Reviewerly/exHarmony huggingface dataset:
Obstacle Avoidance Path Planning of 7-DOF Redundant Manipulator Based on Improved Ant Colony Optimization

Background: Obstacle avoidance path planning is an important parameter of robot manipulators. Path planning can directly affect the working efficiency of the manipulator. Objective: This study aims to summarize the optimization design method from a large number of literature and propose a new optimization design method to make the planned obstacle avoidance path of the manipulator shorter and smoother. Methods: The forward and inverse kinematics of the redundant manipulator is solved. Secondly, the obstacle and the robot manipulator envelope are simplified for collision detection. Then, the ant colony algorithm is improved by adding an obstacle environment to the construction of the heuristic function and dynamically adjusting the heuristic function factor to make the shortest path distance planned by the algorithm. Finally, the worst ant colony is added to the pheromone update to avoid the algorithm falling into the local optimal solution. Results: Through experiments and comparative studies, the optimized design process shows that the path planned by the improved ant colony algorithm has obvious advantages of shorter path distance and smoother path distance, which verifies the rationality of the improved algorithm. Conclusion: This method optimizes the convergence speed of the ant colony algorithm and avoids the ant colony algorithm from falling into the local optimal solution, which is of great significance for improving the obstacle avoidance path planning problem of a redundant manipulator with a degree of freedom.
Evaluation of Sample Preparation Importance for Digital Image Correlation during Cold Deformation

In this paper, tests were performed by monitoring changes of strain amounts and their distribution by digital image correlation during static tensile testing of steel. The influence of different combination of speckles application on the sample surface (white speckles on the black sample surface and black speckles on the white sample surface) during cold deformation of steel was tested with the non-contacting digital image correlation. Strain amounts obtained by digital image correlation analysis of tested samples, with white on black and black on white painted speckle patterns, were compared. Different analysis for determining and monitoring strain amounts, provided by digital image correlation software, were used in this paper. The strain amounts were compared during cold deformation until the occurrence of sample fracture in both combinations of speckles application on the sample surface. Research has shown the importance of proper sample preparation for determination strain amounts and insignificant minor differences of strain amounts, between different sample preparations for digital image correlation, during cold deformation of tested steel.
A Compound Class of Inverse-Power Muth and Power Series Distributions

This paper introduces the inverse-power Muth power series model, which is a composition of the inverse-power Muth and the class of power series distributions. The use of the Bell distribution in this context is emphasized for the first time in the literature. Probability density, survival and hazard functions are studied, as well as their moments. Using the stochastic representation of the model, the maximum-likelihood estimators are implemented by the use of the expectation-maximization algorithm, while standard errors are calculated using Oakes’ method. Monte Carlo simulation studies are conducted to show the performance of the maximum-likelihood estimators in finite samples. Two applications to real datasets are shown, where our proposal is compared with some models based on power series compositions.
Image Inpainting with Parallel Decoding Structure for Future Internet

Image inpainting benefits much from the future Internet, but the memory and computational cost in encoding image features in deep learning methods poses great challenges to this field. In this paper, we propose a parallel decoding structure based on GANs for image inpainting, which comprises a single encoding network and a parallel decoding network. By adding a diet parallel extended-decoder path for semantic inpainting (Diet-PEPSI) unit to the encoder network, we can employ a new rate-adaptive dilated convolutional layer to share the weights to dynamically generate feature maps by the given dilation rate, which can effectively decrease the number of convolutional layer parameters. For the decoding network composed of rough paths and inpainting paths, we propose the use of an improved CAM for reconstruction in the decoder that results in a smooth transition at the border of defective areas. For the discriminator, we substitute the local discriminator with a region ensemble discriminator, which can attack the restraint of only the recovering square, like areas for traditional methods with the robust training of a new loss function. The experiments on CelebA and CelebA-HQ verify the significance of the proposed method regarding both resource overhead and recovery performance.
Dual-Branch Person Re-Identification Algorithm Based on Multi-Feature Representation

Person re-identification can identify specific pedestrians across cameras and solve the visual limitations of a single fixed camera scene. It achieves trajectory analysis of target pedestrians, facilitating case analysis by public security personnel. Person re-identification has become a challenging problem due to occlusion, blur, posture change, etc. The key to affecting the accuracy is whether sufficient information of pedestrians can be obtained. Most existing deep learning methods only consider a single category of characteristics, resulting in problems of information loss and single feature expression. This paper proposed a dual-branch person re-identification algorithm based on multi-scale and multi-granularity branches to obtain complete pedestrian information, called MSMG-Net. The multi-scale branch extracted features from different layers and fused them through the bidirectional cross-pyramid structure. It made up for the loss of information caused by using only high-level semantic features. The multi-granularity branch combined the convolutional neural network (CNN) and Transformer module based on improved self-attention mechanisms to obtain global information, and it performed horizontal segmentation of feature maps to obtain local information. It combined global and local features and solved the problem of single feature expression. The experimental results show that mAP/Rank-1 of MSMG-Net reached 88.6%/96.3% on the Market-1501 dataset and 80.1%/89.9% on the DukeMTMC-ReID dataset. Compared with many state-of-the-art methods, the performance of the proposed algorithm was improved significantly.
Automatic parameter learning method for agent activation spreading network by evolutionary computation

Abstract A variety of planning research is being actively conducted in multiple research fields. The focus of these studies is to flexibly utilize both immediate and deliberative planning in response to the environment and to adaptively prioritize multiple goals and actions in a human-like manner. To achieve this, a method that applies active propagation to multi-agent planning (agent activation spreading network) has been proposed and is being utilized in various research fields. Furthermore, with the recent development of large-scale artificial intelligence models, we should soon be able to incorporate tacit human knowledge into this architecture. However, there is not yet a method for adjusting the parameters in this architecture which creates a barrier to future extension. In response, we have developed a method for automatically adjusting the parameters using evolutionary computation. Our experimental results showed that (1) the proposed method enables a higher degree of adaptation, thanks to taking the agent’s semantics into account, and (2) it is possible to obtain parameters that are appropriate to the environment even when the experimental environment is changed.
Secure Multi-Party Computation for Magnetic Resonance Imaging Classification

Accessing private data always requires a complex negotiation process. This process becomes even more challenging under privacy regulations such as the General Data Protection Regulation and the California Consumer Privacy Act. However, in most cases, the availability of greater amounts of data leads to significant technological breakthroughs. The perfect example is the ImageNet classification challenge, which leads to significant improvements in the image recognition area. The combination of these concerns raises the question of performing computations on data that cannot be seen, and a whole new research field that consolidates privacy-preserving concepts and modern data mining techniques comes into place. This is also the purpose of the work presented in this paper, the discovery of Secure Multi-Party Computation (SMPC) capabilities on protecting privacy during the machine learning process. The main attention is paid towards the combination of SMPC and image classification approach based on the convolutional neural network, especially the secure inference process on the encrypted magnetic resonance images.
Employing Latent Categories of Entities for Knowledge Graph Embeddings With Contrastive Learning

Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for robotics to learn and reason about semantic knowledge. It is crucial for knowledge graph embedding models to infer various relation patterns, such as symmetry/antisymmetry. However, most existing approaches ignore the latent semantic categories information of entities. For example, when robots use a faucet with relation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OperatesOn</i> , its tail entity category should be washable objects, such as apple, bowl, rather than non-washable objects, such as mobile phone, computer. To address such issues, we propose a novel and simple framework for knowledge graph embedding, which utilizes contrastive learning to cluster entities based on the constraints arising from relations, and enhances the discriminative ability for entities with the latent same category. Experimental results demonstrate the effectiveness of our proposed method on four standard knowledge graph completion benchmarks. It is noteworthy that our method can yield some new state-of-the-art results, achieving 88.2% MRR, 84.9% Hits@1 on the AI2Thor dataset, and 39.8% MRR, 30.0% Hits@1 on the FB15k-237 dataset.
Range parameterised maximum correntropy unscented Kalman filter for two dimensional angles-only target tracking problems

This paper address the angles-only target tracking (AoT) problem in two dimension where the focus is on mitigating detrimental effects of the initial range uncertainty and noise uncertainties in measurements, on estimation accuracy. To efficiently handle these uncertainties and to generate more accurate and robust state estimates, maximum correntropy unscented Kalman filter (MC-UKF) is integrated with the range parametrisation approach. As AoT problem is more challenging among the target tracking problems, the presence of non Gaussian noise in measurements combined with the initial uncertainty in range, makes it even more difficult to solve with the help of conventional estimators like the UKF. Hence we develop the range parametrised maximum correntropy unscented Kalman filter (RP-MC-UKF) and the estimation accuracy is compared with the UKF and its range parametrised version (RP-UKF). The non Gaussian noise in measurement is modelled as glint noise plus shot noise. The estimation accuracy was evaluated based on the root mean square error (RMSE) in position and the % of track-loss. It was observed that the developed RP-MC-UKF performed with significant improvement in estimation accuracy in the presence of range uncertainty as well as glint plus shot noise in angular measurements.
A Survey of GAN Based Image Synthesis

Image generation is a hot topic in the academic recently, and has been applied to AI drawing, which can bring Vivid AI paintings without labor costs. In image generation, we represent the image as a random vector, assuming that the images of the natural scene obey an unknown distribution, we hope to estimate its distribution through some observation samples. Especially, with the development of GAN (Generative Adversarial Network), The generator and discriminator improve the model capability through adversarial, the quality of the generated image is also increasing. The image quality generated by the existing GAN based image generation model is so well-paint that it can be passed for genuine one. Based on the brief introduction of the concept of GAN, this paper analyzes the main ideas of image synthesis, studies the representative SOTA GAN based Image synthesis method.
IoT in Smart Irrigation and Node MCU ESP 8266 Wi-Fi Module

The term “Internet of Things,” or “IoT,” refers to the collection of physical objects that are linked to one another over the internet. To wirelessly connect sensors to various items so they can communicate without human beings. India ranks second among nations with irrigation systems. Water effort is extremely important for irrigation because of erratic rainfall and water shortage issues. Crop output has been impacted by climate change and groundwater depletion. We can conserve resources and money with the use of IoT and sensors. The amount of spruce irrigation's functional IoT and sensors is examined in this investigation and described. Different parameters, including as soil moisture, soil temperature, and humidity, have been taken into account when PH was created using an IOT platform and a wireless communication system. The data is sent using a microcontroller.
An Overview of Double JPEG Compression Detection and Anti-detection

JPEG (Joint Image Experts Group) is currently the most widely used image format on the Internet. Existing cases show that many tampering operations occur on JPEG images. The basic process of the operation is that the JPEG file is first decompressed, modified in the null field, and then the tampered image is compressed and saved in JPEG format, so that the tampered image may be compressed several times. Therefore, the double compression detection of JPEG images can be an important part for determining whether an image has been tampered with, and the study of double JPEG compression anti-detection can further advance the progress of detection work. In this paper, we mainly review the literature in the field of double JPEG compression detection in recent years with two aspects, namely, the quantization table remains unchanged and the quantization table is inconsistent in the double JPEG compression process, Also, we will introduce some representative methods of double JPEG anti-detection in recent years. Finally, we analyze the problems existing in the field of double JPEG compression and give an outlook on the future development direction.
A Survey of Privacy Preservation for Deep Learning Applications

Deep learning is widely used in artificial intelligence fields such as computer vision, natural language recognition, and intelligent robots. With the development of deep learning, people’s expectations for this technology are increasing daily. Enterprises and individuals usually need a lot of computing power to support the practical work of deep learning technology. Many cloud service providers provide and deploy cloud computing environments. However, there are severe risks of privacy leakage when transferring data to cloud service providers and using data for model training, which makes users unable to use deep learning technology in cloud computing environments confidently. This paper mainly reviews the privacy leakage problems that exist when using deep learning, then introduces deep learning algorithms that support privacy protection, compares and looks forward to these algorithms, and summarizes this aspect’s development.
Deep Network-based Slow Feature Analysis for Human Fall Detection

One of the most concerning safety hazards for elderly people is abnormal falls in public places. Vision-based fall detection using ambient cameras is a popular non-intrusive solution. Recent research uses Slow Feature Analysis (SFA), which can learn the slow invariant varying shape features obtained from input signals and is efficient. Another recent famous approach in motion detection is deep learning. However, the fall event in actual cases is diverse, resulting in complications in the detection task. Additionally, it is difficult to acquire fall-related data; hence, simulation is done on fall events to generate a training dataset, resulting in smaller data. Considering these complications, we have presented a novel method by combining SFA, deep learning models, namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), and rule-base. CNN is used to extract the object region, thereby reducing the region of interest (RoI). Two shape features, such as aspect ratio and area of RoI are considered as input to the LSTM for retrieving the temporal information which is further used for rule generation, thereby increasing the detection accuracy. The efficacy of the proposed method for various features, such as aspect ratio, area, and aspect r <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> tio+area is demonstrated over the UR Fall data with an accuracy of 95.2%, 93.8%, and 96.36%, respectively.
An Overview of Image Tamper Detection

With the popularization of high-performance electronic imaging equipment and the wide application of digital image editing software, the threshold of digital image editing becomes lower and lower. This makes it easy to trick the human visual system with professionally altered images. These tampered images have brought serious threats to many fields, including personal privacy, news communication, judicial evidence collection, information security and so on. Therefore, the security and reliability of digital information has been increasingly concerned by the international community. In this paper, digital image tamper detection methods are classified according to the clues that they rely on, detection methods based on image content and detection methods based on double JPEG compression traces. This paper analyzes and discusses the important algorithms in several classification methods, and summarizes the problems existing in various methods. Finally, this paper predicts the future development trend of tamper detection.
Real Time Object Detection With Drone Using Deep Learning Algorithm

In that work, a type of an unmanned aerial vehicle (UAV) called quadrotor with an object detection system is the subject that we will talk about. The main objectives of our work is the detection of chosen objects from the quadrotor. For that, there are two parts presented, the first one, a detailed description of the mathematical effects that applied to the structure of our system and how we implemented our system with showing all the parts, and we used the PID controller to stabilize the system. In the second part, we will talk about artificial intelligence generally and deep learning specifically and we will show how exactly the detection happen using the right technics and algorithms with Yolov5 and which version we will use. Finally we will make a real test with the real time showing our prototype working in the field.
Biometric Technologies and Ethics

This chapter starts off by discussing. BiometricBiometrics technologies as accessAccess and identification processes. Biometric technologies confirm a person’s identity by scanning physical characteristics such as a fingerprintFingerprint, voice, eye movement, and facial recognition. We discuss how a typical biometric system operates in two distinct stages: the enrollmentEnrollment stage and the authenticationAuthentication stage. We also discuss biometric technologies traits used to confirm a person’s identity that include fingerprint, voice, eye movement, facial recognition, and a few others. Finally, in discussing the ethical issuesEthicalissues faced in the use of biometricBiometrics technologies, we pose the following questions to the reader: The answers to these questions inform our discussion.
Computer Crime Investigations and Ethics

This chapter discusses what constitutes digital evidenceDigitalevidence, the collection and analysis of digital evidenceDigitalevidence, the chain-of-custodyChain-of-custody, the writing of the report, and the possible appearance in court as an expert witness. We give an in-depth discussion of the digital evidence acquisition rule of thumb and the candidates for evidence extraction. On preserving evidence, a cornerstone of crime investigation, we note that since digital evidence is very fluid, in that it can disappear or change so fast, extra care must be taken in preserving digital evidence. We discuss the various techniques to preserve evidence and what needs to be done if the evidence is to be moved. We stressStress the importance of careful analysis of digital evidence noting that this process is the most difficult and most opinionated. It is also the most important, most time-consuming, and painstakingly slow and should be thorough so that it can support or reject a fact based on identified patterns of activities, file signature anomalies, unusual behaviors, file transfers, and several other trends in the evidence. The final issues discussed in this chapter include the process of report writing and presentation and the ethical implicationsEthicalimplications and responsibilities of both the investigator and the lawyer. The role of the investigator, as the main player in the process, is discussed in depth considering the absence of a legal and ethical framework for digital forensicsForensics investigators to work under. Specific guidelines like the United Kingdom Association of Chief Police Officers (ACPO)’s “Good Practice Guide for Computer-Based Electronic Evidence” and the US National Institute of Standards and Technology (NIST) 800 series special publications are suggested.
Research on Global Collision Avoidance Algorithm for Unmanned Ship Based on Improved Artificial Potential Field Algorithm

Collision avoidance technology is very important in the research of unmanned ship path planning. Aiming at the problem that the existing global path planning algorithm of unmanned vehicle is easy to fall into the local optimal solution and the target point is unreachable, a path planning algorithm based on improved artificial potential field was designed. According to the route requirement of USVS, the power function of target distance change is introduced to improve the repulsive field model. The dangerous target points are screened by using the target point judgment method based on the safe arrival radius. Finally, by dynamically adjusting the artificial potential field coefficient, the unmanned vehicle can jump out of the local optimal trap. Based on the theoretical research, the simulation experiment of path planning is designed. The simulation results show that the algorithm can jump out of the local minimum point trap, and the path planned by the algorithm can successfully reach the target point under the condition of multiple obstacles.