Reviewerly/exHarmony huggingface dataset:
Broken Conductor Fault Location in Power Transmission Lines Using GMDH Function and Single-Terminal Data Independent of Line Parameters

The phenomenon of broken conductor faults (BCFs) in power transmission lines and, consequently, the suspension of the hot-line with no connection to ground, tower, or other conductive/non-conductive bodies is amongst special faults in terms of fault detection and location in the protection industry. Once such a failure occurs, the current of the faulty phase does not increase, which leads to the inability of standard fault detection functions in detecting the event. On the other hand, the variable nature of transmission line parameters due to weather conditions leads to misoperation and malfunction of fault detection and protection schemes of industrial relays in some cases. This paper, for the first time, presents a BCF location scheme without requiring line parameters data and only using magnitudes of current and voltage phasors of a single terminal based on Group Method of Data Handling (GMDH). In this method, a function is interpolated, the inputs of which are the current and voltage of the faulty phase, and its output are the accurate location of the fault. The function can be developed for all topologies of transmission lines. The proposed method is implemented in the MATLAB software and the obtained results verify the solidity and perfect performance of the method for different fault conditions.
High-Performance Object Detection for Optical Remote Sensing Images with Lightweight Convolutional Neural Networks

Convolutional neural network (CNN)-based object detection for optical remote sensing images has achieved higher accuracy compared with traditional detection methods with handcrafted features. However, the deep and large CNNs make it hard to be deployed in real-time scenarios with limited computation, storage, power and bandwidth resources, for example, data processing onboard airborne, satellites and unmanned aerial vehicles for search and rescue. Therefore, in this paper we present a high-performance object detection approach for optical remote sensing images. Based on the widely used Faster R-CNN framework, we integrate state-of-the-art lightweight CNNs as backbone to extract features, slim the heavy-head architecture of two-stage detector by reducing dimensions of features, and fine-tune our models with NWPU VHR-10 optical remote sensing dataset. Besides, the multi-threading for CPU and detection in batches for GPU are deployed to enhance the throughput of detectors and utilization of multi-core CPU and many-core GPU. Experiments show that our presented detection approach can significantly reduce the model size, computation complexity and detection time, while maintaining competitive accuracy. Specifically, the one with ShuffleNet-v2 and slimmed features has achieved a highest mean average precision of 94.39%, a lowest computational complexity of 18.97 Giga floating point operations, a highest detection speed of 90.10 frames per second (fps) for GPU and 3.07 fps for CPU, corresponding to speedups of 6.94X and 13.26X compared with the baseline on the benchmarked system, with a model size of 13.6 MB. Moreover, it further improves the efficiency and achieves 4.98 fps on CPU with 4 threads, and 200.24 fps on GPU with a batch size of 32.
Balancing Shareability and Positive Interdependence to Support Collaborative Problem-Solving on Interactive Tabletops

To support collaboration, researchers from different fields have proposed the design principles of shareability (engaging users in shared interactions around the same content) and positive interdependence (distributing roles and information to make users dependent on each other). While, on its own, each principle was shown to successfully support collaboration in different contexts, these principles are also partially conflicting, and their combination creates several design challenges. This paper describes how shareability and positive interdependency were jointly implemented in an interactive tabletop-mediated environment called Orbitia, with the aim of inducing collaboration between three adult participants. We present the design details and rationale behind the proposed application. Furthermore, we describe the results of an empirical evaluation focusing on joint problem-solving efficiency, collaboration styles, participation equity, and perceived collaboration effectiveness.
Storage and Communication Tradeoff for Wireless Coded Blockchains

Recently, coded blockchain systems have been proposed to encode blockchain into coded fragments and distributively store them. These fragments can be collected and decoded to obtain the original blockchain only when needed. This reduces the storage requirement of blockchains and makes it possible to incorporate blockchain in wireless Internet-of-Things (IoT) systems. However, transmitting such coded fragments consumes nonnegligible resource in wireless networks. Hence, this article studies the storage and transmission tradeoff for coded blockchains, and formulates the coded fragments assignment using an integer linear program (ILP). In particular, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -hop localization property is proposed to ensure that any device is able to collect sufficient coded fragments for decoding within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -hop neighbors. This article then proposes an efficient heuristic algorithm that is built upon the solution to the relaxed ILP. Apart from these, this article studies regrowing blockchains, where nodes dynamically join and leave the networks, and proposes an algorithm to adjust the coded fragments assignments. Simulation results show that the performance of the proposed heuristic algorithm is close to that of the ILP, and only a small portion of nodes need to recalculate their assignments when using the proposed adjustment algorithm in the regrowing blockchains.
Classification of Imbalanced Data Stream: Techniques and Challenges

As the number of generated data increases every day, this has brought the importance of data mining and knowledge extraction. In traditional data mining, offline status can be used for knowledge extraction. Nevertheless, dealing with stream data mining is different due to continuously arriving data that can be processed at a single scan besides the appearance of concept drift. As the pre-processing stage is critical in knowledge extraction, imbalanced stream data gain significant popularity in the last few years among researchers. Many real-world applications suffer from class imbalance including medical, business, fraud detection and etc. Learning from the supervised model includes classes whether it is binary- or multi-classes. These classes are often imbalance where it is divided into the majority (negative) class and minority (positive) class, which can cause a bias toward the majority class that leads to skew in predictive performance models. Handles imbalance streaming data is mandatory for more accurate and reliable learning models. In this paper, we will present an overview of data stream mining and its tools. Besides, summarize the problem of class imbalance and its different approaches. In addition, researchers will present the popular evaluation metrics and challenges prone from imbalanced streaming data.
Curriculum-Based Deep Reinforcement Learning for Adaptive Robotics: A Mini-Review

To facilitate the current and future automation needs, the research community constantly seeks to develop dynamic and efficient autonomous decision-making agents.These agents must not only be robust to modeling uncertainties, internal and external changes, but can adapt to a range of tasks also.Recent progress in deep reinforcement learning has corroborated to its potential to train such autonomous and robust agents.At the same time, the introduction of curriculum learning has made the reinforcement learning process significantly more efficient and allowed for training on much broader tasks.This combination, Curriculum-based Deep Reinforcement Learning (CDRL), presents a powerful solution to meet the increasing complexity of today's automation industry that demands highly intelligent machines.With this work we present a concise review of CDRL methods within the context of their application to the field of adaptive robotics.
CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection

Deep neural network based object detection has become the cornerstone of many real-world applications. Along with this success comes concerns about its vulnerability to malicious attacks. To gain more insight into this issue, we propose a contextual camouflage attack (CCA for short) algorithm to influence the performance of object detectors. In this paper, we use an evolutionary search strategy and adversarial machine learning in interactions with a photo-realistic simulated environment to find camouflage patterns that are effective over a huge variety of object locations, camera poses, and lighting conditions. The proposed camouflages are validated effective to most of the state-of-the-art object detectors.
An improved fully convolution network model for change detection in mining areas using sentinel-2 images

High-resolution satellite images have recently been widely used for change detection of mineral resources and mining area environments. However, with their increasing resolution and number of feature sub-categories, traditional machine learning methods have become sensitive to pseudo-changes and intra-class changes, which are prone to producing misclassifications. To address these problems, a novel fully convolutional network, the Hybrid Dilated Convolutional Siamese Network (HDC-Siam) was proposed. This model combines the modular dilated convolution network with the fully convolutional Siamese networks structure, in order to reduce the commission rate. In this paper, pairs of Sentinel-2 images with an interval of about two years was used as the experimental data. The HDC-Siam model was used to detect changes, where we evaluated the accuracy in the Dongsheng coalfield in Ordos, China and the Kuznetsk coalfield in Kemerovo, Russia. We obtained F1-scores of 85% and 75% for these respective locations. In addition, we conducted comparative experiments using two other methods – Fully Convolutional Siamese – Difference (FC-Siam-diff) and Fully Convolutional Early Fusion (FC-EF) – in order to verify that the HDC-Siam works.
A Transformer-based Radical Analysis Network for Chinese Character Recognition

Recently, a novel radical analysis network (RAN) has the capability of effectively recognizing unseen Chinese character classes and largely reducing the requirement of training data by treating a Chinese character as a hierarchical composition of radicals rather than a single character class. However, when dealing with more challenging issues, such as the recognition of complicated characters, low-frequency character categories, and characters in natural scenes, RAN still has a lot of room for improvement. In this paper, we explore options to further improve the structure generalization and robustness capability of RAN with the Transformer architecture, which has achieved start-of-the-art results for many sequence-to-sequence tasks. More specifically, we propose to replace the original attention module in RAN with the transformer decoder, which is named as a transformer-based radical analysis network (RTN). The experimental results show that the proposed approach can significantly outperform the RAN on both printed Chinese character database and natural scene Chinese character database. Meanwhile, further analysis proves that RTN can be better generalized to complex samples and low-frequency characters, and has better robustness in recognizing Chinese characters with different attributes.
Dance Action Recognition and Pose Estimation Based on Deep Convolutional Neural Network

Sports action recognition helps athletes correct their action range and standardize their poses. But it is not an easy task to recognize sports actions, due to the individual difference in action execution. Besides, the difficulty of action recognition increases with the diversity of actions and the complexity of background. The previous studies have not fully considered temporal changes, and failed to determine the exact staring point of actions. To solve the problem, this paper proposes a new method to recognize dance actions and estimate poses based on deep convolutional neural network (DCNN). Firstly, the authors presented full-effect expression of global and local features of dance actions, and derived an optimal model based on DeepPose. Next, a dance pose evaluation model was established based on time sequence segmentation network, and the sparse time sampling strategy was introduced to realize efficient and effective learning of the frame sequence of the whole video. Experimental results confirm the superiority of the full-effect expression of global and local features, and the effectiveness of the proposed model. The research results provide a reference for the application of deep learning (DL) in other scenarios of action recognition and pose estimation.
CHILab @ HaSpeeDe 2: Enhancing Hate Speech Detection with Part-of-Speech Tagging

The present paper describes two neural network systems used for Hate Speech Detection tasks that make use not only of the pre-processed text but also of its Part-of-Speech (PoS) tag. The first system uses a Transformer Encoder block, a relatively novel neural network architecture that arises as a substitute for recurrent neural networks. The second system uses a Depth-wise Separable Convolutional Neural Network, a new type of CNN that has become known in the field of image processing thanks to its computational efficiency. These systems have been used for the participation to the HaSpeeDe 2 task of the EVALITA 2020 workshop with CHILab as the team name, where our best system, the one that uses Transformer, ranked first in two out of four tasks and ranked third in the other two tasks. The systems have also been tested on English, Spanish and German languages.
Machine Learning in 3D Space Gesture Recognition

The rapid increase in the development of robotic systems in a controlled and uncontrolled environment leads to the development of a more natural interaction system. One such interaction is gesture recognition. The proposed paper is a simple approach towards gesture recognition technology where the hand movement in a 3-dimensional space is utilized to write the English alphabets and get the corresponding output in the screen or a display device. In order to perform the experiment, an MPU-6050 accelerometer, a microcontroller and a Bluetooth for wireless connection are used as the hardware components of the system. For each of the letters of the alphabets, the data instances are recorded in its raw form. 20 instances for each letter are recorded and it is then standardized using interpolation. The standardized data is fed as inputs to an SVM (Support Vector Machine) classifier to create a model. The created model is used for classification of future data instances at real time. Our method achieves a correct classification accuracy of 98.94% for the English alphabets’ hand gesture recognition. The primary objective of our approach is the development of a low-cost, low power and easily trained supervised gesture recognition system which identifies hand gesture movement efficiently and accurately. The experimental result obtained is based on use of a single subject.
Smart Campus Monitoring Based Video Surveillance using Haar Like Features and K-Nearest Neighbour

Intelligent video surveillance systems based on Internet of Things (IoT) technology have proven to be major primary components for security in many areas, such as smart cities.These systems are important because they provide messages that transfer information about people on campus among camera nodes, thereby providing real-time video surveillance monitoring.The proposed system consists of several cameras and an intelligent processing system, represented by a raspberry pi.The cameras are distributed in different locations in the university campus.Each camera node is connected to the internet and can communicate and share information with other nodes, as well as communicate with a central monitoring (server) via Message Queuing Telemetry Transport (MQTT) IoT protocol.The cameras can extract information in real time from video, and identify everyone as either students, teachers and/or employees using computer vision algorithms.Two methods of face detection and recognition techniques are applied: a feature-based technique that uses the Haar cascade, and an image-based technique that uses k-nearest neighbour (kNN).Face detection and recognition based on the Haar cascade classifier is more suitable for resources with embedded limited systems since it requires less computation, while kNN is more accurate and shows better results in a dynamic environment.All programs were written using opensource Python under a Linux operating system and by using OpenCV library.
Towards On-the-wall Tangible Interaction: Using Walls as Interactive, Dynamic, and Responsive User Interface

This paper presents our vision of on-the-wall tangible interaction. We envision a future where tangible interaction can be extended from conventional horizontal surfaces to vertical surfaces; indoor vertical areas such as walls, windows, and ceilings can be used for dynamic and direct physical manipulation. We first discuss the unique properties that vertical surfaces may offer for tangible interaction and the interaction scenarios they imbue. We then propose two potential paths for realizing on-the-wall interaction and the technical challenges we face. We follow with one prototype called Climbot. We showcase how Climbot can be used as an on-the-wall tangible user interface for dynamic lighting and as a wall switch controller. We conclude with a discussion of future work.
Super Resolution for Compressed Screen Content Video

In this paper, we concentrate on the super-resolution (SR) of compressed screen content video, in an effort to address the real-world challenges by considering the underlying characteristics of screen content. Firstly, we propose a new dataset for the SR of screen content video with different distortion levels. Meanwhile, we design an efficient SR structure that could capture the characteristics of compressed screen content video and manipulate the inner-connections in consecutive compressed low-resolution frames, facilitating the high-quality recovery of the high-resolution counter-part. Moreover, we design a new loss function for network training to better remedy the compression distortion and perceptual distortion. Experimental results demonstrate the effectiveness and superiority of the proposed method.
PENGEMBANGAN BUKU CERITA BERGAMBAR UNTUK MENINGKATKAN MINAT BACA SISWA KELAS IV SEKOLAH DASAR

The purpose of this paper is to know the effectiveness of picture storybook to improve student's interest in reading of the fourth-grade students of an elementary school in Yogyakarta. The Enhancement of student’s interest in reading of elementary school need to be surmounted with the right media in process learning. It aims to improve student's interest in reading through picture storybook effectively. The paper used quasi-experiment. The technique of analysis data to know the effectiveness picture storybook used pairs sample t-test with improved significant 0.05. The result of this paper shows that the learning process with picture storybook effective improves student's interest in reading with score -26,317 with the probability (sig) 0.000. This paper suggests that interest in reading can improve by picture storybook. The elements of picture storybook can improve student's interest in reading.
Efficient traversal of decision tree ensembles with FPGAs

System-on-Chip (SoC) based Field Programmable Gate Arrays (FPGAs) provide a hardware acceleration technology that can be rapidly deployed and tuned, thus providing a flexible solution adaptable to specific design requirements and to changing demands.In this paper, we present three SoC architecture designs for speeding-up inference tasks based on machine learned ensembles of decision trees.We focus on QuickScorer, the state-of-the-art algorithm for the efficient traversal of tree ensembles and present the issues and the advantages related to its deployment on two SoC devices with different capacities.The results of the experiments conducted using publicly available datasets show that the solution proposed is very efficient and scalable.More importantly, it provides almost constant inference times, independently of the number of trees in the model and the number of instances to score.This allows the SoC solution deployed to be fine tuned on the basis of the accuracy and latency constraints of the application scenario considered.
Automatic Speech Recognition: A Comprehensive Survey

Abstract Speech recognition is an interdisciplinary subfield of natural language processing (NLP) that facilitates the recognition and translation of spoken language into text by machine. Speech recognition plays an important role in digital transformation. It is widely used in different areas such as education, industry, and healthcare and has recently been used in many Internet of Things and Machine Learning applications. The process of speech recognition is one of the most difficult processes in computer science. Despite numerous searches in this domain, an optimal method for speech recognition has not yet been found. This is due to the fact that there are many attributes that characterize natural languages and every language has its particular highlights. The aim of this research is to provide a comprehensive understanding of the various techniques within the domain of Speech Recognition through a systematic literature review of the existing work. We will introduce the most significant and relevant techniques that may provide some directions in the future research.
Public Key Encryption With Equality Test for Industrial Internet of Things Based on Near-Ring

Organizations have moved from the conventional industries to smart industries by embracing the approach of industrial internet of things (IIoT), which has provided an avenue for the integration of smart devices and communication technologies. In this context, this work presents a public key encryption with equality test based on DLP with decomposition problems over near-ring. The proposed method is highly secure, and it solves the problem of quantum algorithm attacks in industrial internet of thing systems. Further, the proposed system is highly secure, and it prevents the chosen-ciphertext attack in type-I adversary and it is indistinguishable against the random oracle model for the type-II adversary. The proposed scheme is highly secure, and the security analysis measures are comparatively stronger than existing techniques.
Security enhanced cascaded phase encoding based on a 3D phase retrieval algorithm

In this paper, we propose a security enhanced cascaded phase encoding scheme based on a 3D phase retrieval algorithm. Under the constraints of binary amplitude masks (BAMs), a 2D plaintext is converted into a series of sparse images to form 3D distributed patterns at different axis positions, before being encoded into two phase-only masks to serve as the ciphertexts in the proposed 3D phase retrieval algorithm. As a result, the proposed method not only removes the explicit relationship between the plaintext and the ciphertexts, but also enhances the sensitivity of keys and the level of security. For decryption, a series of intensity patterns are captured at different axial positions, and then multiplexed into a single decrypted image with the aid of BAMs. Numerical simulations confirm the feasibility and effectiveness of the proposed method.