Reviewerly/exHarmony huggingface dataset:
Cortical Surface Shape Analysis Based on Alexandrov Polyhedra

Shape analysis has been playing an important role in early diagnosis and prognosis of neurodegenerative diseases such as Alzheimer's diseases (AD). However, obtaining effective shape representations remains challenging. This paper proposes to use the Alexandrov polyhedra as surface-based shape signatures for cortical morphometry analysis. Given a closed genus-0 surface, its Alexandrov polyhedron is a convex representation that encodes its intrinsic geometry information. We propose to compute the polyhedra via a novel spherical optimal transport (OT) computation. In our experiments, we observe that the Alexandrov polyhedra of cortical surfaces between pathology-confirmed AD and cognitively unimpaired individuals are significantly different. Moreover, we propose a visualization method by comparing local geometry differences across cortical surfaces. We show that the proposed method is effective in pinpointing regional cortical structural changes impacted by AD.
MPTS-AFBP: Multi-pedestrian Tracking and Segmentation Based on Anchor-Free Detector

In the last two years, a small amount of work has explored a new task called Multi-Object Tracking and Segmentation (MOTS), which aims to classify, locate, segment, and track all instances of a particular category in the whole video sequence. However, the existing MOTS algorithms have some problems, such as low segmentation accuracy and frequent object identity switching. In order to solve these problems, we propose a multi-pedestrian tracking and segmentation method based on anchor-free detector. Subsequently, in the segmentation branch, we fuse multiple information to refine the mask through blender module. In addition, in the tracking branch, we encode the position information of the object and combine it with appearance feature to generate association vector. In order to test the effectiveness of our method, we have carried out verification on the KITTI MOTS and MOTS Challenge dataset, and the experimental results prove the effectiveness of the method.
Estimating Patient Independence with Sleep Sensors

The aging population problem is growing worldwide and physical therapists must be able to care for the elderly and injured efficiently. One burden facing physical therapists is determining the Functional Independence Measure (FIM), which measures the level of independence in activities of daily living for the elderly and injured. This measure is used in designing rehabilitation programs. Determining the FIM is a time-consuming task for both physical therapists and rehabilitation patients because it requires an interview. Some researchers have explored estimating FIM using wearable devices; however, it is burdensome for patients to wear such devices continuously. Therefore, we propose a method to estimate FIM motor items automatically using machine learning and sleep sensors. The proposed method classifies patients into three levels of FIM values that are referred to as s-FIM using vital data acquired through sleep sensors, personal data (age, gender, and Body Mass Index), and s-FIM values from the previous day. The results of a one-week study based on 19 patients showed that the proposed method had a mean accuracy of 0.87 for s-FIM motor items. The results were more accurate than continuing to use the s-FIM values determined by the physical therapist without updating them for one week.
Oversampling the minority class using a dedicated fitness function and genetic algorithmic progression

Summary Class imbalance is a pertinent characteristic of a number of real‐world datasets. The underrepresentation of the minority class in such datasets causes difficulty in learning and prediction of its members. In this era of Big Data, we need to address this practical issue in order to get admissible outputs from our classifier models. We present a novel work, oversampling the minority class using a dedicated fitness function and genetic algorithmic progression – to tackle the problem of class imbalance. We generate a random set of feature points in the given space and use genetic‐algorithm guided progression to metamorphose them into a set of potential minority points. The optimization function looks at the neighborhood of the synthetic minority points for computing the fitness scores. We have employed 19 real‐world datasets, three metrics, three classifiers, and four diversified oversampling methods in the empirical study. A comprehensive study involving several well‐established minority oversamplers demonstrates the efficaciousness of the proposed method in handling class imbalance.
Design of an Embedding Algorithm in Image Steganography

Steganography is the art and science of hiding information which embeds hidden content into cover media so as not to provoke eavesdroppers suspicious activities. Steganography differs from cryptography in the sense that where cryptography focuses on keeping the contents of data secret while steganography focuses on keeping the existence of a data secret. Image steganography is the process of embedding of information into the cover image without degradation in the image and make the image robust so that users who do not have authorization with this information cannot access it. The approach is to implement new embedding technique for image steganography which is simple and this technique will use for all the file formats of an image. This embedding algorithm embeds the master image file size, type of encryption algorithm (DES or AES), compression ratio of secret file size, data of secret file size which is also encrypted using a secret key. The Deflate compression technique is also used to compressed secret file into the cover file. The secret message or another image file or a file can be embedded into a cover image which is also referred to as stego image. The proposed system maintains imperceptibility and data integrity property of steganography. The proposed method satisfies the reversibility property, imperceptibility, hidden capacity of data into cover image of steganography to reconstruct secret and cover images without any loss of information.
Research on the Planning Method of Robot Terminal Speed Based on Task Characteristics

Abstract In order to study the speed planning of the industrial robots trajectory end under different tasks, the three speed planning methods of the third degree polynomial, the fifth degree polynomial and the five-segment S-curve are compared and studied. Analyze the stability and positioning accuracy of the robot’s end motion process in different occasions, and combine the speed and acceleration changes of the three speed planning methods, and recommend the end speed planning method for common tasks such as palletizing, loading and unloading, and grinding. Taking specific tasks as an example, the simulation results verify the rationality of the recommended method.
SGBMN: Symplectic Group Bayesian Manifold Network for Few-shot Classification

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Particularly, employing meta-learning for few-shot classification has achieved remarkable advances. However, the uncertainty problem triggered by the noisy data or modeling assumptions hinders the performance of existing approaches to be further improved. To tackle the above issue, this paper proposes a novel meta-learning approach called Symplectic Group Bayesian Manifold Network (SGBMN) for a more accurate classification prediction. Specifically, we adopt symplectic group bayesian matrix to represent each input data point, such that the uncertainty problem caused by complex data could be alleviated. Then, a manifold space is constructed by combing the above symplectic group bayesian matrices, in which the optimization can be performed by natural gradient descent. We conduct extensive experiments on two real-world image datasets and the results demonstrate that our proposed method outperforms several baseline approaches.
Enhancing the Structure of Cyber Risks in Cloud Environment Using Cloud Forensics Technique

With a lively growth of cloud in all the sectors all over, cloud computing has become one of the reference structures in the field of war for cyber attackers, where one of the major problems of cloud computing is to protect data from multiple outbreaks. In security programs, cybersecurity encourages many compliance mechanisms to resolve many vendor management attackers. Multiple mechanisms are required in an enterprise to set up a supplier management system that involves determining the risks of confidentiality, credibility and accessibility associated with third parties in resource systems. The stance of cybersecurity depends on knowing the hazards that threaten them. A threat vector that requires access to confidential data and analytical systems is a third party. Together with an investigative team for cloud forensics, a substructure focused on trustworthy third-party proves to be a safer approach. To improve the service provider's productivity and thus allow cloud providers to collect strong evidence from security attackers could aid in further legal action.
Improving Object Permanence using Agent Actions and Reasoning

Object permanence in psychology means knowing that objects still exist even if they are no longer visible. It is a crucial concept for robots to operate autonomously in uncontrolled environments. Existing approaches learn object permanence from low-level perception, but perform poorly on more complex scenarios, like when objects are contained and carried by others. Knowledge about manipulation actions performed on an object prior to its disappearance allows us to reason about its location, e.g., that the object has been placed in a carrier. In this paper we argue that object permanence can be improved when the robot uses knowledge about executed actions and describe an approach to infer hidden object states from agent actions. We show that considering agent actions not only improves rule-based reasoning models but also purely neural approaches, showing its general applicability. Then, we conduct quantitative experiments on a snitch localization task using a dataset of 1,371 synthesized videos, where we compare the performance of different object permanence models with and without action annotations. We demonstrate that models with action annotations can significantly increase performance of both neural and rule-based approaches. Finally, we evaluate the usability of our approach in real-world applications by conducting qualitative experiments with two Universal Robots (UR5 and UR16e) in both lab and industrial settings. The robots complete benchmark tasks for a gearbox assembly and demonstrate the object permanence capabilities with real sensor data in an industrial environment.
Integration of Coordinate and Geometric Surface Normal for 3D Point Cloud Object Detection

LiDAR-based 3D object detection is important for numerous applications, such as autonomous driving and robot navigation. How to use the specialized geometric representation of point clouds, such as surface normal, to promote the performance of 3D detection is an essential but seldom explored issue. In this paper, we introduce a normal module using geometric surface normal representation to supplement existing methods using coordinate representation. We also propose an attention module for further integration. It integrates features learning from the two representations – coordinate and surface normal, so that they can make up for each other's feature space. Our framework can improve the object detection accuracy, especially for targets with complementary coordinate and surface normal features. As far as we know, we are the first work applying surface normal of LiDAR point cloud to 3D object detection, which can be easily extended to other LiDAR-based methods. Extensive experiments on the KITTI dataset demonstrate that our method boosts the performance of 3D object detection for the voxel-based methods and achieves state-of-the-art results.
Inferring the Number and Order of Embedded Topics Across Documents

Documents are often organized according to an embedded structure, where a set of documents covers a topic and gets extended to a more specialized topic. We refer to this structure as embedded topics and address the issue of inferring the number and order of topics in a given corpus. While this problem is akin to finding clusters of documents and has been addressed in numerous studies in areas such as topic modeling, information extraction and knowledge discovery, we show that existing approaches are not effective in the specific context of embedded topic structures, and propose a novel technique for that purpose. We also propose an approach to uncover the order of such embedded topics. To determine the number of topics, the proposed method relies on the analysis of eigenvalues of a conditional probability matrix derived from the document-term matrix. We use Kmeans to determine the actual topic clusters, and conditional probability computation to determine the order. We compare the performance of our method to alternative methods for determining clusters and dimensionality. Results show that the proposed approach can effectively derive the right number of topics and embedding structure order.
Deep Learning in Smart Video Surveillance for Crowd Management: A Systematic Literature Review

A crowd is defined as a gathering of people in the same premises. When the number of people exceeds normal conditions, overcrowding becomes a concern in safety and health-related matters due to the risks that a large crowd can impose on the individuals present in the area. Crowd analysis is a growing trend in computer vision related to the concerns in crowd monitoring. To alleviate risks related to crowds, intelligent techniques applied to surveillance are used to analyze a crowd and to monitor its density and the behavior of people captured in footage. Through a systematic literature review of various papers published in the last five years related to crowd analysis, the numerous deep learning algorithms applied in past researchers are presented and are assessed to come up with a solution that will further aid in crowd management
Research on UAV Anti-Multi-type Interferences Strategy Based on Improved Q-Learning

UAVs have been widely used in military and civilian applications, and they play a huge role in many aspects. However, due to the open nature of the UAV communication link, it is susceptible to malicious or unintentional interference. Besides, the interference that UAVs are facing has developed from a single type of interference to multi-type interference. The traditional anti-interference method is designed to deal with single-dimensional interference and cannot meet the requirements of anti-Multi-type interference. The purpose of the paper is to improve the communication capabilities of UAVs by programming frequency-power (Change the transmission frequency and transmission power of the UAV)in Multi-type interference environment. Compared with the existing work, the UAVs in the system can exploit degree-of-freedom in frequency, power to optimize the communication quality in the receiving area and reduce energy costs. The paper propose a Anti-multi-type interference strategy based on improved and eligibility traces Q-learning algorithm, which is based on multi-parameters, and the simulation results show the effectiveness of the algorithm.
Fine-Grained Classification of Neutrophils with Hybrid Loss

Acute leukemia is a malignant clonal disease of hematopoietic stem cells, which is usually diagnosed by morphological examination of bone marrow cells. However, the morphological examination usually relies on the subjective inference of cell morphology experts and is labor-intensive. With the development of computer vision, automatic classification and counting of blood cells is increasingly popular, which greatly improves work efficiency. Within this context, we here propose a novel method for neutrophil classification, which is based on deep neural network. In brief, it first crops the single cells from the large images, and then makes use of the loss functions designed for face recognition and weakly-supervised fine-grained visual classification. With the hybrid loss, the trained network can focus on nucleus areas, extract features with inter-class differences and intra-class compactness. Experiments show that the proposed method can obtain higher overall accuracy. Data is available at https://github.com/stevenxmy/subAML-dataset.git.
A web page query technology based on ASP.NET and SQL Server

For the development of web dynamic web pages based on ASP.NET and SQL Server, in order to improve the response speed of web pages when dealing with large amounts of data, a custom paging control is developed. By setting the properties of the paging control and writing a brief calling code, the paging function is realized.
ACARS Signal Source Generation and Recognition Based on Convolutional Neural Network

With the rise of artificial intelligence technology, using convolutional neural network (CNN), which has outstanding effect in other fields, to quickly identify and classify electromagnetic signals has become a subject with practical value. In this paper, the downlink Aircraft Communications Addressing and Reporting System (ACARS) signals of civil aircraft communicating with the airport tower is studied. With a digital receiver used to collect signals, software decoding signals and cross-correlation method adopted to locate and crop signals, a dataset contains thousands of ACARS signals is created. The type of the aircraft sending the signal is taken as the learning target to carry out the classification task. Using a pre-transformation network consisting of several convolutional layers to take full advantage of the information of the signal, the multiscale information and periodic characteristics of the signal are fused. This net transforms the input signal to a 3×224×224 feature map, and then puts it into a classic network like VGG19 and ResNet, the accuracy rate of 92.2% and 88.8% is achieved on two different levels goals respectively.
Towards Execution-Efficient LSTMs via Hardware-Guided Grow-and-Prune Paradigm

Long short-term memory (LSTM) applications need fast yet compact models. Neural network compression approaches, e.g., the grow-and-prune paradigm, have proved to be promising for cutting down network complexity by skipping insignificant weights. However, current compression strategies remain mostly hardware-agnostic and network complexity reduction does not always translate to execution efficiency. In this work, we propose a hardware-guided symbiotic training methodology for compact, accurate, yet execution-efficient inference models. It is based on our observation that hardware may introduce substantial non-monotonic behavior, which we call the latency hysteresis effect, when evaluating network size versus inference latency. This observation raises question about the mainstream smaller-dimension-is-better compression strategy, which often leads to a sub-optimal model architecture. Leveraging the hardware-impacted hysteresis effect and sparsity, we enable a symbiosis of model compactness and accuracy with execution efficiency, thus reducing LSTM latency while increasing its accuracy. We have evaluated our approach on language modeling and speech recognition applications. Relative to the traditional stacked LSTM architecture obtained for the Penn Treebank dataset, we reduce the number of parameters by 18.0× (30.5×) and measured run-time latency by up to 2.4× (5.2×) on Nvidia GPUs (Intel Xeon CPUs) without any accuracy degradation. For the DeepSpeech2 architecture obtained for the AN4 dataset, we reduce the model size by 7.0× (19.4×), word error rate from 12.9% to 9.9% (10.4%), and measured run-time latency by up to 1.7× (2.4×) on Nvidia GPUs (Intel Xeon CPUs). Our method consistently outperforms prior art for both applications, with compact, accurate, and execution-efficient inference models.
Research on classification and application of sports images based on visual attention analysis

Nowadays, with the rapid expansion of video information, how to label video quickly and effectively and locate the key content of video quickly is a big challenge in the field of multimedia processing and computer vision. Advanced digital equipment and endless media means produce a lot of multimedia information every day. The popularization of broadcast communication and Internet technology makes these information spread, shared and applied in the world. With the increasing popularity of modern video technology, sports visual culture needs to study various visual images in the field of sports. This paper proposes a moving image classification method based on visual attention analysis, which greatly reduces the amount of data and improves the classification efficiency.
A human in the technogenic reality of life

In the conditions of industrial social development, a man-made environment (the technosphere) enlarges, while the biosphere and its biological processes are being transformed in the anthropogenic way, and as a result, this technogenic reality of life, in which a person exists, is being formed. The accelerated expansion of the technosphere requires not only Cyclopean depleting of natural resources, but makes a man apply himself fully-extended both physically and psycho-emotionally, causing certain difficulties in his adaptation and socialization profession-wide, including such an innovative science-intensive industry as railways. Modern education system does not correct such processes, contributing to repressing of personal enhancement, and prevents marshalling of the individual’s resources in the struggle for the preservation of biosphere life and natural habitat. Therefore, the problem of the need of modern education resources identification together with the development of its new philosophical growth strategy based on the potential of social pedagogy aimed at the full adaptation and socialization of a person in the technogenic reality of life, both physically tangible and virtual has been raised in the study. The research of social pedagogy is based on the concept of socialization, which will make possible the maximum expansion of the scope intended for the development of practical support programs for the existence of an individual. Such an interdisciplinary approach, combining educational technologies and socio-pedagogical support of the individual throughout his life, will allow both overcoming the negative consequences of transformational processes connected with the expansion of the technosphere, and solving the problems of adaptation and socialization of a person in the conditions of dangerously changing socio-technogenic development of the world.
Label projection online hashing for balanced similarity

Since online hashing has the advantages of low storage and fast calculation ,it attracts the attention of many scholars. However, the learning of new data streams separates the similarity between new data and existing data in many online hashing methods, which leads to poor retrieval performance. In addition, the similarity measure ignores the expression of different similarity. In this paper, we propose a novel supervised method, namely Label Projection Online Hashing for Balanced Similarity (LPOH). Compared with existing online hashing methods, LPOH aims to solve the problem of the effective establishment of the projection between the label vector and the binary code, and the successful realization of description of different similarity between the same labeled data. Specifically, LPOH overcomes the problem of similarity deviation caused by data imbalance via establishing a mapping matrix to derive a relationship between the data label vector and the binary code. Furthermore, the error between the binary code and the hash function concerning data streams is described. Extensive experiments on widely-used three benchmark datasets demonstrate that LPOH outperforms the state-of-the-art online hashing methods.