Reviewerly/exHarmony huggingface dataset:
Construction of applied talents training system based on machine learning under the background of new liberal arts

The development of the new liberal arts field places emphasis on the integration of disciplines such as humanities, engineering, medicine, and agriculture. It specifically highlights the incorporation of new technologies into the education and training of liberal arts majors like economics, law, literature, history, and philosophy. However, when dealing with complex talent data, shallow machine learning algorithms may not provide sufficiently accurate evaluations of the relationship between input and output. To address this challenge, this article introduces a comprehensive evaluation model for applied talents based on an improved Deep Belief Network (DBN). In this model, the GAAHS algorithm iteratively generates optimal values that are utilized as connection weights and biases for the restricted Boltzmann machines (RBM) in the pre-training stage of the DBN. This approach ensures that the weights and biases have favorable initial values. Moreover, the paper constructs a quality evaluation index system for creative talents, which consists of four components: knowledge level, innovation practice ability, adaptability to the environment, and psychological quality. The training results demonstrate that the optimized DBN exhibits improved convergence speed and precision, thereby achieving higher accuracy in the classification of applied talent evaluations.
Time‐varying long‐range navigation signal channel modelling based on Dirichlet process mixtures method

Abstract A model of the LOng‐RAnge Navigation signal can provide a mathematical model for the development of related technology research. The propagation variations of the signal exhibit non‐stationary and non‐linear characteristics due to the time‐varying nature of the atmospheric dielectric constant in the channel. A Dirichlet process mixtures method is used by the authors to model the statistical properties of radio wave attenuation, and the Gibbs sampling method is used to flexibly estimate the parameters of the model. The model as a non‐parametric model can be applied to capture the dynamics of time‐varying signals with the parameters being adaptively inferred. A receiver is used as a measurement device to obtain real propagation data to check the performance of the model. Kullback–Leibler Divergence (KL‐Div) was used to evaluate different datasets. The results showed that the model's statistical properties were consistent. Its flexibility allows it to represent complex distributions without prior knowledge.
Guiding Deep Learning with Expert Knowledge for Dense Stereo Matching

Abstract Dense depth information can be reconstructed from stereo images using conventional hand-crafted as well as deep learning-based approaches. While deep-learning methods often show superior results compared to hand-crafted ones, they commonly learn geometric principles underlying the matching task from scratch and neglect that these principles have already been intensively studied and were considered explicitly in various models with great success in the past. In consequence, a broad range of principles and associated features need to be learned, limiting the possibility to focus on important details to also succeed in challenging image regions, such as close to depth discontinuities, thin objects and in weakly textured areas. To overcome this limitation, in this work, a hybrid technique, i.e., a combination of conventional hand-crafted and deep learning-based methods, is presented, addressing the task of dense stereo matching. More precisely, the input RGB stereo images are supplemented by a fourth image channel containing feature information obtained with a method based on expert knowledge. In addition, the assumption that edges in an image and discontinuities in the corresponding depth map coincide is modeled explicitly, allowing to predict the probability of being located next to a depth discontinuity per pixel. This information is used to guide the matching process and helps to sharpen correct depth discontinuities and to avoid the false prediction of such discontinuities, especially in weakly textured areas. The performance of the proposed method is investigated on three different data sets, including studies on the influence of the two methodological components as well as on the generalization capability. The results demonstrate that the presented hybrid approach can help to mitigate common limitations of deep learning-based methods and improves the quality of the estimated depth maps.
Information System For New Residents Registration Based On Web and Android

The Suko Village is one of 24 villages in the Sidoarjo sub-district, Suko village is the second most populous village in the Sidoarjo sub-district, reaching 15 thousand people spread over 72 RTs. Geographically, the Suko village area is in the middle of the city which has led to many housing developments that have made Suko village a densely populated settlement, as well as a destination for residents outside the city of Sidoarjo to stop by and settle down.The purpose of this research is to design a Information System For Registration New Residents Based On Web and Android that can be accessed. The research method used is the waterfall method, while the technique for collecting data uses observation and interview techniques. The result of this research is to make the village government aware of all its citizens, including those who live not permanently and can record citizens efficiently and no longer use a manual system.
Roman Script Orthography Development in Africa: Historical and Contemporary Perspectives

The aim of this chapter is to provide an account of the movement to develop Roman script orthographies for African languages. The first part traces the history of this movement from its origins in the early nineteenth century to the development of the Unicode standard in the twenty-first century. Each stage includes an assessment, noting which initiatives have stood the test of time and which have been discarded. The second part draws attention to five of the most challenging issues facing contemporary orthography developers, which are identified as under-representation of vowels, grammatical tone marking, dialect issues, cross-border issues, and smartphone adaptations. These are illustrated with cases studies from DR Congo, Uganda, Tanzania, Ethiopia, Nigeria, Niger, Benin and Togo.
Analysis of the Recursive Locally-Adaptive Filtration of 3D Tensor Images

This work is focused on the computational complexity (CC) reduction of the locally-adaptive processing of 3D tensor images, based on recursive approaches. As a basis, a local averaging operation is used, implemented as a sliding mean 3D filter (SM3DF) with a central symmetric 3D kernel. Symmetry plays a very important role in constructing the working window. The presented theoretical approach could be adopted in various algorithms for locally-adaptive processing, such as additive noise reduction, sharpness enhancement, texture segmentation, etc. The basic characteristics of the recursive SM3DF are analyzed, together with the main features of the adaptive algorithms for filtration of Gaussian noises and unsharp masking where the filter is aimed at. In the paper, the ability of SM3DF implementation through recursive sliding mean 1D filters, sequentially bonded together, is also introduced. The computational complexity of the algorithms is evaluated for the recursive and non-recursive mode. The recursive SM3DF also suits the 3D convolutional neural networks which comprise sliding locally-adaptive 3D filtration in their layers. As a result of the lower CC, a promising opportunity is opened for higher efficiency of the 3D image processing through tensor neural networks.
MSTPose: Learning-Enriched Visual Information with Multi-Scale Transformers for Human Pose Estimation

Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through random initialization, and fed into a parallel Transformer structure to learn spatial dependencies between features. As the Transformer outputs one-dimensional sequential features, the mainstream two-dimensional heatmap method is abandoned in favor of one-dimensional coordinate vector regression. The experiments show that MSTPose outperforms other CNN-based pose estimation models and demonstrates clear advantages over CNN + Transformer networks of similar types.
UDL: a cloud task scheduling framework based on multiple deep neural networks

Abstract Cloud task scheduling and resource allocation (TSRA) constitute a core issue in cloud computing. Batch submission is a common user task deployment mode in cloud computing systems. In this mode, it has been a challenge for cloud systems to balance the quality of user service and the revenue of cloud service provider (CSP). To this end, with multi-objective optimization (MOO) of minimizing task latency and energy consumption, we propose a cloud TSRA framework based on deep learning (DL). The system solves the TSRA problems of multiple task queues and virtual machine (VM) clusters by uniting multiple deep neural networks (DNNs) as task scheduler of cloud system. The DNNs are divided into exploration part and exploitation part. At each scheduling time step, the model saves the best outputs of all scheduling policies from each DNN to the experienced sample memory pool (SMP), and periodically selects random training samples from SMP to train each DNN of exploitation part. We designed a united deep learning (UDL) algorithm based on this framework. Experimental results show that the UDL algorithm can effectively solve the MOO problem of TSRA for cloud tasks, and performs better than benchmark algorithms such as heterogeneous distributed deep learning (HDDL) in terms of task scheduling performance.
Tourism Support System to Utilize Virtual Reality Space Reflecting Dynamic Information in Real Time

It is important that both static and dynamic information is efficiently used to create a suitable tourism plan. However, collecting, accumulating and managing dynamic information can cost tourists time, money and energy. In the present study, an original tourism support system was designed and developed with the purpose of reducing the burden of tourists who are unfamiliar with the urban tourist destinations in particular. An original tourism planning support system and web-geographic information systems (Web-GIS) were integrated into a single system, and two original functions were implemented. The system was operated targeting Osaka City, Japan, for a period of one month. The information concerning 529 sightseeing spots was collected from tourism-related web media and then saved to the database of the system beforehand. During the operation period, a cumulative total of 160 users accessed the system. Based on the questionnaire survey results, most of the main functions were highly rated regardless of users’ travel experiences to the operation target area. Additionally, based on the access log analysis results, it was clear that the use of the system was in line with the purpose of the present study, as the number of visits to the page for the main functions were high.
The Impact of the Web Data Access Object (WebDAO) Design Pattern on Productivity

In contemporary software development, it is crucial to adhere to design patterns because well-organized and readily maintainable source code facilitates bug fixes and the development of new features. A carefully selected set of design patterns can have a significant impact on the productivity of software development. Data Access Object (DAO) is a frequently used design pattern that provides an abstraction layer between the application and the database and is present in the back-end. As serverless development arises, more and more applications are using the DAO design pattern, but it has been moved to the front-end. We refer to this pattern as WebDAO. It is evident that the DAO pattern improves development productivity, but it has never been demonstrated for WebDAO. Here, we evaluated the open source Angular projects to determine whether they use WebDAO. For automatic evaluation, we trained a Natural Language Processing (NLP) model that can recognize the WebDAO design pattern with 92% accuracy. On the basis of the results, we analyzed the entire history of the projects and presented how the WebDAO design pattern impacts productivity, taking into account the number of commits, changes, and issues.
Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues

In the Knowledge Grounded Dialogue (KGD) generation, the explicit modeling of instance-variety of knowledge specificity and its seamless fusion with the dialogue context remains challenging. This paper presents an innovative approach, the Knowledge Interpolated conditional Variational auto-encoder (KIV), to address these issues. In particular, KIV introduces a novel interpolation mechanism to fuse two latent variables: independently encoding dialogue context and grounded knowledge. This distinct fusion of context and knowledge in the semantic space enables the interpolated latent variable to guide the decoder toward generating more contextually rich and engaging responses. We further explore deterministic and probabilistic methodologies to ascertain the interpolation weight, capturing the level of knowledge specificity. Comprehensive empirical analysis conducted on the Wizard-of-Wikipedia and Holl-E datasets verifies that the responses generated by our model performs better than strong baselines, with notable performance improvements observed in both automatic metrics and manual evaluation.
Optimization Based Layer-Wise Pruning Threshold Method for Accelerating Convolutional Neural Networks

Among various network compression methods, network pruning has developed rapidly due to its superior compression performance. However, the trivial pruning threshold limits the compression performance of pruning. Most conventional pruning threshold methods are based on well-known hard or soft techniques that rely on time-consuming handcrafted tests or domain experience. To mitigate these issues, we propose a simple yet effective general pruning threshold method from an optimization point of view. Specifically, the pruning threshold problem is formulated as a constrained optimization program that minimizes the size of each layer. More importantly, our pruning threshold method together with conventional pruning works achieves a better performance across various pruning scenarios on many advanced benchmarks. Notably, for the L1-norm pruning algorithm with VGG-16, our method achieves higher FLOPs reductions without utilizing time-consuming sensibility analysis. The compression ratio boosts from 34% to 53%, which is a huge improvement. Similar experiments with ResNet-56 reveal that, even for compact networks, our method achieves competitive compression performance even without skipping any sensitive layers.
A New Model for Emotion-Driven Behavior Extraction from Text

Emotion analysis is currently a popular research direction in the field of natural language processing. However, existing research focuses primarily on tasks such as emotion classification, emotion extraction, and emotion cause analysis, while there are few investigations into the relationship between emotions and their impacts. To address these limitations, this paper introduces the emotion-driven behavior extraction (EDBE) task, which addresses these limitations by separately extracting emotions and behaviors to filter emotion-driven behaviors described in text. EDBE comprises three sub-tasks: emotion extraction, behavior extraction, and emotion–behavior pair filtering. To facilitate research in this domain, we have created a new dataset, which is accessible to the research community. To address the EDBE task, we propose a pipeline approach that incorporates the causal relationship between emotions and driven behaviors. Additionally, we adopt the prompt paradigm to improve the model’s representation of cause-and-effect relationships. In comparison to state-of-the-art methods, our approach demonstrates notable improvements, achieving a 1.32% improvement at the clause level and a 1.55% improvement at the span level on our newly curated dataset in terms of the F1 score, which is a commonly used metric to measure the performance of models. These results underscore the effectiveness and superiority of our approach in relation to existing methods.
Optical High-Speed Rolling Mark Detection Using Object Detection and Levenshtein Distance

This paper presents an automated high-speed rolling mark recognition system for railroad rails utilizing image processing techniques. Rolling marks, which consist of numbers, letters, and special characters, were engraved into the rail web as 3D information. These rolling marks provide crucial details regarding the rail manufacturer, steel quality, year of production, and rail profile. As a result, they empower rail infrastructure managers to gain valuable insights into their infrastructure. The rolling marks were captured using a standard color camera under dark field illumination. The recognition of individual numbers, letters, and special characters was achieved through state-of-the-art deep neural network object detection, specifically employing the YOLO architecture. By leveraging reference rolling marks, the detected characters can then be accurately interpreted and corrected. This correction process involves calculating a weighted Levenshtein distance, ensuring that the system can identify and rectify partially misidentified rolling marks. Through the proposed system, the accurate and reliable identification of rolling marks was achieved, even in cases in which there were partial errors in the detection process. This novel system thus has the potential to substantially improve the management and maintenance of railroad infrastructure.
Trust Model of Privacy-Concerned, Emotionally Aware Agents in a Cooperative Logistics Problem

In this paper we propose a trust model to be used into a hypothetical mixed environment where humans and unmanned vehicles cooperate. We address the inclusion of emotions inside a trust model in a coherent way to the practical approaches to the current psychology theories. The most innovative contribution is how privacy issues play a role in the cooperation decisions of the emotional trust model. Both, emotions and trust have been cognitively modeled and managed with the Beliefs, Desires and Intentions (BDI) paradigm into autonomous agents implemented in GAML (the programming language of GAMA agent platform) that communicates using the IEEE FIPA standard. The trusting behaviour of these emotional agents is tested in a cooperative logistics problem where: agents have to move objects to destinations and some of the objects and places have privacy issues. The execution of simulations of this logistic problem shows how emotions and trust contribute to improve the performance of agents in terms of both, time savings and privacy protection
Benefits and drawbacks of OpenVPN in accessing Osiris in academic libraries

Purpose The aim of this study is to investigate the benefits and weaknesses of OpenVPN to access Osiris. Design/methodology/approach This study uses a case study approach to describe a phenomenon. The data collected from observation of the real-world environment. Findings The benefits of OpenVPN are the following: librarian can access the Osiris and other resources remotely and safely; OpenVPN protects the connection when accessing the Osiris; and librarian can also manage Osiris systems easily to control access, monitoring the users efficiently. However, there are some drawbacks of OpenVPN, such as network infrastructure dependency, require stabile internet connection, configuration complexity, to arrange and configurate OpenVPN needs skills and knowledge on the related field, data protection and security breach. Originality/value The originality of this study is the use of OpenVPN to access Osiris that is managed by library. There are some variables to determine the benefits and drawbacks of the OpenVPN such as managing access, data security and efficiency. This study gives an original contribution to the use of OpenVPN in academic libraries.
Influence of the Sphericity Coefficient on the Deposition Characteristics of Aerosol Particles on the Surface of Photovoltaic (PV) Modules: Numerical Simulation

The deposition of aerosol particles has a significant impact on the output capacity of photovoltaic modules. Therefore, studying the deposition characteristics of aerosol particles on photovoltaic modules is of great importance for improving their output capacity. Particle morphology is one of the important parameters affecting the deposition characteristics of aerosol particles. This study introduces the spherical coefficient as a quantification method for characterizing the morphology of aerosol particles. Numerical simulations using FLUENT 2022 software were conducted to investigate the influence of the spherical coefficient on the deposition characteristics of aerosol particles on photovoltaic modules. The reliability of the numerical simulations was further validated through experimental studies. Based on the research, the following conclusions can be drawn: the airflow velocity near the surface of the photovoltaic panel increases from bottom to top, with the lowest wind speed recorded near the ground at a minimum value of 2.2 m/s and a maximum value of 3.89 m/s. The air pressure near the surface of the photovoltaic panel shows a decreasing trend from bottom to top, with the highest pressure recorded near the ground at a maximum value of 10 pa and a minimum value ranging from 3.33~5.56 pa. During the deposition process, the accumulation of particles increases with an increase in the sphericity factor. Furthermore, as the sphericity factor gradually increases, the distribution of particles on the surface of the photovoltaic panel becomes more dispersed, covering the entire surface.