Reviewerly/exHarmony huggingface dataset:
Voting Based Ensemble Classification for Software Defect Prediction

Fault Prediction procedures are meant to help focus on software testing and troubleshooting; they can caution developers on programming segments that appear to be defective. Here, a Voting Based Ensemble Classification is proposed in which we apply feature selection on a preprocessed data utilizing two approaches, which include a Wrapper-based and Heuristic-based approach using python and WEKA respectively. After which, we train a classifier built on the selected attributes using the Voting Based Ensemble Learning Algorithm where predictions from multiple models are combined. We use three base learners which include the Adaboost classifier, Random Forest classifier, and Naive Bayes classifier. This paper embraces NASA datasets to check the exhibition of this design. The paper demonstrates that preprocessing technique using the Wrapper-based approach outperforms preprocessing done using the Heuristic-based approach. It also proved that the new Voting Based Ensemble Learning Algorithm consisting of Random Forests (RF), Adaboost, and Naive Bayes is a better approach than other algorithms for Software Defect Prediction that currently exist.
Convolutionally evaluated gradient first search path planning algorithm without prior global maps

Most of the existing path planning algorithms require a prior global map. Although there have been some algorithms proposed for unknown environments, they can only deal with those which just have several hidden obstacles in a roughly known global map. In order to improve the efficiency of robot's path planning without prior global maps, this paper proposes a Convolutionally Evaluated Gradient First Search (CE-GFS) path planning algorithm. It allows the robot to collect environmental information and complete path planning simultaneously. Firstly, the Gradient First Search (GFS) algorithm is proposed based on the gradient score parameter, with which the conventional cost function is replaced. The GFS can adapt to any moving direction through the environmental information surrounding the mobile robot and computing the gradient score parameter. Secondly, CE-GFS path planning algorithm is proposed based on GFS and convolutional evaluation method. The CE-GFS helps the robots to evaluate the efficiency of the global path and get global perception ability, so that they are prevented from going astray, which can significantly improve the efficiency of path planning. Finally, several simulation tests and field tests have been carried out. The test results show that convolutional evaluation improves the efficiency of CE-GFS by 86.94% on average compared with GFS at the price of 0.18% decrease in path planning success rate. Moreover, the time cost of the proposed CE-GFS algorithm is 42.18% less than that of FAR-Planner in some special cases.
Federated Deep Learning for Collaborative Intrusion Detection in Heterogeneous Networks

In this paper, we propose Federated Deep Learning (FDL) for intrusion detection in heterogeneous networks. Local Deep Neural Network (DNN) models are used to learn the hierarchical representations of the private network traffic data in multiple edge nodes. A dedicated central server receives the parameters of the local DNN models from the edge nodes, and it aggregates them to produce an FDL model using the Fed+ fusion algorithm. Simulation results show that the FDL model achieved an accuracy of 99.27 ± 0.79%, a precision of 97.03 ± 4.22%, a recall of 98.06 ± 1.72%, an F1 score of 97.50 ± 2.55%, and a False Positive Rate (FPR) of 2.40 ± 2.47%. The classification performance and the generalisation ability of the FDL model are better than those of the local DNN models. The Fed+ algorithm outperformed two state-of-the-art fusion algorithms, namely federated averaging (FedAvg) and Coordinate Median (CM). Therefore, the DNN-Fed+ model is preferable for intrusion detection in heterogeneous wireless networks.
Improved neural machine translation for low-resource English–Assamese pair

Language translation is essential to bring the world closer and plays a significant part in building a community among people of different linguistic backgrounds. Machine translation dramatically helps in removing the language barrier and allows easier communication among linguistically diverse communities. Due to the unavailability of resources, major languages of the world are accounted as low-resource languages. This leads to a challenging task of automating translation among various such languages to benefit indigenous speakers. This article investigates neural machine translation for the English–Assamese resource-poor language pair by tackling insufficient data and out-of-vocabulary problems. We have also proposed an approach of data augmentation-based NMT, which exploits synthetic parallel data and shows significantly improved translation accuracy for English-to-Assamese and Assamese-to-English translation and obtained state-of-the-art results.
Emotional talk about robotic technologies on Reddit: Sentiment analysis of life domains, motives, and temporal themes

This study grounded on computational social sciences and social psychology investigated sentiment and life domains, motivational, and temporal themes in social media discussions about robotic technologies. We retrieved text comments from the Reddit social media platform in March 2019 based on the following six robotic technology concepts: robot ( N = 3,433,554), AI ( N = 2,821,614), automation ( N = 879,092), bot ( N = 21,559,939), intelligent agent ( N = 15,119), and software agent ( N = 18,324). The comments were processed using VADER and LIWC text analysis tools and analyzed further with logistic regression models. Compared to the other four concepts, robot and AI were used less often in positive context. Comments addressing themes of leisure, money, and future were associated with positive and home, power, and past with negative comments. The results show how the context and terminology affect the emotionality in robotic technology conversations.
Efficient Interpolation-based Reversible Watermarking for Protecting Fingerprint Images

In this paper, an efficient interpolation-based reversible watermarking scheme for protecting fingerprint images is proposed. The interpolation approach, which generates additional pixels, is adopted to ensure perfect retrieval of the original image. The aim of this scheme is to guarantee both authenticity and integrity verification of fingerprint images, by embedding two multibit watermarks into interpolated pixels. A new robust watermarking technique is proposed to embed the authenticity watermark into the Lifting Wavelet Transform (LWT) coefficients, by using an adapted perceptual model. The insertion of the integrity watermark is achieved by adapting a semi-fragile technique, which is based on the Intermediate Significant Bit Substitution (ISBS) approach and operates in the spatial domain. The integrity watermark is generated from a block checksum procedure and serves for tamper detection and localization purposes. Experimental results showed that the proposed technique offers good performances in terms of imperceptibility and robustness, and is also capable of detecting and localizing tampers on watermarked images.
Multi-Level Two-Stream Fusion-Based Spatio-Temporal Attention Model for Violence Detection and Localization

Detection of violent human behavior is necessary for public safety and monitoring. However, it demands constant human observation and attention in human-based surveillance systems, which is a challenging task. Autonomous detection of violent human behavior is therefore essential for continuous uninterrupted video surveillance. In this paper, we propose a novel method for violence detection and localization in videos using the fusion of spatio-temporal features and attention model. The model consists of Fusion Convolutional Neural Network (Fusion-CNN), spatio-temporal attention modules and Bi-directional Convolutional LSTMs (BiConvLSTM). The Fusion-CNN learns both spatial and temporal features by combining multi-level inter-layer features from both RGB and Optical flow input frames. The spatial attention module is used to generate an importance mask to focus on the most important areas of the image frame. The temporal attention part, which is based on BiConvLSTM, identifies the most significant video frames which are related to violent activity. The proposed model can also localize and discriminate prominent regions in both spatial and temporal domains, given the weakly supervised training with only video-level classification labels. Experimental results evaluated on different publicly available benchmarking datasets show the superior performance of the proposed model in comparison with the existing methods. Our model achieves the improved accuracies (ACC) of 89.1%, 99.1% and 98.15% for RWF-2000, HockeyFight and Crowd-Violence datasets, respectively. For CCTV-FIGHTS dataset, we choose the mean average precision (mAp) performance metric and our model obtained 80.7% mAp.
Combinatorial double auction for resource allocation with differential privacy in edge computing

Application service providers deploy their services by purchasing certain edge computing resources from edge service providers. In this way, they can offer computing or storage support for users which promotes the formation of edge computing resource trading market. However, few research focus on the protection of the privacy of transaction participants. In this view of challenge, we propose a combinatorial double auction scheme based on differential privacy for multi-resource allocation in the trading market. In this scheme, our objective is to maximize the social welfare of the auction and apply the differential privacy based on Gaussian Mechanism to protect the security of the auction market. The simulation results show that the proposed scheme ensures the security and privacy of the auctioneers.
An improved dark channel defogging algorithm based on the HSI colour space

When the traditional dark channel prior (DCP) algorithm is used for image defogging, it faces some problems such as unsatisfactory restoration of the target edge details, distortion in large-scale and high-brightness sky regions. To solve these problems, this paper proposes an improved dark channel defogging algorithm based on the HSI colour space, named the IDCP algorithm. First, an asymmetric mean filter window is used to obtain the modified dark channel image to improve the grey value mutation in the traditional dark channel image. Based on this work, an improved global atmospheric transmittance calculation method is proposed, which effectively improves the inapplicability of the dark channel prior in large-scale and high-brightness sky regions. Finally, the Laplacian operator is used to sharpen the image to enhance the target edge information. Simulation results show that the proposed algorithm can effectively solve the distortion phenomenon caused by the traditional DCP algorithm when handling large-scale and high-brightness sky regions, improve the target edge details, and restore the true colour of the image scene.
The Effect of Context and Individual Differences in Human‐Generated Randomness

Many psychological studies have shown that human-generated sequences are hardly ever random in the strict mathematical sense. However, what remains an open question is the degree to which this (in)ability varies between people and is affected by contextual factors. Herein, we investigated this problem. In two studies, we used a modern, robust measure of randomness based on algorithmic information theory to assess human-generated series. In Study 1 ( N=183 ), in a factorial design with task description as a between-subjects variable, we tested the effects of context and mental fatigue on human-generated randomness. In Study 2 ( N=266 ), in online research, in experimental design, we further investigated the effect of mental fatigue on the randomness of human-generated series and the relationship between the need for cognition (NFC) and the ability to produce random-like series. Results of Study 1 show that the activation of the ability to produce random-like series depends on the relevance of the contextual cues ( χ2(2)=7.9828,p=.0192 ), whether they activate known representations of a random series generator and consequently help to avoid the production of trivial sequences. Our findings from both studies on the effect of mental fatigue (Study 1 - t(47,529.5568)=-18.62,p<.001 ; Study 2 - F(edf=3.587,Ref.df=3.587)=11.863,p<.0001 ) and cognitive motivation ( t(180)=2.66,p=.009 ) demonstrate that regardless of the context or task's novelty people quickly lose interest in the random series generation. Therefore, their performance decreases over time. However, people high in the NFC can maintain the cognitive motivation for a longer period and consequently on average generate more random series. In general, our results suggest that when contextual cues and intrinsic constraints are in optimal interaction people can temporarily escape the structured and trivial patterns and produce more random-like sequences.
An Adversarial Learning Approach for Super-Resolution Enhancement Based on AgCl@Ag Nanoparticles in Scanning Electron Microscopy Images

Scanning electron microscopy (SEM) plays a crucial role in the characterization of nanoparticles. Unfortunately, due to the limited resolution, existing imaging techniques are insufficient to display all detailed characteristics at the nanoscale. Hardware-oriented techniques are troubled with costs and material properties. Computational approaches often prefer blurry results or produce a less meaningful high-frequency noise. Therefore, we present a staged loss-driven neural networks model architecture to transform low-resolution SEM images into super-resolved ones. Our approach consists of two stages: first, residual channel attention network (RCAN) with mean absolute error (MAE) loss was used to get a better peak signal-to-noise ratio (PSNR). Then, discriminators with adversarial losses were activated to reconstruct high-frequency texture features. The quantitative and qualitative evaluation results indicate that compared with other advanced approaches, our model achieves satisfactory results. The experiment in AgCl@Ag for photocatalytic degradation confirms that our proposed method can bring realistic high-frequency structural detailed information rather than meaningless noise. With this approach, high-resolution SEM images can be acquired immediately without sample damage. Moreover, it provides an enhanced characterization method for further directing the preparation of nanoparticles.
NCGAN:A neural adversarial collaborative filtering for recommender system

The major challenge of recommendation system (RS) based on implict feedback is to accurately model users’ preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users’ preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users’ non-linear characteristics. At the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users’ ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators.
Super-resolution semantic segmentation with relation calibrating network

To achieve high-resolution segmentation results, typical semantic segmentation models often require high-resolution inputs. However, high-resolution inputs inevitably bring high cost on computation, which limits its application seriously in realistic scenarios. To address the problem, we propose to predict a high-resolution semantic segmentation result with a degraded low-resolution image as input, which is called super-resolution semantic segmentation in this paper. We further propose a Relation Calibrating Network (RCNet) for this task. Specifically, we propose two modules, namely Relation Upsampling Module (RUM) and Feature Calibrating Module (FCM). In RUM, the input feature map generates the relation map of pixels in low-resolution, which is then gradually upsampled to high-resolution. Meanwhile, FCM takes the input feature map and the relation map from RUM as inputs, gradually calibrating the feature. Finally, the last FCM outputs the high-resolution segmentation results. We conduct extensive experiments to verify the effectiveness of our method. Specially, we achieve a comparable segmentation result (from 70.01% to 70.90%) with only 1/4 of the computational cost (from 1107.57 to 255.72 GFLOPs) based on FCN on Cityscapes dataset.
Siamese Network-based Open Set Identification of Communications Emitters with Comprehensive Features

Radio frequency (RF) fingerprint-based identification schemes is of great significance in the domain of wireless devices, and we use them to complete device identification and authentication tasks. They extract the features caused by the inherent hardware circuits, which are very hard to forge. Recently, a large number of RF fingerprint identification methods based on deep learning have emerged. However, they make the close world assumption that the communications emitters appeared in the test data must have appeared in training. This assumption does not hold in open world classification. In this paper, we propose an open set identification method based on Siamese Network, dealing with the open set identification of communications emitters. Our method can extract more comprehensive signals features and has a better open set recognition effect. In addition, our method is more robust. In the case of increasing openness, it can still ensure sufficient discrimination between close set and open set classes.
Hyper-reoriented walking in minimal space

Abstract We present a new reorientation technique, “hyper-reoriented walking,” which greatly reduces the amount of physical space required in virtual reality (VR) applications asking participants to walk along a grid-like path (such as the most common layout in department stores). In hyper-reoriented walking, users walk along the gridlines with a virtual speed of twice the speed of real walking and perform turns at cross-points on the grid with half the speed of the rotation speed in the physical space. The impact of the technique on participants’ sense of orientation and increase in simulator sickness was investigated experimentally involving 19 participants walking in a labyrinth of infinite size that included straight corridors and 90° T-junctions at the end of the corridors. Walking accuracy was assessed by tracking the position of the head mounted display, and cyber-sickness was recorded with the simulator sickness questionnaire and with open questions. Walking straight forward was found to closely match the ideal path, which is the grid line, but slight errors occasionally occurred when participants turned at the T-junctions. A correction algorithm was therefore necessary to bring users back to the gridline. For VR experiments in a grid-like labyrinth with paths of 5 m in length, the technique reduces required size of the tracked physical walking area to 3 m × 2 m.
Automatic Segmentation of Immunohistochemical Images Based on U-net Architecture

In this paper, the application of convolutional neural networks for automatic segmentation of immunohistochemical images based on the U-net architecture is investigated. The quality of segmentation of immunohistochemical images plays a key role at the stage of recognition and classification, so the selection of segmentation algorithms is a time-consuming and important process. Recently, neural networks with U-net typology are increasingly used to implement image segmentation. The authors present an image processing algorithm, which includes the stage of pre-processing and automatic segmentation. The program implemented an application for automatic segmentation of immunohistochemical images, which allowed to improve the quality of segmentation based on the training sample. Comparative analysis of the neural network is formed on the basis of comparing the resulting image with the image processed by an expert. To select the best parameters of the neural network, a comparative analysis of the quality of segmentation in comparison with the reference image by the criterion of “number of iterations”.
Internet of Things-based Hydroponic: Literature Review

Abstract Hydroponics is a method of growing crops without using soil, with the benefits of controlling the environment and nutrients, conserving water, and reducing labor. The applied technology is used to improve results that have consistency. This research was conducted with the aim of knowing the IoT devices and platforms used in the development of IoT-based hydroponics. Data obtained from the website garuda.ristekbrin.go.id and www.sciencedirect.com from 2016 to 2021. This study is a literature review using the PRISMA method. This method is used for literature review using a systematic and structured basic framework. The results of this study indicate that the variables measured are Temperature, Potential Hydrogen (pH), Total Dissolve Solid, Water Temperature, Humidity, UV, Carbon Dioxide, Soil Moisture and Electrical Conductivity. The device used is the ESP8266, Arduino, and Raspberry PI with the MySQL, Thingspeak, Firebase, Domoticz, and Wyliodrin IoT Platforms.
Shaping Progressive Net of Reinforcement Learning for Policy Transfer with Human Evaluative Feedback

Deep reinforcement learning has achieved significant success in many fields, but will confront sampling efficiency and safety problems when applying to robot control in the real world. Sim-to-real transfer learning was proposed to make use of samples in the simulation and overcome the gap between simulation and real world. In this paper, we focus on improving Progressive Neural Network — an effective sim-to-real learning method, by proposing Interactive Progressive Network Learning (IPNL). IPNL integrates progressive network and interactive reinforcement learning (interactive RL) which learns from evaluative feedback provided by an observing human trainer. We test our method using five RL tasks with discrete or continuous actions in OpenAI Gym and a sinusoids curve following task with AUV simulator on the Gazebo platform. Our results suggest that while Progressive Network has good performance when transferring from tasks with low-dimensional state space to those with high-dimensional one but has little effect for transferring from high-dimensional tasks to low-dimensional ones, IPNL allows an agent to learn a more stable policy with better performance faster for both cases. More importantly, our further analysis indicate that there is a synergy between Progressive Network and interactive RL for improving the agent’s learning. Our results in the path following of AUV shed light on the potential of applying our method in the real world tasks.