Reviewerly/exHarmony huggingface dataset:
Global Convolutional Neural Networks With Self-Attention for Fisheye Image Rectification

Fisheye images are attracting attention in computer vision such as autonomous vehicles and virtual reality because of the wide field of view (WFoV). However, fisheye images have geometric distortions caused by the refractive index of the lens. Conventional fisheye rectification methods require multiple images to calculate distortion coefficients and lens intrinsic parameters. This means that if the fisheye lens is changed, the same operation will have to be repeated. On the other hand, by using deep learning, images with different distortion coefficients can be rectified. Also, with end-to-end learning, no feature engineering is required. To improve the performance of fisheye image rectification, we propose global convolutional neural networks with self-attention to rectify the fisheye images. The proposed method employs dilated convolutional neural networks (D-CNNs) to enlarge receptive fields, and self-attention to extract the most important features of input images. In this way, the proposed method can extract global features from input images. To better train and evaluate the proposed method, we generate fisheye images from the Place2 dataset with Cartesian and polar coordinates, and label them with original images (ground-truth). we also schedule the learning rate with cosine annealing and use an integrated loss function. The experimental results show that the proposed method achieves an excellent performance in both qualitative and quantitative evaluations.
Comparison of Machine Learning Algorithms for Rainfall Prediction in Semarang City

Floods often occur in large urban areas and lowlands such as Jakarta, Semarang, Surabaya, etc. Based on disaster recapitulation data from Badan Penanggulangan Bencana Daerah (BPBD) / the Regional Disaster Management Agency of Semarang City in 2020, there were 23 floods in Semarang City. Based on the explanation, it is necessary to have an application that can predict rain in Semarang City to provide information about tomorrow’s rain prediction based on daily climate data from the BMKG Semarang Climatology Station. This study uses several machine learning classification methods to get the best model for predicting rainfall. In assessing the performance of the prediction model, several evaluations were used, such as accuracy, precision, recall, f-measure, AUC score, and 10-fold cross-validation. The results showed that the CatBoost Classifier prediction model had the best performance with an accuracy value of 76.77% and a precision value of 78.70%.
Deep Learning Based Short-Term Total Cloud Cover Forecasting

In this research, we conduct deep learning based Total Cloud Cover (TCC) forecasting using satellite images.The proposed system employs the Otsu's method for cloud segmentation and Long Short-Term Memory (LSTM) variant models for TCC prediction.Specifically, a region-based Otsu's method is used to segment clouds from satellite images.A time-series dataset is generated using the TCC information extracted from each image in image sequences using a new feature extraction method.The generated time series data are subsequently used to train several LSTM variant models, i.e.LSTM, bi-directional LSTM and Convolutional Neural Network (CNN)-LSTM, for future TCC forecasting.Our approach achieves impressive average RMSE scores with multi-step forecasting, i.e. 0.0543 and 0.0823, with respect to both the first half of daytime and full daytime TCC forecasting on a given day, using the generated dataset.
Design of a Fuzzy Logic Controller for Washing Machine

In this project, a Fuzzy Logic Controller for a washing machine was designed using FisPro. The controller considers not only the inputs related to the washing strategy but also the status of the washing machine, such as the door status and levelling/balancing. Inputs of the washing machine are load mass, fabric type (roughness) of the clothes, soil level (the dirtiness of the clothes), levelling/balancing of the washing machine, and status of the door (open, close, partially open); whereas outputs of the washing machine are washing time, spinning speed, rinse cycle, water intake, water temperature, and the operation of washing machine itself (on or off) with no error. These inputs and outputs were considered as either fuzzy or Boolean sets according to their nature. A total of 48 fuzzy rules have been developed based on human knowledge and reasoning. Simulation has shown that the system output of the controller is realistic and satisfactory. (Abstract)
On Conditional Belief Functions in the Dempster-Shafer Theory

The primary goal is to define conditional belief functions in the Dempster-Shafer theory. We do so similar to the notion of conditional probability tables in probability theory. Conditional belief functions are necessary for constructing directed graphical belief function models in the same sense as conditional probability tables for constructing Bayesian networks. Besides defining conditional belief functions, we state and prove a few basic properties of conditionals. We provide several examples of conditional belief functions, including those obtained by Smets' conditional embedding.
Modulation Recognition based on Complex Binarized Neural Network

In recent years, communication technology is developing rapidly. With an increasing number of radio devices entering people's life there have been emerged much more communication modulation methods being used when modulation recognition technology has very important application value. In the past few years, the development and progress of deep learning is remarkable, using deep learning algorithms to solve the modulation recognition problem also has an excellent performance. Faced with massive amounts of electromagnetic data and resource limited hardware, existing technologies cannot meet the needs of deployable systems to handle short-term observations or the sudden arrival of a short-term signal and immediate decision-making. In this paper, Binary Complex Neural Network (BCNN) combining Binary Neural Network (BNN) and Deep Complex Neural Network (DCN) is used for modulation recognition, and we propose a scheme of Lightweight Neural Network for Electromagnetic Signal (LNN-ES). Experiments show that compared with BNN, BCNN has a better result in the recognition accuracy. And compared with the residual network, the lightweight neural network proposed in this paper can achieve the same recognition accuracy with less parameters.
A DEEP ENSEMBLE LEARNING METHOD FOR EFFORT-AWARE JUST-IN-TIME DEFECT PREDICTION

Nowadays, logistics for transportation and distribution of merchandise are a key element to increase the competitiveness of companies. However, the election of alternative routes outside the panned routes causes the logistic companies to provide a poor-quality service, with units that endanger the appropriate deliver of merchandise and impacting negatively the way in which the supply chain works. This paper aims to develop a module that allows the processing, analysis and deployment of satellite information oriented to the pattern analysis, to find anomalies in the paths of the operators by implementing the algorithm TODS, to be able to help in the decision making. The experimental results show that the algorithm detects optimally the abnormal routes using historical data as a base.
Use of gamification in the teaching process of solving logistic tasks

This article explores different forms of gamification and its direct use in teaching. It shows that games are an essential part of a person's life in modern times and must also not be missing in education. In the introduction, gamification is described as a way of gaining experience and skills using games and gradually transitioning into forms of education. Part of the work output is a game based on accumulated knowledge from gamification and the university environment. The developed game helps students of the Scheduling and Logistics course to understand better individual models and tasks in the form of a gaming experience. The article describes game development as its solution to gamification and addresses its use in practice. The report explains and statistically proves that even a particular form of gamification is essential in teaching and helps improve study results.
OBE Integrated Teaching Practice of Single-Chip Microcomputer Course

The teaching practice and the problems in traditional teaching of principle of single-chip microcomputer and application course are expounded.In order to improve the application ability of students, with the concept of OBE, the integrated teaching mode of theory and practice teaching is adopted.From multiple perspectives, the cultivation of application ability is enhanced through teaching content, teaching methods and assessment methods.Students are encouraged to design independently, and practice-based teaching is discussed to improve the application ability of students.
Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution

Real-time planning for a combined problem of target assignment and path planning for multiple agents, also known as the unlabeled version of Multi-Agent Path Finding (MAPF), is crucial for high-level coordination in multi-agent systems, e.g., pattern formation by robot swarms. This paper studies two aspects of unlabeled-MAPF: (1) offline scenario: solving large instances by centralized approaches with small computation time, and (2) online scenario: executing unlabeled-MAPF despite timing uncertainties of real robots. For this purpose, we propose TSWAP, a novel sub-optimal complete algorithm, which takes an arbitrary initial target assignment then repeats one-timestep path planning with target swapping. TSWAP can adapt to both offline and online scenarios. We empirically demonstrate that Offline TSWAP is highly scalable; providing near-optimal solutions while reducing runtime by orders of magnitude compared to existing approaches. In addition, we present the benefits of Online TSWAP, such as delay tolerance, through real-robot demos.
Research of Error Control Coding and Decoding

Error control technology improves the reliability and validity of communication transmission. Linear block code, cyclic code, convolutional code, LDPC code and CRC are commonly used. In this paper, the principles of these codes are analysed, simulations of encoding and decoding are experimented. According to the simulation results, it is concluded that error control coding is helpful to reduce the error rate of the communication system.
Performance Improvement of Blockchain-based IoT Applications using Deep Learning Techniques

Internet of Things (IoT) deployments have increased drastically based on third-party (fog-assisted architecture) mechanisms to store, process, and share sensor data. IoT environments are mostly vulnerable to security threats due to the lack of intrinsic security measures. Blockchain technology with an untrusty framework to establish trust communication among IoT devices becomes a major concern in lightweight IoT frameworks. To solve this trust issue, we propose a DeepIoT-Block model that combines the consensual deep learning (CDL) technique using the elliptic Diffihelman protocol to strengthen the blockchain-based data storage scheme (BDSS) and Directed Acyclic Graph (DAG) to construct the blockchain network. DeepIoT-Block has implemented using a blockchain system for IoT applications to address storage security issues. DeepIoT-Block guarantees simultaneous computational complexity and transaction efficiency. The performance of the proposed model was verified and validated for IoT-based smart road traffic data. The simulation outcomes show that our proposed model, DeepIoT-Block, is computationally efficient and secure for larger scale IoT applications.
DFBVS: Deep Feature-Based Visual Servo

Classical Visual Servoing (VS) relies on handcrafted visual features, which limit their generalizability. Recently, a number of approaches, some based on Deep Neural Networks, have been proposed to overcome this limitation by comparing directly the entire target and current camera images. However, by getting rid of the visual features altogether, those approaches require the target and current images to be essentially similar, which precludes the generalization to unknown, cluttered, scenes. Here we propose to perform VS based on visual features as in classical VS approaches but, contrary to the latter, we leverage recent breakthroughs in Deep Learning to automatically extract and match the visual features. By doing so, our approach enjoys the advantages from both worlds: (i) because our approach is based on visual features, it is able to steer the robot towards the object of interest even in presence of significant distraction in the background; (ii) because the features are automatically extracted and matched, our approach can easily and automatically generalize to unseen objects and scenes. In addition, we propose to use a render engine to synthesize the target image, which offers a further level of generalization. We demonstrate these advantages in a robotic grasping task, where the robot is able to steer, with high accuracy, towards the object to grasp, based simply on an image of the object rendered from the camera view corresponding to the desired robot grasping pose.
A Case Study of Building Shared Understanding of Non-Functional Requirements in a Remote Software Organization

Building a shared understanding of non-functional requirements (NFRs) is a known but understudied challenge in requirements engineering, especially in organizations that adopt continuous software engineering (CSE) practices. During the peak of the COVID-19 pandemic, many CSE organizations complied with working remotely due to the imposed health restrictions; some continued to work remotely while implementing business processes to facilitate team communication and productivity. In remote CSE organizations, managing NFRs becomes more challenging due to the limitations to team communication. While previous research has identified the factors that lead to a lack of shared understanding of NFRs in CSE, we still have a significant gap in understanding how CSE organizations, particularly in remote work, build a shared understanding of NFRs. We conduct a three-month ethnography-informed case study of a remote CSE organization. Through thematic analysis of our qualitative data from interviews and observations, we identify a number of practices for building a shared understanding of NFRs, such as validating NFRs through feedback. The collaborative workspace the organization uses for remote interaction is Gather, which simulates physical workspaces, and which our findings suggest allows for informal communications instrumental for building shared understanding. In addition, we describe the limitations to building a shared understanding of NFRs in the organization, such as gaps in communication and the limited understanding of customer context. As actionable insights, we discuss our findings in light of proactive practices that represent opportunities for software organizations to invest in building a shared understanding of NFRs in their development.
Differentially Private Graph Neural Networks for Whole-Graph Classification

Graph Neural Networks (GNNs) have established themselves as state-of-the-art for many machine learning applications such as the analysis of social and medical networks. Several among these datasets contain privacy-sensitive data. Machine learning with differential privacy is a promising technique to allow deriving insight from sensitive data while offering formal guarantees of privacy protection. However, the differentially private training of GNNs has so far remained under-explored due to the challenges presented by the intrinsic structural connectivity of graphs. In this work, we introduce a framework for differential private graph-level classification. Our method is applicable to graph deep learning on multi-graph datasets and relies on differentially private stochastic gradient descent (DP-SGD). We show results on a variety of datasets and evaluate the impact of different GNN architectures and training hyperparameters on model performance for differentially private graph classification, as well as the scalability of the method on a large medical dataset. Our experiments show that DP-SGD can be applied to graph classification tasks with reasonable utility losses. Furthermore, we apply explainability techniques to assess whether similar representations are learned in the private and non-private settings. Our results can also function as robust baselines for future work in this area.
AI-mediated Job Status Tracking in AR as a No-Code service

Sophisticated AR experiences that can track the status of a manual assembly or maintenance job and provide corresponding guidance are built by teams of computer vision engineers over several months. These engineers model temporal causation on top of object detection and human skeletal tracking provided by traditional AR and AI SDKs. We demonstrate our Pathfinder system which automatically builds computational models of a complex manual task, such as a manufacturing assembly activity, given recorded demonstrations of the task - without requiring any custom coding. These computational models enable ordinary AR developers to build AI-mediated feedback in minutes instead of months. Keywords: Task Guidance, Computer Vision, Human Computer Interaction, Machine Learning, Industrial Applications
PSCM: Towards Practical Encrypted Unknown Protocol Classification

Network traffic classification is the basis for network management, Quality of Service and intrusion detection. As the number of Internet applications increases, the variety of unknown protocols grows, posing a significant challenge to network traffic classification. Traditional rule-based traffic classification methods are currently limited by the rise of dynamic ports and encryption protocols. Statistical methods using statistical features have good recognition of protocols with public formats. However, there is no public protocol format for unknown protocols, making it challenging to extract useful features. This paper proposes a practical Probability Statistics and Cluster Merging (PSCM) method to automatically extract encrypted unknown protocol features and map the clustering results to the actual protocols. Experimental results on real-world network traffic show that the method achieves an accuracy of 99.28% and performs well in the sampling scenarios.
An Attention Approach for Dimensionality Reduction That Can Correct Feature Collection Skewness

It has been demonstrated that adding an attention mechanism to a convolutional neural network enhances network performance. However, in practice, the skewness error during the extraction process affects the feature information owing to the implementation of the global average pooling operation, which eventually lowers the performance of the network design. We think that when the weight is activated, the spatial information is effectively captured while the extraction range of the channel information is narrowed. We also think that because the weight is activated using multiple local extraction substitutions, the influence of skewness error can be effectively reduced. As a result, we suggest dimensionality reduction attention(RA) in this study as a new type of attention mechanism. Dimensionality reduction attention is a method of combining spatial information and channel information into a new feature distribution by aggregating dimensionality reduction of feature information, in contrast to other attentions that act on distinct feature tensors in space and channel. Under the premise of capturing long-distance context information and guaranteeing feature coordinates, it may realize the locality of spatial information through this operation and identify the local information of each channel. To completely display the important feature information, the produced feature maps are then encoded into two complimentary attention maps along the spatial and channel directions, respectively. Our dimension reduction focus is a straightforward and all-encompassing module. We run tests on various datasets using various deep architectures and various class designs. The outcomes of the trial demonstrate that our attention-based approach has clear benefits.
Logistics Translator. Concept Vision on Future Interlanguage Computer Assisted Translation

The computer translation task is ambiguous and before starting the process it occurs many difficulties and questions to be solved. The simple texts on general topics can be fast translated by using the modern machine translation systems based on cloud exchange algorithms. In this regard it should be noted that the main problem exists by translation of specialized texts including terms, abbreviations and linguistics items related to the particular sublanguage. The issue of post-editing can be solved manually, but taking into account the volume of texts, it has been risen a strong demand to automatize the process by development and implementation a professional tool that can proceed the semi-automatic translation of sublanguages. In this regard it was researched the modern computer assisted translation programs, the main mechanism of their functioning, advantages and disadvantages and based on them it was developed the advanced web tool with friendly-user interface based on the main mistakes of the machine translation systems. This program is unique due to the wide range of tools that are aimed to the implementation of practical tasks and without any exaggeration, it can change the idea of computer translation due to the new approach to the interlanguage perception. For realizing not only translation task, but providing a complex professional solution, it was added some additional functions as translation memory (MT), Natural Language Toolkit (NLTK) and some other features aimed at performing special linguistic tasks. The using of the program and first conducted tests based on preselected multilingual corpora can only prove the practical value of this development. The conducted survey of received feedbacks only maintain and prove the necessity of further continuation of research aimed to creation the advanced computer assisted translation tool that could complete professional linguistic tasks, in particular as the main task to perform the high-quality translation of sublanguages.