Reviewerly/exHarmony huggingface dataset:
Moving Object Detection and Tracking

An image sequence is composed of a series of 2-D (spatial) images that are continuous in time, or a special type of 3-D image, which can be represented as f(x, y, t). The continuously collected image sequence can reflect the movement of the scene and the change of the scene. This chapter will introduce the basic method of using pixel-by-pixel difference to detect change information. The cumulative difference image can better overcome the influence of random noise. This chapter will discuss some basic background modeling methods and compares their effects, including: single Gaussian model-based methods, video initialization-based methods, Gaussian mixture model-based methods, and codebook-based methods. This chapter will introduce the derivation of the optical flow equation and the least square method of optical flow estimation; on this basis, the optical flow in motion is analyzed, and a dense optical flow algorithm based on the brightness gradient is given. This chapter will introduce several typical moving object tracking methods, including Kalman filter, particle filter, and mean shift and kernel tracking technology.
Unmanned Aerial Vehicle Implementation for Pavement Condition Survey

This research takes photos of pavement conditions using drones to produce 2D and 3D models as visual media for assessing pavement conditions using PCI and SDI methods. The results of the assessment on the model compared with the assessment in the field. In comparison, the dimension level of accuracy of pavement distress in the model against the results of manual measurements in the field, edge cracking has the lowest accuracy value of 75.72%, and joint reflective crack has the highest accuracy value of 97.86%. The linear regression equation for the PCI method in the model against manual PCI in the field is y = 0.931x + 3.6003 with a coefficient of determination r2 = 0.86 and an MSE of 121.333. While the linear regression equation for the SDI method in the model against manual SDI in the field is y = 0.5831x + 28.867 with a coefficient of determination that is r2 = 0.653 and MSE = 2115. Based on the comparison above, in this study, the application of UAV for pavement condition assessment is more precisely using the PCI method.
Low-Rank and Sparse Metamorphic Autoencoders for Unsupervised Pathology Disentanglement

In order to establish population-based analysis of image data from multi-center studies, it is often helpful to disentangle images in their shape and appearance components. However, abnormal (e.g. pathological) and normal appearances of images strongly differ and should ideally be separated in the modeling process. In this work, we propose a metamorphic autoencoder for the disentanglement of shape as well as normal and abnormal appearance of medical images by integrating a low-rank and sparse decomposition into the training process. Experiments show that this method can reliably be used for unsupervised pathology disentanglement opening perspectives for unsupervised pathology segmentation, pseudo-healthy image synthesis and conditional image generation.
Sc-Pinns: Integrating the Stochastic Collocation Method with Physics Informed Neural Networks for Uncertainty Propagation

A formulation is introduced, denoted as SC-PINNs, that integrates physics informed neural networks (PINNs) into the stochastic collocation method for uncertainty propagation. PINNs are aimed toward establishing a neural network model that maps input spatial-temporal coordinates to the solution field for a single, deterministic boundary value problem. In contrast, SC-PINNs are concerned with uncertainty propagation and aim to establish a neural network that maps the input uncertain parameter sample space to the solution field space. This is accomplished by substituting the solution field at stochastic collocation points, traditionally obtained from finite element analyses, with a PINN model that takes as input the stochastic collocation points and outputs their corresponding solution fields. The stochastic collocation points form a natural training dataset for the SC-PINN model. By integrating with the stochastic collocation method, where Lagrange basis functions interpolate the solution field over the uncertain parameter sample space, the SC-PINN is deliberately overfit to the training data because these are the only points in the sample space that it is required to evaluate. The key feature of SC-PINNs is that the solution fields for all the stochastic collocation points are constructed simultaneously. This is contrast to de facto uncertainty propagation techniques which treat the boundary value problem associated with the samples as deterministic and independent problems. These sample-based approaches lead to the so-called curse of dimensionality, which states that computational complexity increases exponentially with increasing stochastic dimension. On the other hand, SC-PINNs are less susceptible to the curse of dimensionality because of the simultaneous incorporation of all stochastic collocation points during training. This is the primary feature of the introduced framework and key to its potential as an efficient uncertainty propagation technique. The present study introduces the formulation of SC-PINNs and provides numerical examples to demonstrate and evaluate their performance.
Improvement in Software Maintainability Prediction Using Data Balancing and Machine Learning

A challenging job in software engineering predictive modeling is the early identification of software modules or classes that require a lot of maintenance effort. In recent years, a number of prediction models have been constructed to predict the maintainability of software classes or modules using machine learning (ML) and ensemble approaches. But the major problem with the Software Maintainability Prediction (SMP) models is their accuracy of prediction. The accuracy of prediction is normally very low due to the imbalanced data sets used for training the models. Classification and training algorithms using the imbalance dataset for training tend to favor majority classes (classes with low maintainability effort) in their predictions while ignoring the minority class instances as noise. In this research, a methodology for the development of efficient and effective software maintainability prediction (SMP) models has been proposed. A further empirical investigation has been done to evaluate the effectiveness of SMP models created with ML and ensemble methods. The study uses three different data balancing methods to preprocess the unbalanced data before training the SMP models. The study's findings indicate that the Resample method of data balancing is a practical way to handle uneven datasets and develop accurate SMP prediction models, while the booted tree and bagged tree ensemble techniques show promising accuracy for training SMP models.
'Can AI Develop Human like Consciousness? Cognitive Abilities

A future where appliances' spiritual measurements, tactics, of our personal is quickly illustrating nearer. PC-based insight (ML) calculations are winding up being powerfully solid, and we can feel them breathing down our necks. Handles with human-level data ready for talk and thinking will arise in the accompanying various years, passing on a great number of advantages to financial points, authoritative issues, and, unavoidably, combat. The improvement of affirmed made reasoning will by and large impact manliness' approach, including whether or not it has one. "The present moment, research is in progress to all the more likely handle what the following AI frameworks will actually want to do while staying inside the present insight constraints. Most of AI programs now being developed are restricted to simplifying choices or executing straightforward procedure on modest quantities of information." Despite the fact that scholastics fluctuate on what precisely comprises insight, regular or counterfeit, most figure that PCs will achieve fake general knowledge (AGI) sooner or later.
Socializing Social Bots on Social Media

The advancement of conversational AI techniques is driving the rise of chatbot applications in various fields. Pioneering business studies have examined the effects of functional chatbots for specific business tasks, such as customer service. This study extends the business literature by focusing on a new type of chatbot, the social chatbot. We develop a GPT-based social chatbot for fitness-related topics and implement it on Twitter. Specifically, this field experiment identifies a strong negative impact of the chatbot’s identity on its ability to engage social media users. We further explore the mechanisms of a reduction in perceived socialization value caused by chatbot identity disclosure by diving into a commonly studied factor, gender matching. The chatbot presents as female, whose perceived socialization value is higher for male social media users. Correspondingly, male users are likely to experience a stronger reduction in perceived socialization value if the account is disclosed as a chatbot. Moreover, we investigate the role of gender cues in the chatbot’s textual replies. While the chatbot is constructed as a female, male cues could potentially present gender inconsistency, which we find to be further detrimental to user engagement after chatbot identity is disclosed. This study explores the new format of the social chatbot on social media and digs into its socialization value. In deepening the understanding of social chatbots, we use gender as a key factor to uncover how their potential socialization value can be achieved or undermined with or without the chatbot’s identity being revealed.
Artificially Intelligent Cultural Perspectives with NLP: Exploring Japanese Cultural Heritage Collections with AI (Slides)

This document provides slides representing the topic: 'Artificially Intelligent Cultural Perspectives with NLP: Exploring Japanese Cultural Heritage Collections with AI, and is based on a presentation at Rutgers University during Fall of 2022. It addresses the use of artificial intelligence (AI) techniques to support the development of cultural perspectives with a focus on Japan. AI,defined as 'a set of technologies that mimic the functions and expressions of human intelligence, specifically cognition and logic' and natural language processing (NLP) can be used to support cultural heritage and historical research - new insights, restoration and AI-NLP supported initiatives hold significant promise. (This post contains slides).
Bangladesh's Data Protection Bill

Of the few countries in Asia which do not have data privacy laws or Government Bills, Bangladesh is the most likely to enact such a law. The government released The Data Protection Act, 2022 for public comment in April 2022, but it has not yet reached the floor of Bangladesh’s unicameral Parliament. This article is a brief introduction to the Bill, its strengths and weaknesses, noting key features such as:* A confusing approach to extra-territorial application.* A mistaken exclusion of pseudonymous data.* A reliance on consent rather than legitimate grounds for processing.* A separate requirement to protect ‘the right of privacy’ in data collection, processing and use.* A non-independent Data Protection Office (regulator) must be established by the government, controlled by the Digital Security Agency, and headed by its Director-General.* The maximum penalties or compensation will be US$ 5,000 (approx. 500,000 (5 lac) Bangadeshi rupees (taka)).
Temporal fact extraction of fruit cultivation technologies based on deep learning

&lt;abstract&gt;&lt;p&gt;There are great differences in fruit planting techniques due to different regional environments. Farmers can't use the same standard in growing fruit. Most of the information about fruit planting comes from the Internet, which is characterized by complexity and heterogeneous multi-source. How to deal with such information to form the convenient facts becomes an urgent problem. Information extraction could automatically extract fruit cultivation facts from unstructured text. Temporal information is especially crucial for fruit cultivation. Extracting temporal facts from the corpus of cultivation technologies for fruit is also vital to several downstream applications in fruit cultivation. However, the framework of ordinary triplets focuses on handling static facts and ignores the temporal information. Therefore, we propose Basic Fact Extraction and Multi-layer CRFs (BFE-MCRFs), an end-to-end neural network model for the joint extraction of temporal facts. BFE-MCRFs describes temporal knowledge using an improved schema that adds the time dimension. Firstly, the basic facts are extracted from the primary model. Then, multiple temporal relations are added between basic facts and time expressions. Finally, the multi-layer Conditional Random Field are used to detect the objects corresponding to the basic facts under the predefined temporal relationships. Experiments conducted on public and self-constructed datasets show that BFE-MCRFs achieves the best current performance and outperforms the baseline models by a significant margin.&lt;/p&gt;&lt;/abstract&gt;
A Flexible Toolflow for Mapping CNN Models to High Performance FPGA-based Accelerators

There have been many studies on developing automatic tools for mapping CNN models onto FPGAs. However, challenges remain in designing an easy-to-use toolflow. First, the toolflow should be able to handle models exported from various deep learning frameworks and models with different topologies. Second, the hardware architecture should make better use of on-chip resources to achieve high performance. In this work, we build a toolflow upon Open Neural Network Exchange (ONNX) IR to support different DL frameworks. We also try to maximize the overall throughput via multiple hardware-level efforts. We propose to accelerate the convolution operation by applying parallelism not only at the input and output channel level, but also at the output feature map level. Several on-chip buffers and corresponding management algorithms are also designed to leverage abundant memory resources. Moreover, we employ a fully pipelined systolic array running at 400 MHz as the convolution engine, and develop a dedicated bus to implement the im2col algorithm and provide feature inputs to the systolic array. We generated 4 accelerators with different systolic array shapes and compiled 12 CNN models for each accelerator. Deployed on a Xilinx VCU118 evaluation board, the performance of convolutional layers can reach 3267.61 GOPS, which is 99.72% of the ideal throughput (3276.8 GOPS). We also achieve an overall throughput of up to 2424.73 GOPS. Compared with previous studies, our toolflow is more user-friendly. The end-to-end performance of the generated accelerators is also better than that of related work at the same DSP utilization.
Robust User Authentication Scheme for IoT-Based Healthcare Applications

Cloud technology and internet of things (IoT) is a quickly developing computing technology through which resources are shared via internet. IoT facilitates seamless interactions between humans and heterogeneous devices including personalized healthcare systems. It has the potential to provide ensure supervision of the elderly, telemonitoring, teleconsultations, and computer-assisted rehabilitation. These healthcare services allow easy access of patient data in real time. However, IoT-based remote patient monitoring and other healthcare applications are growing fast; the hurdle to its growth is security. This chapter proposes a novel remote user authentication scheme for IoT-enabled healthcare applications. The scheme is lightweight and robust to attacks and also has low computational overhead. The proposed scheme satisfies the desired essential attributes of security. A formal verification performed using ProVerif proves the security of the scheme.
3D digital geoscientific modeling and its significance of Julong’an uranium deposit

The 3D (three-dimensional) modeling has become an important digital and information technology with the continuous progress of science and technology, and it has been widely used in the ore prospecting, especially the uranium ore, which is an important strategic resource for the development of our country's nuclear industry. In recent years, 3D digital modeling technology has become a mainstream method in the geological industry with the rapid development of prospecting and prediction methods, and it plays a huge role in prospecting and prediction research for uranium deposits. In order to strengthen the prospecting prediction research of uranium deposits in the Xiangshan uranium ore field, a three-dimensional digital modeling study was carried out in this paper. Based on previous research of the metallogenic geological background, ore deposit characteristics and metallogenic regularity of the Julong'an uranium deposit in Xiangshan area, Jiangxi Province, a semi-automatic 3D digital and information modeling software, SKUA-GOCAD, was used to build topographic surface, structure stratigraphy and ore body models of the Julong'an uranium deposit in this paper. It can analyze the spatial distribution and geological morphological characteristics of each geological body in the study area using these digital and information models intuitively. Together with metallogenic characteristics, the metallogenic prospective area was divided using the 3D digital geological prediction model, and it showed that the prospecting potential for the Julong'an uranium mining area was great. From this research we can draw a conclusion that it has an obvious ore-controlling effect for the stratigraphy, structure and abrupt changing zone of interfaces of different formations. Meanwhile, the intersection region with the interface between the rock formations, the abrupt changing zone of the interface between different formations, and the intersection region of three interfaces are the favorable metallogenic areas. This research not only provided important prospecting ideas for the subsequent mineral exploration of uranium deposits, but also was beneficial to the next stage of ore prediction research in the Xiangshan area.
The practice and thinking of the construction of the examination database of biochemistry

In the traditional biochemistry teaching process, the examination is usually done by the teacher after the course is finished. This test mode is currently the more “classic” mode, and students are generally accustomed to this method. However, there are bound to be certain defects in the teacher's independent examination, such as the choice of the content of the test questions is more arbitrary, and the knowledge scope of the assessment is relatively narrow. In addition, the traditional mode of separately marking and correcting questions will also result in different teachers' question-setting style, degree of depth, and different grading standards for teachers when grading. In the case of large fluctuations in test scores, it is difficult to test the teaching effect. In order to pursue a high pass rate, some teachers usually delineate the scope of the exam for students before the exam. Students often do not study seriously before the exam, which makes the students' exam scores and their understanding and mastery of knowledge points. Therefore, the course group is carrying out a computer-based examination question bank construction. It is designed to implement the separation of teaching and examination, and make the course examination more scientific, objective and accurate. At the same time, it can also provide conditions for the paperless examination of the course. Through teaching practice, the database can effectively improve the enthusiasm of students to study, and the teaching effect is good.
Analysis of the Application of Blockchain in the Military Supply Chain

The military manufacturing is a complex and huge industry. With the rapid development of economic globalization and the deepening of industrial division of labor, issues such as trust management between enterprises, privacy protection during interaction, and the authenticity and transparency of product information have become increasingly prominent. The technical characteristics of the blockchain itself can play a great advantage in the field of collaborative management of the military supply chain. This paper studies the current development status of blockchain technology, supply chain collaboration, military supply chain and other fields, and the application direction of blockchain technology in various fields are analyzed and summarized. It discusses and summarizes the possible combination of the current military supply chain data collaborative sharing and the blockchain, also, some possible improvements are proposed.
ToSA: A Top-Down Tree Structure Awareness Model for Hierarchical Text Classification

Hierarchical text classification (HTC) is a challenging task that classifies textual descriptions with a taxonomic hierarchy. Existing methods have difficulties in modeling the hierarchical label structure. They focus on using the graph embedding methods to encode the hierarchical structure, ignoring that the HTC labels are based on a tree structure. There is a difference between tree and graph structure: in the graph structure, message passing is undirected, which will lead to the imbalance of message transmission between nodes when applied to HTC. As the nodes in different layers have inheritance relationships, the information transmission between nodes should be directional and hierarchical in the HTC task. In this paper, we propose a Top-Down Tree Structure Awareness Model to extract the hierarchical structure features, called ToSA. We regard HTC as a sequence generation task and introduce a priori hierarchical information in the decoding process. We block the information flow in one direction to ensure the graph embedding method is more suitable for the HTC task, then get the enhanced tree structure representation. Experiment results show that our model can achieve the best results on both the public WOS dataset and a collected E-commerce user intent classification dataset $$^3$$ .
POSE: A Positional Embedding Model for Knowledge Hypergraph Link Prediction

Link prediction in knowledge hypergraphs has been widely recognized as crucial for various downstream tasks of knowledge-enabled applications, from question answering to recommender systems. However, most current approaches are directly extended from binary relation of the knowledge graph to n-ary relation, thus cannot capture entities’ role and positional information in each n-ary tuple. To accommodate the transformation of relations from binary to n-ary in the knowledge hypergraph, in this work, we propose POSE, which exploits the semantic properties of tuples at both role and position levels. POSE explores an embedding space with basis vectors and represents the role and positional information of entities through a linear combination, which promotes similar representations for entities with related roles and the same positions. Then, a relation matrix is further employed to capture the compatibility of both information with all associated entities, and a scoring function is used to measure the plausibility of tuples composed of entities with specific roles and positions. Meanwhile, POSE achieves full theoretical expressiveness and predictive efficiency. Experimental results show that POSE achieves an average improvement of 4.1% on MRR compared to state-of-the-art knowledge hypergraph embedding methods. Our code is available at https://github.com/zirui-chen/POSE .