithm, which makes the overall performance of the whole algorithm and the optimal solution have significant performance improvement. The superiority of the improved algorithm is verified by simulation experiments.
An improved foraging heuristic considering reduced fog latency

Summary Most often, in centralized distributed computing, users are getting impatient by experiencing the delays in loading the content of the web applications over the internet; it is usually caused by the poor latency management and is becoming a catastrophic situation in availing the services and applications of the centralized‐cloud computing; cloud being a distributed platform, its customers are facing the latencies issues at the various tiers of the cloud. Workloads distributions are occurring concurrently on various virtual cloud platforms. Latency‐oriented applications and services usually in cloud platform are running over various virtual machines and possess the high complexities while interacting and face the difficulties in the consolidations of the various applications containing heterogenetic workloads. In this paper, we study the fog computing and consider that it will be a feasible platform to get rid of the cloud high‐latency issues and to reduce the delay time and workflow across the network. The main purpose is to enhance the latency across the various nodes in a network. The factors like interference, availability, and interaction among various participating nodes with nearer VMware are of importance for reducing the latencies. Reduced latencies will enhance the quality of service parameters, resources, and services availability. Latency can be an important factor for the resource optimization issues in cloud computing. The comparisons with existing technologies greatly show that latency in fog is quite efficient than in the cloud paradigm.
Energy Efficient Implementation of Machine Learning Algorithms on Hardware Platforms

Machine and deep learning algorithms are currently employed for many applications such as computer vision, speech recognition and portable/wearable electronics. Regrettably, machine learning algorithms have high complexity adding more challenges for the implementation of such algorithms on embedded hardware platforms. This paper aims to present an overview about state of the art techniques enabling efficient implementation of Machine and Deep learning (ML/DL) algorithms aiming to improve the energy efficiency. An assessment of the algorithms suitable for embedded implementation is provided, presenting some hardware platforms supporting artificial intelligent systems. On the other hand, we have exploited the choice of implementing ML/DL algorithm on embedded hardware platforms.
Image Processing and Model-Based Spill Coverage Path Planning for Unmanned Surface Vehicles

Remote sensing technology and unmanned surface vehicles (USVs) have great potential for spill coverage applications. However, there is still a lack of research on adequate path planners for USVs based on remote sensing images. In this study, we propose an image processing and model-based path planner that can generate an efficient path for a USV to cover a spill according to aerial images from remote sensing. We mathematically formulate a spill processing model of the conceptual USV, which can remove the spill by suction mechanism, to determine its speed limit and operation range. To perform coverage with high completeness to the spill in the workspace, we first develop an image segmentation strategy and partition the area with geometric tessellation. We then formulate the coverage problem with tessellation as a traveling salesman problem (TSP) and utilize the self-organizing map (SOM) approach for effective path planning. Finally, with real aerial images containing spills, we demonstrate the effectiveness of the proposed path planning method.
Extractive Odia Text Summarization System: An OCR Based Approach

Automatic text summarization is considered as a challenging task in natural language processing field. In the case of multilingual scenario particularly for the low-resource, morphologically complex languages the availability of summarization data set is rare and difficult to construct. In this work, we propose a novel technique to extract Odia text from the image files using optical character recognition (OCR) and summarize the obtained text using extractive summarization techniques. Also, we performed a manual evaluation to measure the quality of summaries to validate our techniques. The proposed approach is found suitable for generating summarized Odia text and the same technique can also extend to other low-resource languages for extractive summarization system.