Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactive Power Control of a 3-phase Grid-Connected Building Integrated Photovoltaic System

Recently, a tendency in the growing of grid-connected building integrated photovoltaic (BIPV) systems has been noticed in most countries. Hence, high penetration of PV power into the system network can be observed in many points in the network. This may cause severe problems in the distribution network originated by the intermittent behaviour of PV systems. As a feasible solution, the reactive power capability of voltage source inverter in PV systems can be employed rather than approaching expensive grid infrastructures in the distribution network. The purpose of this study is to implement a 3-phase grid-connected BIPV system with reactive power control to regulate the system voltage and improve the system power factor. Subsequent to this, the system model is designed and developed in MATLAB Simulink application. The inverter control system along with the reactive power control is developed for the proper system operation at different system conditions to reduce the var compensation on the utility grid. A better system accuracy of the developed system model is verified by validating with real system data.
Generalized Benders Decomposition to Secure Energy-Efficient Resource Allocation for Multiuser Full-Duplex Relay Cooperative Networks

In this paper, we investigate the issue of resource allocation for secure energy efficient communication in a multiuser orthogonal frequency division multiplexing (OFDM) based full-duplex (FD) relaying network in the presence of a passive eavesdropper whose channel state information (CSI) is not perfectly known. Our goal is to maximize the overall secure energy efficiency (SEE), which presents the relationship between energy consumption and secrecy performance. In the context of multiuser communications, such a resource allocation strategy jointly combines subcarrier permutation, subcarrier pair allocation, as well as power allocation altogether. The considered optimization problem is formulated as a mixed integer nonconvex programming problem, which is generally NP hard. Analyzing the property of such a problem, we first use the Dinkelbach's method to eliminate the fractional form and then exploit Generalized Benders decomposition to decouple the original problem into a master problem for pure integer programming and a primal problem for nonlinear programming. More specific, given the nonconvexity of the primal problem, we accordingly transform it into an equivalent relaxed convex problem by applying dual decomposition, alternative convex search, and difference of convex function programming. The numerical results are provided to validate the theoretical analysis and to demonstrate the effectiveness of the proposed algorithm.
Scalable Global Optimization via Local Bayesian Optimization

Bayesian optimization has recently emerged as a popular method for the sample-efficient optimization of expensive black-box functions. However, the application to high-dimensional problems with several thousand observations remains challenging, and on difficult problems Bayesian optimization is often not competitive with other paradigms. In this paper we take the view that this is due to the implicit homogeneity of the global probabilistic models and an overemphasized exploration that results from global acquisition. This motivates the design of a local probabilistic approach for global optimization of large-scale high-dimensional problems. We propose the TuRBO algorithm that fits a collection of local models and performs a principled global allocation of samples across these models via an implicit bandit approach. A comprehensive evaluation demonstrates that TuRBO outperforms state-of-the-art methods from machine learning and operations research on problems spanning reinforcement learning, robotics, and the natural sciences.
GENERATING DYNAMIC 3D SCENES FOR RENDERING BENCHMARKS

Performance comparison of different rendering methods is either based on synthetic 3D scenes or commonly used 3D scenes. A lot of static scenes exist but dynamic scenes are not common. Performance of modern rendering methods depends on the dynamic state of objects and varies with an increasing number of objects, degree of scene volatility. In this paper, two new methods of dynamic scene generation are proposed. We consider the bottlenecks of common rendering methods, based on which the parameters of the proposed methods are selected. We propose a method for dynamic scene generation, which allows to create scenes with a specified number of objects, vertices, different appearance order, temporal coherence of visibility, degree of spatial coherence of appearance and other parameters. We propose a method for converting of static scenes into dynamic scenes using space-filling curves to determine the order in which objects appear in a scene. The proposed methods will fill the gap in the field of rendering benchmarks and will allow to use the proven static scenes when evaluating the performance of methods with dynamic objects.
Correctness and Progress Verification of Non-Blocking Programs

The progression of multi-core processors has inspired the development of concurrency libraries that guarantee safety and liveness properties of multiprocessor applications. The difficulty of reasoning about safety and liveness properties in a concurrent environment has led to the development of tools to verify that a concurrent data structure meets a correctness condition or progress guarantee. However, these tools possess shortcomings regarding the ability to verify a composition of data structure operations. Additionally, verification techniques for transactional memory evaluate correctness based on low-level read/write histories, which is not applicable to transactional data structures that use a high-level semantic conflict detection. In my dissertation, I present tools for checking the correctness of multiprocessor programs that overcome the limitations of previous correctness verification techniques. Correctness Condition Specification (CCSpec) is the first tool that automatically checks the correctness of a composition of concurrent multi-container operations performed in a non-atomic manner. Transactional Correctness tool for Abstract Data Types (TxC-ADT) is the first tool that can check the correctness of transactional data structures. TxC-ADT elevates the standard definitions of transactional correctness to be in terms of an abstract data type, an essential aspect for checking correctness of transactions that synchronize only for high-level semantic conflicts. Many practical concurrent data structures, transactional data structures, and algorithms to facilitate non-blocking programming all incorporate helping schemes to ensure that an operation comprising multiple atomic steps is completed according to the progress guarantee. The helping scheme introduces additional interference by the active threads in the
A Unified Object Detection Framework for Intelligent Retail Container Commodities

Motivated by commodity detection in retail, this paper studies the problem of generating object bounding boxes in retail images. The existing object detection algorithms are not suitable due to the characteristics of the intensive distribution of commodities in intelligent retail containers. Therefore, we present a unified object detection framework used for dense scenarios in retail images, which consists of the hierarchical labeling pattern, the Similarity Recognition Network (SRN) subnetwork and the optimized NMS algorithm on the basis of YOLOv3. Three datasets are built to verify the effectiveness of the proposed framework, two of which are used for training the object detection network, and the other one is used for training the SRN subnetwork. The experimental results show that the proposed framework achieves a significant improvement for dense scenario in retail images compared with the traditional object detection algorithms.
Jointgraph: A DAG‐based efficient consensus algorithm for consortium blockchains

The blockchain is a distributed ledger that records all transactions and operations in a shared manner. Public blockchains such as Bitcoin realize decentralization at the cost of mining overhead, which is not suitable for real‐life scenarios requiring high throughput. Techniques such as the consortium blockchain improve efficiency through partial decentralization. However, the consensus algorithms used in the existing state‐of‐the‐art consortium blockchains face many challenges when dealing with commercial applications. For example, the high communication overhead hinders the scalability of PBFT‐based consensus algorithms even though they are efficient at small scale. Hashgraph, one of the most popular Directed Acyclic Graph‐based (DAG‐based) consensus algorithms, achieves good performance in scalability; however, it does not allow users' dynamic participation. To deal with these challenges, we propose Jointgraph, a Byzantine fault‐tolerance consensus algorithm for consortium blockchains based on DAG. In Jointgraph, transactions are packed into events and validated by no less than 2/3 of all members. A supervisor is introduced in our design, who monitors member behaviors and improves consensus efficiency. Simulation results demonstrate that Jointgraph outperforms Hashgraph in both throughput and latency.
A Lexicon-Based Graph Neural Network for Chinese NER

Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success. However, the characteristic of chain structure and the lack of global semantics determine that RNN-based models are vulnerable to word ambiguities. In this work, we try to alleviate this problem by introducing a lexicon-based graph neural network with global semantics, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency. Based on the multiple graph-based interactions among characters, potential words, and the whole-sentence semantics, word ambiguities can be effectively tackled. Experiments on four NER datasets show that the proposed model achieves significant improvements against other baseline models.
MULTI-SENSOR 3D RECORDING PIPELINE FOR THE DOCUMENTATION OF
JAVANESE TEMPLES

Abstract. 3D recording is an important procedure in the conservation of heritage sites. This past decade, a myriad of 3D sensors has appeared in the market with different advantages and disadvantages. Most notably, the laser scanning and photogrammetry methods have become some of the most used techniques in 3D recording. The integration of these different sensors is an interesting topic, one which will be discussed in this paper. Integration is an activity to combine two or more data with different characteristics to produce a 3D model with the best results. The discussion in this study includes the process of acquisition, processing, and analysis of the geometric quality from the results of the 3D recording process; starting with the acquisition method, registration and georeferencing process, up to the integration of laser scanning and photogrammetry 3D point clouds. The final result of the integration of the two point clouds is the 3D point cloud model that has become a single entity. Some detailed parts of the object of interest draw both geometric and textural information from photogrammetry, while laser scanning provided a point cloud depicting the overall overview of the building. The object used as our case study is Sari Temple, located in Special Region of Yogyakarta, Indonesia.
Precise Adjacent Margin Loss for Deep Face Recognition

Softmax loss is arguably one of the most widely used loss functions in CNNs. In recent years some Softmax variants have been proposed to enhance the discriminative ability of the learned features by adding additional margin constraints, which significantly improved the state-of-the-art performance of face recognition. However, the ‘margin’ referenced in these losses does not represent the real margin between the different classes in the training set. Furthermore, they impose a margin on all possible combinations of class pairs, which is unnecessary. In this paper we propose the Precise Adjacent Margin loss (PAM loss), which gives an accurate definition of ‘margin’ and has precise operations appropriate for different cases. PAM loss has better geometrical interpretation than the existing margin-based losses. Extensive experiments are conducted on LFW, YTF, MegaFace and FaceScrub datasets, and results show that the proposed method has state-of-the-art performance.
Bridging the Defined and the Defining: Exploiting Implicit Lexical Semantic Relations in Definition Modeling

Definition modeling includes acquiring word embeddings from dictionary definitions and generating definitions of words. While the meanings of defining words are important in dictionary definitions, it is crucial to capture the lexical semantic relations between defined words and defining words. However, thus far, the utilization of such relations has not been explored for definition modeling. In this paper, we propose definition modeling methods that use lexical semantic relations. To utilize implicit semantic relations in definitions, we use unsupervisedly obtained pattern-based word-pair embeddings that represent semantic relations of word pairs. Experimental results indicate that our methods improve the performance in learning embeddings from definitions, as well as definition generation.
Dynamic Spatial Predicted Background for Video Surveillance

Video foreground-background separation is considered as a basic step for many computer vision applications. Common approaches excel in handling background variances while trying to keep the computational load low. We propose a novel method that models the scene as a superposition of illumination effects while predicting each pixel’s value with a linear estimator comprised by a few other pixels of the scene. By doing so, we are able to achieve real-time performance using minimal hardware, which is a crucial consideration for embedding such a system on surveillance cameras. Experimental results on two common datasets show our method’s potential by comparing it to state-of-the-art methods.
Neural Attribution for Semantic Bug-Localization in Student Programs

Providing feedback is an integral part of teaching. Most open online courses on programming make use of automated grading systems to support programming assignments and give real-time feedback. These systems usually rely on test results to quantify the programs' functional correctness. They return failing tests to the students as feedback. However, students may find it difficult to debug their programs if they receive no hints about where the bug is and how to fix it. In this work, we present NeuralBugLocator, a deep learning based technique, that can localize the bugs in a faulty program with respect to a failing test, without even running the program. At the heart of our technique is a novel tree convolutional neural network which is trained to predict whether a program passes or fails a given test. To localize the bugs, we analyze the trained network using a state-of-the-art neural prediction attribution technique and see which lines of the programs make it predict the test outcomes. Our experiments show that NeuralBugLocator is generally more accurate than two state-of-the-art program-spectrum based and one syntactic difference based bug-localization baselines.
Rule-Based Approach for Constrained Motion Control of a Teleoperated Robot Arm in a Dynamic Environment

This paper presents a preliminary robotic solution for constrained teleoperation tasks in an uncertain and dynamic environment. The robotic system is supported by a reasoning agent which makes the control action reactive and context-sensitive. The investigation is motivated by the future Human-Robot collaboration, therefore, it focuses on minimizing or avoiding collisions within the robot and the surroundings objects. The report describes the developed control architecture, which, in its modular and hierarchical structure, combines knowledge from different areas such as control theory, path and trajectory planning, computer vision, collision avoidance, and decision-making theory. The software is implemented in a ROS framework, in order to support a clear and modular design, suitable for future extensions and integration on different hardware components. The experiments are run on both real and simulated systems. The results show an autonomous robot capable of continuously adapting its movements despite the external agent interruptions, with a 99% success rate. We can conclude that an adaptive robotic system capable of performing constrained tasks and simultaneously reacting to external stimuli in an uncertain and dynamic environment is potentially obtainable.
Automated Machine Learning in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures

The use of machine learning (ML) algorithms significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the feature of interest? What are the best parameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated machine learning (autoML) has been gaining attention. Here, we used TPOT which is a tree-based pipeline optimisation tool that scans a model space of models, their hyperparameters and finds the model with the highest accuracy. To explore autoML approaches and evaluate their efficacy within neuroimaging datasets, we choose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT could scan through the model space and create pipelines that outperform the state-of-the-art accuracy for Freesurfer-based models (MAE: 4.89 years) using only the cortical thickness and subcortical volume information. It also suggests interesting ensembles that do not match the current most used models for brain prediction but generalise well to an unseen dataset (MAE: 4.94 years). Thus, TPOT can be used as a data-driven approach to find ML models that accurately predict brain age.
Investigating the Effectiveness of BPE: The Power of Shorter Sequences

Byte-Pair Encoding (BPE) is an unsupervised sub-word tokenization technique, commonly used in neural machine translation and other NLP tasks. Its effectiveness makes it a de facto standard, but the reasons for this are not well understood. We link BPE to the broader family of dictionary-based compression algorithms and compare it with other members of this family. Our experiments across datasets, language pairs, translation models, and vocabulary size show that - given a fixed vocabulary size budget - the fewer tokens an algorithm needs to cover the test set, the better the translation (as measured by BLEU).
Comparative Study ofConvolutional Neural Network with Word Embedding Technique for Text Classification

—This paper presents an investigation of the convolutional neural network (CNN) with Word2Vec word embedding technique for text classification. Performance of CNN is tested on seven benchmark datasets with a different number of classes, training and testing samples. Test classification results obtained from proposed CNN are compared with results of CNN models and other classifiers reported in the literature. Investigation shows that CNN models are better suitable for text classification than other techniques. The main objective of the paper is to identify best-fitted parameter values batch size, epochs, activation function, dropout rates and feature maps values. Results of proposed CNN are better than many other classification techniques reported in the literature for Yelp Review Polarity dataset and Amazon Review Polarity dataset. For all the seven datasets, accuracy obtained by proposed CNN is close to the best-known results from the literature.
True motion estimation for video frame rate up-conversion

Since the invention of high definition display technologies, video standards conversion problem has become an important task to store, transmit and display new video formats. One of the standards conversion issue, video frame rate up-conversion (FRUC) should be considered as an important problem for high definition displays because these displays reach high refresh rates up to 100 Hz and low video frame rates should be increased before displaying. The solutions generated by conventional FRUC techniques cause artifacts such as jerky motion and judder effect on the screen. The objective of this thesis is to remove judder and occlusion artifacts and generate a smooth object motion for high definition displays. In order to solve the problem, this thesis describes a multi-stage motion vector (MV) post-processing technique for true motion estimation and proposes a new video frame rate up-conversion method with occlusion adaptive overlapped block motion compensation, which increases the temporal resolution without loss of spatial resolution and consistency. Conventional block matching algorithms yields us unreliable MVs which should be post-processed in the refinement stages to get a consistent MV field in spatial domain. Relations between neighboring MVs are utilized to detect occlusion regions. Overlapped Block Motion Compensation (OBMC) is adapted to these problematic regions in order to reduce halo artifacts caused by occlusion. Compared to existing methods, the proposed algorithm achieves FRUC with reduced loss of spatial resolution and reduced amount of artifacts.
Video Face Recognition using Deep Learning based Representation

: Smart phones and surveillance cameras have initiated research in video face identification. Face recognition in video is extremely significant in the area of law enforcement purposes, the recent advances have reported high accuracies at equal error rates, performance at lower false accept rates require significant enhancement. The decision of verification can be obtained by using a classifier called multilayer neural network. The proposed algorithm yields accuracy of verification about 97% at equal error rate on the database of YouTube faces. We proposed to design a novel video face verification algorithm that uses discrete wavelet transform and entropy computation to select feature-rich frames from a video sequence. This proposed algorithm is implemented and simulated using MATLAB tool.
Hyperspectral Image Classification Based on Generative Adversarial Network with Dropblock

Deep learning (DL) algorithms are widely applied in hyperspectral images (HSIs) classification. However, the insufficient utilization in spatial semantic information and inadequate number of HSIs samples both restrict the classification performance of DL-based HSIs algorithms. In this paper, we propose a novel method based on generative adversarial network (GAN) with DropBlock structure (DBGAN). Specifically, DropBlock enforces each unit in convolution neural network (CNN) to learn features by dropping contiguous regions of feature maps, therefore more spatial semantic information is capable to contribute in HSIs classification. Furthermore, GAN model can generate realistic samples by an adversarial game to mitigate HSIs data shortage. Extensive experimental comparisons demonstrate the effectiveness of the proposed method.