Reviewerly/exHarmony huggingface dataset:
From modeling dose-response relationships to improved performance of decision-tree classifiers for predictive toxicology of nanomaterials

The development and application of predictive models towards toxicity of engineered nanomaterials is still far from being satisfactory. One promising contribution to confront this challenge is to effectively augment the performance of machine learning classifiers by progressing the approach towards balancing experimental toxicity data. We propose an improved balancing methodology by fitting the in-vitro toxicological dose-response datasets of engineered nanomaterials to three, four, and five, free parameter dose-response models. The four-free parameter model displays the best fit (in terms of adjusted R2) for most of the examined data. The fitted curve yields, in each case, a continuous sequence of data points, which extends the restricted experimental data and generates additional fitted data points for the minority class, leading to the formation of balanced data for predicting the nanoparticle’s toxicology by decision tree classifiers. The ability to best predict the experimental toxicity data, by applying the decision tree model, was tested by forming three versions of the same experimental data: the imbalanced raw experimental data, the balanced data by applying the common Synthetic Minority Oversampling Technique, and by using the approach of Balanced Fitted Dose-Response method, introduced in the present study. We demonstrate that our approach provides improved performance of decision trees in predicting nanoparticles’ toxicity, a method that pertains also to chemical toxicity, central in health and environmental research.
Multi-Sensor Fusion Based Off-Road Drivable Region Detection and Its ROS Implementation

There is a growing demand for multi-sensor fusion based off-road drivable region detection in the field of autonomous vehicles and robotics. This technology allows for improved navigation and localization in off-road environments, such as rough terrain, by combining data from multiple sensors. This can lead to more accurate and reliable detection of drivable regions, which is crucial for the safe operation of autonomous vehicles in off-road environments. In this work, a deep learning architecture is employed to identify drivable and obstacle regions on images. It learns to classify and cluster the regions simultaneously using semantic segmentation. Further, a LiDAR-based ground segmentation method is introduced to classify drivable regions more effectively. The ground segmentation method splits the regions into small bins and applies the ground fitting technique with adaptive likelihood estimation. Finally, a late fusion method is proposed to fuse both results better to classify the drivable region. The entire fusion architecture was implemented on ROS. On the RELLIS3D dataset, the semantic segmentation achieves a mean accuracy of 84.3%. Furthermore, it is observed that certain regions misclassified by the semantic segmentation are corrected by LiDAR-based ground segmentation and the fusion provides a better representation of the drivable region.
Hashing-based remote sensing image tamper detection system

Considering the large scale of remote sensing images in practical application scenarios, this paper proposes an image detection system by extracting feature hashing of remote sensing images. The feature hashing is generated from different perspectives such as spatial and frequency domain, local and global. For the edge features, an optimized Canny operator based on four pairs of threshold (OCO-4) is employed to calculate the spatial coordinates of rich edge blocks and low edge blocks. In the frequency domain, compression sensing (CS) is exploited to compress and sample Discrete Cosine Transform (DCT) blocks coefficients. For the global texture features, contrast, correlation, energy and homogeneity are extracted by gray-level co-occurrence matrix (GLCM). Furthermore, in order to enhance the security during cloud transmission, pseudo-random numbers are used as secret keys to construct the secure hashes. Consequently, the feature hashes are obtained in both the frequency domain and the space domain, which ensures the effectiveness and uniqueness of feature hashing. Simulation experiments are carried out on four different remote sensing data sets. The results show that the proposed detection system has high sensitivity and a desirable balance between robustness and discrimination, and the average detection accuracy can reach 99.96%.
BINO: Automatic recognition of inline binary functions from template classes

In this paper, we propose BINO, a static analysis approach that relieves reverse engineers from the challenging task of recognizing library functions that have been inlined. BINOrecognizes inline calls of methods of C++ template classes (even with unknown data types). We do this through a binary fingerprinting and matching approach. Our fingerprint model captures syntactic and semantic features of an assembly function, along with its Control-Flow Graph structure. Using these fingerprints and subgraph isomorphism, it recognizes inline method calls in a target binary. BINOautomates the fingerprints generation phase by parsing the source code of the template classes and automatically building appropriate binaries with representative inline calls of said methods. We evaluate BINOby performing experiments on a dataset of 555 GitHub C++ projects containing 10600 inline functions, exploring several optimization levels that allow the compiler to inline function calls. We show that our approach can recognize inline function calls to the most used methods of well-known template classes with an F1-Score up to 63% with the -O2, -O3, and -Ofast optimizations levels.
An adaptive method for prevention of overflow in reversible data hiding schemes

In reversible data hiding (RDH) schemes for images, the overflow/underflow problem must be considered to ensure reversibility. Existing techniques to avoid this problem seem to be sensitive to the working principle of a particular RDH scheme, and their performance may be adequate only when the number of problematic pixels, i.e., pixels at risk of overflow, is small or moderate compared to the total number of pixels in the image. However, when the number of problematic pixels is significantly large, the size of the recovery information needed to restore the original image increases significantly, and the performance of the RDH implementation could be affected. This paper presents an adaptive method for generating a flag array of recovery information to avoid the overflow problem. The results show that the proposed method is suitable for images with a high rate of problematic pixels since it produces much smaller recovery information streams than existing techniques. Additionally, the proposed technique allows exploiting problematic pixels during the embedding stage, increasing the effective capacity. Furthermore, the proposed method is tested with different RDH schemes and different pixel modification amplitudes, and it can preserve the structural quality of the processed images.
Question Answering System using Knowledge Graphs

A question answering system aims to answer the asked question with relevant responses thus sufficing the re-quested query asked in natural language by responding in the same language. Knowledge Graph Question Answering (KGQA) aims to answer questions asked by the user on a paragraph from a knowledge graph (KG). A strongly connected KG is essential in picking out answers for the requested question. This is because the KG is traversed to select the answer. A well connected KG thus provides a relevant answer. The knowledge graph is built by identifying the subject, the object and the relation for every sentence in the input text or knowledge base. Questions are processed to identify the source-relation-target triples which are then matched with that of the triples forming the KG. The challenge is in extracting the entities and relations between them to create the KG. The model's performance is directly proportional to the strength of the KG. Hence, the presence of a well connected KG provides great accuracy while a poorly connected one would break the system. The proposed model is tested on a Multi RC dataset. Multi RC is a dataset for multi hop question answering that includes short paragraphs and multi-sentence questions. This allows catering to both single hop and multi hop questions. The primary objective was to build a question answering system with the ability to answer multi hop questions together with an efficient response time through the usage of knowledge graphs. A novel approach has been employed where natural language questions are processed into key-value pairs, by leveraging python modules whose dependencies aid in parts of speech tagging in the English language thereby mapping back to the data entities present in the KG to retrieve the correct answer.
Integral Factor Neural Computing System Optimization of Lagrange System based on Fractional Order Model

When using neural networks to solve practical problems, there will be different requirements for different analytic objectives, so various stability problems such as the asymptotic stability, Lagrange stability, multi-stability, and input-to-state stability have emerged. In this study, Lagrange stability is considered to discuss the integral factor neural computing system optimization of Lagrange system based on the fractional order model. Based on the Lyapunov stability theory, aiming at the problem of nonlinear input limitation in practice, taking the associated Lagrange system as the research object, the optimized neural network architecture is studied and tested the stability of the model in the prediction problem. After the testing, the prediction is tested on data set of 50, and error is acceptable.
Automated Method for Calibration Findings of Auto Slide Scanner Using Automated Digital Microscopy

Applications in the automated microscopy require careful calibration of the microscope system. In one such scenario where automated microscope is made to work with auto slide scanner, accurate measurement of the calibration parameters becomes an essential procedure. Here, we propose an automated method for finding the calibration parameters of a microscope based auto slide scanner. Microscopic images of a grid micrometer slide are analyzed for finding calibration parameters. Apart from the automated microscope and auto slide scanner, the procedure requires a digital camera and a grid micrometer slide. This calibration finding embraces the following parameters of camera and slide scanner: (a) measuring the pixel size in micrometers, (b) measuring the camera orientation and (c) measuring the linearity and uniformity of the slide scanner. The proposed method is accurate, reproducible and scalable enough to be suitable for applications requiring in-situ calibration.
Word Segmentation and Sandhi Resolution on Ayurveda Classical Scriptures

Sanskrit is one of the oldest of the Indo-Aryan languages which has an abundant amount of texts in literature. One of the main sources of these Sanskrit texts is the textbooks written about the field of Ayurveda, the ancient Indian medical system. However, proper computational systems for processing this language are bare. A very challenging part of processing the Sanskrit language is the task of Sandhi resolution, which means the splitting of phonetically merged words. This study mainly focuses on this complex task of joint compound splitting and Sandhi resolution in Sanskrit specific to Ayurveda texts. This paper also surveys and compares all the existing research on Sandhi resolution in Sanskrit. In this work, an Ayurvedic dataset is compiled from some popular Ayurvedic texts, and a performance analysis of the state-of-the-art word segmentation model is carried out using this dataset.
An Attribute Contribution-Based K-Nearest Neighbor Classifier

K-nearest neighbor algorithm (KNN) is one of the most representative methods in data mining classification techniques. However, the KNN algorithm has a problem that when the traditional Euclidean distance formula is used to calculate the nearest neighbor distance, we ignore the relationship between attributes in the feature space. To tackle this issue, a covariance matrix is used to calculate the attribute contribution of the samples in order to solve the above problem. So an attribute contribution-based k-nearest neighbor classifier (ACWKNN) is proposed in this paper. The proposed algorithm is compared and experimented on the UCI standard dataset, and the results show that the method outperforms other KNN algorithms.
A Virtual Try-on Model with Enhanced Feature Representation Capability

When consumers choose to buy clothing online, virtual try-on technology can provide them with a better shopping experience. The optimization of virtual try-on technology not only helps consumers to evaluate the selected clothing, but also can improve the profit for merchants. However, the traditional virtual try-on technology has problems such as high cost, image distortion, and deviation of clothing style. In order to solve the above problems, this paper proposes a virtual try-on model with enhanced feature representation capability. Through the improved residual block of Squeeze-and-Excitation Networks (SENet) and the style encoding module introduced by the Pyramid Squeeze Attention (PSA) module, our model enriches the content and style information, strengthens the representation ability of features, and the reconstructed image preserves the more details. Compared with related work, we improve the structural similarity measure by 1.1% and the Inception Score by 10.1%. It is demonstrated that our model can reconstruct more accurate and realistic images.
Improved YoloV5 for the Authenticity Identification of Silver Coins in Modern China

Silver coin is an important circulating currency in modern China, and the edge teeth of silver coins are the key factor to identify its authenticity. However it is difficult for some hobbyists to distinguish the authenticity. So we propose an improved yoloV5 neural network algorithm, which can distinguish the authenticity of silver coin through its edge tooth images, and the value of mAP is more than 0.8. The algorithm in this paper adopts the Self-Attention mechanism, which can make full use of the correlation between image pixels and fully focus on the key details in the image, so that the network model can capture the global features of the image when learning a few parameters. Compared with yoloV5, the improved network model in this paper performs better on the public data set. No matter the value of mAP, FLOPs or average processing speed all have improved significantly. In addition, this paper also constructs a set of silver coin edge tooth images data set to facilitate relevant research in the future.
Lightweight image matting algorithm based on deep learning

Abstract To solve the problem that the deep learning‐based image matting algorithm cannot balance accuracy and model size, a lightweight image matting algorithm based on deep learning is proposed. Considering the limitation of memory and computing resources, and aiming at lightweight. We construct a network and gradually improved it. Firstly, apply deep detachable convolution to deep image matting networks to form faster and stronger encoder and decoder networks. The simultaneous use of depth‐separable convolution can also reduce the number of corresponding model parameters and computation. And then attention mechanism is integrated into the model and SE Block was used to assign different weights to feature channels to improve the accuracy of the model. Finally, knowledge distillation scheme is designed in part of the encoder‐decoder structure, the corresponding loss function is proposed, and the method of knowledge distillation is used to improve the feature learning ability of the lightweight neural network. Compared with the original deep image matting model, the number of parameters in the new model is greatly reduced without too much loss of accuracy.
Separation of productivity index zones using fractal models to identify promising areas of fractured reservoir rocks

Abstract Identifying geological characteristics such as rock types and fractures is an important step in fractured reservoirs’ modeling and developing oil and gas fields. The productivity index (PI) is an essential parameter for this purpose. There are different methods for separating and identifying rock types and fractures, including simple statistical methods and complex fractal methods based on the spatial structure of the data. In this study, rock areas were isolated after modeling the PI parameter in a rock reservoir in southern Iran by ordinary kriging estimation. Then, the fractal concentration–area (C–A) and concentration–number (C–N) methods were used to classify the PI zones. The C–A fractal analysis revealed six different rock types and zones, and the C–N fractal method indicated four anomalies based on PI data in the studied reservoir rock. Based on the C–N and C–A models, the parts with PI ≤ 44 and PI ≤ 63, respectively, correspond to the production of wells from the reservoir rock matrix in this oil field and PI ≥ 223 include the production of wells at the fracture network of the reservoir rock. Fractal modeling indicates that the highest PI values occurred in the southeast and northwest parts of the studied oil field, suggesting better reservoir rock quality in this area. This problem is attributed to the presence of faults and the accumulation of fractures in these areas, which increases reservoir rock’s PI and permeability. The present study showed that multifractal methods are a very accurate method for separating all types of rock types in the reservoir and it separates things that are not visible in other methods such as petrophysical methods. The anomalies and communities identified for the PI parameter with these methods are well confirmed by geological evidence, especially the impact of fractures, faults and other diagenesis factors in the reservoir rock.
Seg-CURL: Segmented Contrastive Unsupervised Reinforcement Learning for Sim-to-Real in Visual Robotic Manipulation

Training image-based reinforcement learning (RL) agents are sample-inefficient, limiting their effectiveness in real-world manipulation tasks. Sim2Real, which involves training in simulations and transferring to the real world, effectively reduces the dependence on real data. However, the performance of the transferred agent degrades due to the visual difference between the two environments. This research presents a low-cost segmentation-driven unsupervised RL framework (Seg-CURL) to solve the Sim2Real problem. We transform the input RGB views to the proposed semantic segmentation-based canonical domain. Our method incorporates two levels of Sim2Real: task-level Sim2Real, which transfers the RL agent to the real world, and observation-level Sim2Real, which transfers the simulated U-nets to segment real-world scenes. Specifically, we first train contrastive unsupervised RL(CURL) with segmented images in the simulation environment. Next, we employ two U-Nets to segment robotic hand-view and side-view images during real robot control. These U-Net are pre-trained with synthetic RGB and segmentation masks in the simulation environment and fine-tuned with only 20 real images. We evaluate the robustness of the proposed framework in both simulation and real environments. Seg-CURL is robust to the texture, lighting, shadow, and camera position gap. Finally, our algorithm is tested on a real Baxter robot with a dark hand-view in the cube lifting task with a success rate of 16/20 in zero-shot transfer.
Quality Analysis and Improvement of 3D Panoramic Stitching Sequence Image

Using a computer to splice images with overlapping parts is the current mainstream method for 3D panorama construction. However, the quality of the spliced sequence images cannot be guaranteed due to the uneven quality of the resulting panorama. Aiming at quality problems such as inconsistent brightness and contrast of stitched sequence images and easy noise generation during the shooting process, a batch quality improvement process for multiple sequence images required for 3D panorama construction is proposed, which is different from single image processing; firstly, the median value is used The filtering method is used to denoise multiple sequential images, secondly to make the gray range of multiple images consistent, and finally to improve the contrast of the sequential images by using histogram equalization. Experimental results show that this method has a good effect on improving the quality of 3D panorama stitching sequence images.
History of the CAL Timesharing System

The CAL Timesharing System (CAL TSS) was developed at the University of California at Berkeley between 1968 and 1971 to provide interactive computing for research and instruction.It ran on a mainframe computer, the Control Data Corporation 6400, and was one of the earliest systems to use capabilities for protection.Using our memories, archival records, and files preserved by McJones that are part of a donation to the Computer History Museum, we discuss the origin of the project, development of the software, and the brief experience using the system before it was shut down in May 1972.
Adaptive Neural Network (NN) Coordinated Tracking Control Based on Artificial Intelligence Algorithm

The research of NN adaptive algorithm and detail factors has carried out a lot of academic exploration in the field of control. The known linear or partially linear system models can be solved by the algorithm of control theory. However, in the actual process, there must be external interference and the system has unknown parameters, which should be considered in the design of control strategy. In this paper, the adaptive NN coordinated tracking control based on artificial intelligence algorithm is studied, and the NN control, adaptive control and NN adaptive algorithm are discussed and analyzed; Taking the variable speed fan as the research object, the power tracking problem of the unit below the rated wind speed is studied, and a high-precision and fast nonlinear power tracking control is designed. A NN adaptive proportional tracking control method is designed to solve the power tracking control problem of the system with model uncertainty, external interference and unknown ideal power trajectory, and the model construction of unknown ideal power trajectory is proposed; Through control simulation and analysis, the wind farm output power tracking curve is tested. The results show that the proposed control method before and after optimization can achieve good trajectory tracking effect, make the NN play a better learning and estimation ability, better tracking effect and less error.
Confidence Calibration of Hand Gesture Recognition Networks Based on Ensemble Learning

Confidence calibration refers to the degree of matching between predicted confidence and actual correctness probability. The ensemble of neural networks, also known as the ensemble of models, is a common method for estimating predictive uncertainty. However, this method requires a lot of modifications to the training procedure, and the computational cost is also expensive compared with other existing methods. We propose an approximate ensemble method that is simple to implement and results in well-calibrated confidence estimates without introducing various sets of models. More specifically, the historical model weights in the training phase are selected to simulate a new set of model parameters for uncertainty estimation. We show that our proposed method can produce high-quality predictive uncertainty estimates comparable to standard ensemble methods through a series of experiments on the Jester benchmark dataset.