Reviewerly/exHarmony huggingface dataset:
The Regional Rooftop Photovoltaic Potential User Mining and Power Generation Capacity Evaluation based on the Remote Sensing Image Recognition

The rooftop photovoltaic power generation system can convert solar energy into electric energy so as to realize the clean and efficient energy use. To promote the photovoltaic power generation system construction and mine more photovoltaic potential users, this paper proposes a regional rooftop photovoltaic potential user mining method based on the image recognition technology. This method uses the image target recognition technology of the regional high-resolution remote sensing satellite image to extract the roof area information, so as to locate the potential user group and evaluate the regional rooftop photovoltaic power generation capacity. To achieve accurate object recognition, this paper proposes a boundary smoothing-based filtering method for the image prepossessing and uses maximum between-class variance method (OTSU method) based on the gray-scale image threshold for the target recognition. Finally, the effectiveness of the proposed method is verified by the example analysis.
Research on the Algorithm with or without Anchor Frame

Target detection is the core and one of the most challenging problems in the field of computer vision research, and it is also a popular direction in computer vision and digital image processing. It is widely used in robots and car navigation, industrial inspection, aerospace, vehicle obstacle avoidance and other scenes. Human consumption can also be reduced by computer vision. With the widespread application of deep learning technology, the efficiency and accuracy of target detection are gradually improved, and it is also for subsequent tasks, such as face recognition, gait recognition, crowd counting, and semantic segmentation. Waiting plays an important cornerstone role. In some respects, it reaches or exceeds the resolution level of the human eye. With the development of computing power and deep learning, the current environment puts forward new requirements for target detection, which improves the accuracy of small target detection. Therefore, this article compares the target detection algorithms with or without anchor frames, analyzes and compares various algorithms, looks forward to the future development trend of the target detection field, and several important directions that can be studied.
Team Characteristics Moderating Effect on Software Project Completion Time

This study highlights the importance of human factors in software projects developed in capability maturity model (CMM) level software development environments. While software process initiatives help streamline the development process, people factors can influence project outcomes. Using data procured from the International Software Benchmarking Standard Group, the effects of team turnover, team heterogeneity, and team member work experience were examined as they moderate project elapsed time for software projects developed in CMM level software development environments. Team member work experience and team functional heterogeneity were found to have significant moderating effects on project elapsed time to completion. The turnover of members on the team did not have a significant moderating effect on project elapsed time to completion. Previous studies have examined the benefits of raising the level of maturity as identified by the capability maturity model. This study identifies the importance of human factors as they moderate project success.
Overview

This chapter examines the potential value of multiagent systems (MAS) technology to present-day applications. In terms of contribution, it describes fundamental concepts and approaches within the field of multiagent systems that are appropriate to engineering applications. Besides presenting a comprehensive review of the meaningful engineering applications for which MAS are being investigated, it also defines the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector.
Transformer based multi-head attention network for aspect-based sentiment classification

Aspect-based &lt;span&gt;sentiment classification is vital in helping manufacturers identify the pros and cons of their products and features. In the latest days, there has been a tremendous surge of interest in aspect-based sentiment classification (ABSC). Since it predicts an aspect term sentiment polarity in a sentence rather than the whole sentence. Most of the existing methods have used recurrent neural networks and attention mechanisms which fail to capture global dependencies of the input sequence and it leads to some information loss and some of the existing methods used sequence models for this task, but training these models is a bit tedious. Here, we propose the multi-head attention transformation (MHAT) network the MHAT utilizes a transformer encoder in order to minimize training time for ABSC tasks. First, we used a pre-trained Global vectors for word representation (GloVe) for word and aspect term embeddings. Second, part-of-speech (POS) features are fused with MHAT to extract grammatical aspects of an input sentence. Whereas most of the existing methods have neglected this. Using the SemEval 2014 dataset, the proposed model consistently outperforms the state-of-the-art methods on aspect-based sentiment classification tasks.&lt;/span&gt;
Language-Independent Type Inference of the Instances from Multilingual Wikipedia

Extracting knowledge from Wikipedia has attracted much attention in recent ten years. One of the most valuable kinds of knowledge is type information, which refers to the axioms stating that an instance is of a certain type. Current approaches for inferring the types of instances from Wikipedia mainly rely on some language-specific rules. Since these rules cannot catch the semantic associations between instances and classes (i.e. candidate types), it may lead to mistakes and omissions in the process of type inference. The authors propose a new approach leveraging attributes to perform language-independent type inference of the instances from Wikipedia. The proposed approach is applied to the whole English and Chinese Wikipedia, which results in the first version of MulType (Multilingual Type Information), a knowledge base describing the types of instances from multilingual Wikipedia. Experimental results show that not only the proposed approach outperforms the state-of-the-art comparison methods, but also MulType contains lots of new and high-quality type information.
Chinese Opinion Target Extraction Based on MRC Framework

In this paper, we formulate the Opinion target extraction (OTE) task as a machine reading comprehension (MRC) task. By formalizing the task of OTE as extracting answer spans to the question “Which opinion target is mentioned in the comment text?”, the prior semantic knowledge of the opinion target is introduced to improve the effect of OTE. We conduct experiments on the method proposed in this paper on pre-trained language models such as Bert and use the Lexical Analysis of Chinese (LAC) lexical analysis tool to correct the results predicted by the model. We conduct experiments on the Chinese evaluation object extraction datasets Baidu, Mafengwo, and Dianping, and the optimal F1 scores were 91.42%, 91.33%, and 93.72%, respectively. The experimental results show that the Pre-trained Model-MRC-LAC Model proposed in this paper can effectively improve the extraction effect of Chinese opinion target extraction.
Comparative Analysis and Empirical Research on the Development Status of Innovation Credits System

Innovation credits are an important part of college students' innovation and entrepreneurship education. It is of great significance to construct and perfect the innovative credits system to implement the spirit of the state, the Ministry of Education, and other departments, and to encourage the innovation and entrepreneurship practice of college students. This study selects 4 domestic universities to conduct horizontal comparison of innovative credits system, and analyzes the current situation of college students' innovative credits system construction based on the relevant survey data of Xiamen University, and puts forward suggestions for improvement.
A Multi-stream Fusion Network for Multi-task Image Restoration

Image contains a lot of effective information. However, due to the limitation of image acquisition technology and the interference of acquisition environment, motion and other factors, the acquired image will appear multiple distortion problems simultaneously, such as low resolution, motion blur and noise, which affect our acquisition of information. Therefore, it is an important task to solve the multi-distortion problem, so as to obtain a high-resolution, noiseless and clear image. In this paper, we propose a multi-stream fusion network (dubbed MSFNet) to deal with the multi-distortion problem of deblurring, denoising and super-resolution. Each stream of the proposed method handles a task individually to strengthen the optimization of the corresponding task. A multi-feature fusion module is used to establish the relationship between different tasks, which realizes the joint optimization of multi-tasks. Experimental results show that the proposed MSFNet achieves better quantitative and qualitative performance than the two-stage image restoration method.
Study on Strain-measuring of High-speed Bowl of Milk-Separator Based on Digital Image Correlation

Due to the high-speed rotation of the bowl of the disc-separator, the domestic study has not been able to solve the problem of actual measurement of its strain. In order to investigate the feasibility, reliability and application effect of the non-contacting measurement of strain of the high-speed bowl by the digital image correlation (DIC). Using the principal strain of the bowl shell of the milk separator in the rotating state, and measure the strain values of each point along the circumferential and axial directions of the conical and cylindrical of the bowl under no-load and water-loaded conditions, and the strain distribution law curves are drawn and analyzed. The shortcomings of high-speed slip-ring initiator and wireless telemetry are analyzed, and the working principle of DIC and the test procedure are discussed. The test results show that: DIC is used for high-speed bowl strain measurement. The technology is feasible and effective, easy to realize, and has technological advancement and reliability in disc-separator. The application of this technology to the disc-separator will provide effective data support for simulation model verification, structural optimization, strength assessment, and has important theoretical and engineering practical significance.
Proactive information retrieval

Users interact with digital systems with some underlying tasks in their minds. In our research scope, a task can be either single or multi-staged. A single-staged task is associated with a single information need, whereas a multi-staged one is associated with more than one information needs. An example of a single-staged task is suggesting related papers to an author while they are writing a section of the research papers. An example of a multi-staged task is planning for a vacation, where the different underlying information needs could be 'places to visit', 'booking accommodation' etc. In the process of accomplishing their task objectives, a user often needs to interact with an information retrieval (IR) system to address one or more information needs. For instance, for writing a research paper on a chosen topic, a user needs to look for existing research work related to the topic. Traditional IR systems do not take into account a user's task intent while showing search results to the user. In our work, we propose a methodology towards developing next generation IR systems (i.e. proactive IR systems) which seek to anticipate the task intent of a user from their interactions with digital systems in order to proactively suggest potentially relevant information sources to assist them to complete their tasks. Specifically speaking, in this PhD, we proposed an embedding approach that captures the task semantics from the interactions of a user with a digital system (e.g. laptop, desktop, smartphone etc.). The proposed embedding approach is then applied for the downstream tasks of providing proactive suggestions in both single and multi staged scenarios. For the single-staged task, we propose a simulation setup to simulate a user's reading and writing interactions in a desktop environment. For the multi-staged task, we focus on web search sessions where a user can have multiple information needs corresponding to a search task. We also proposed a reproducible evaluation framework to compare between different proactive suggestion models. Awarded by : Dublin City University, Ireland on 10 August 2021. Supervised by : Gareth Jones. Available at : https://procheta.github.io/sprocheta/Thesis.pdf.
Flotation model parameter selection criteria for safe water purification modes

The paper deals with criteria for selecting parameters of the flotation model providing safe modes of water purification. It presents the method of solving water purification problems by flotation through numerical methods. The obtained solution of the differential equations system makes it possible to continue research works on creation of optimal safety regimes for water purification. The diagrams of obtained solutions in this work qualitatively match those obtained earlier by the solution authors. In the authors' opinion, it proves, on the one hand, the correctness of the chosen approach and, on the other hand, the achievement of high accuracy. At the same time, quantitative evaluation criteria for the chosen method can be recommended when solving specific tasks of flotation purification of both natural and waste water. The results obtained correlate well enough with the experimental data and can be used to control the flotation wastewater processes with the possibility of using stable and in this sense safe purification modes. In this case, the adequacy is observed when carrying out flotation treatment of various types of contaminated wastewater, both with hydrophobic and hydrophilic surface, as well as particles with a surface that includes alternating water repellent and water-attracting zones, and, in addition, processes forming aerofloccules. In these cases, the flotation process description may include systems of hydrophilic and hydrophobic particles. The approach used allows determining the flotation wastewater purification mode which is safe from the point of view of stability.
Spatiotemporal Perturbation Based Dynamic Consistency for Semi-supervised Temporal Action Detection

Temporal action detection usually relies on huge tagging costs to achieve significant performance. Semi-supervised learning, where only a small amount of data are annotated in the training set, can help reduce the burden of labeling. However, the existing action detection models will inevitably learn inductive bias from limited labeled data and hinder the effective use of unlabeled data in semi-supervised learning. To this end, we propose a generic end-to-end framework for Semi-Supervised Temporal Action Detection (SS-TAD). Specifically, the framework is based on the teacher-student structure that leverages the consistency between unlabeled data and their augmentations. To achieve this, we propose a dynamic consistency loss by employing an attention mechanism to alleviate the prediction bias of the model, so it can make full use of the unlabeled data. Besides, we design a concise yet valid spatiotemporal feature perturbation module to learn robust action representations. Experiments on THUMOS14 and ActivityNet v1.2 demonstrate that our method significantly outperforms the start-of-the-art semi-supervised methods and is even comparable to the fully-supervised methods.
Performance Evaluation of Azure Kinect and Kinect 2.0 and Their Applications in 3D Key-Points Detection of Students in Classroom Environment

Kinect, which has the functions of depth data acquisition and human skeleton 3D key-points detection, is one of the most popular consumer-grade depth data acquisition devices in the past decade. In 2019, Microsoft released the latest generation of Kinect (Azure Kinect). The contribution of this paper is to design experiments to evaluate the performance of this sensor in depth data acquisition and human skeleton 3D key-points detection. In particular, we explored the effect of Azure Kinect and Kinect 2.0 in 3D key-points detection of students in the classroom environment. In order to qualitatively compare their performance in the detection of human skeleton 3D key-points in classroom environment, we conducted a multi-person experiment in the classroom environment. The experimental results show that: (1) Azure Kinect is better than Kinect 2.0 in depth data acquisition accuracy; (2) The performance of Azure Kinect is better than Kinect 2.0 in acquiring depth data at different distances; (3) In terms of human 3D key-points detection accuracy, the difference between Kinect 2.0 and Azure Kinect is not big; Under the condition that the detected people are occluded, the detection effect of Azure Kinect is better than Kinect 2.0; (4) In the experiment of multi-person 3D key-points detection, Azure Kinect has better performance than Kinect 2.0; Azure Kinect is more accurate than Kinect 2.0 in assigning ID to the tested person.
Extract It! Product Category Extraction by Transfer Learning

The categorization of e-commerce products is salient as its quality directly affects search, recommendations, and related personalized services. Putting the products into the best possible category in a hierarchical manner divided by subcategories is challenging due to the vast range of products creating complexity in product information to select suitable categories. Earlier research proves inefficient with a smaller dataset, so we propose a model to extract the most relevant information from the product description and a pre-trained vocabulary to transform it into subcategories for the prediction of the product category tree. A minute inaccuracy, in this case, can hamper customer satisfaction while searching for the products in the desired categories. To tackle these challenges, we merge the areas of machine learning and deep learning with natural language processing (NLP) to propose a multi-level-based product categorization model. We implemented a selective approach starting with the product name being the node of the tree to recursively form a hierarchical tree structure, searching and extracting subcategories from the product description and a pre-trained vocabulary, which eventually made us predict the most relevant categorization of products. Basically, it is a process of extraction of subcategory levels from the product description and a pre-trained vocabulary built by transfer learning. The proposed model was tested on a Flipkart product dataset containing 20,000 products with several features describing the product. The cosine similarity between the predicted and the given product category tree computed was 0.77, which takes the accuracy to 86%.
Improving Accuracy of crime data using K-Means and Decision Tree Techniques

The data is growing exponentially and the accurate data mining has become a crucial task to perform. Out of different type of data, crime data is increasing at high pace due to the immense occurrence of physical and cybercrimes in different origins of the world. Also, the accuracy in this data is quite important. There are many machine learning algorithms has been used by the researchers to achieve high accuracy. In this paper, we have shown the performance comparison of different classification and clustering techniques. We have used K-means clustering and Decision tree classification on dataset and have shown that the combination provides more accurate result as compared to the individual K-Means.
Correct the distribution by augmenting feature for few-shot learning

Few-shot classification is a challenge in deep learning, the purpose is to learn a good classifier through the limited samples. Since the number of samples for learning is small, the learned classifier is easily overfitted. In this paper, to address the problem we propose an effective few-shot-learning method. Our contributions are three-fold: 1) the pre-training model on the base class (the sufficient examples) is adaptively generated by the manifold-mixup and self-supervision training way of rotation. 2) we adopt the features of the novel class to fine-tune the last layer of the model. 3) Using the mean and covariance of the base class features, we design a novel feature augmentation method to estimate the feature distribution of the novel class to further improve the performance of our few-shot-learning model. Experiments on benchmark datasets show that our method is effective, and it can achieve the better results.
A Test Suite for the Evaluation of Portuguese-English Machine Translation

This paper describes the development of the first test suite for the language direction Portuguese-English. Designed for fine-grained linguistic analysis, the test suite comprises 330 test sentences for 66 linguistic phenomena and 14 linguistic categories. Eight different MT systems were compared using quantitative and qualitative methods via the test suite: DeepL, Google Sheets, Google Translator, Microsoft Translator, Reverso, Systran, Yandex and an internally built NMT system trained over 30 h on 2,5M sentences. It was found that ambiguity, named entity & terminology and verb valency are the categories where MT systems struggle most. Negation, pronouns, subordination, verb tense/aspect/mood and false friends are the categories where MT systems perform best.
Evaluation and Customized Support of Dynamic Query Form Through Web Search

New sorted out web databases keep up enormous and assorted information and these genuine databases incorporate more than hundreds or even a colossal number of relations and characteristics. Normal predefined request structures are not prepared to satisfy distinctive uncommonly selected inquiries from customers on those databases. Dynamic Question Structure (DQF) is a novel database request structure interface, which can effectively make question shapes. The focal thought of DQF is to get a customer's tendency and rank request structure parts, profitable anyone to choose. The making of an inquiry structure is an iterative strategy and is guided by the customer. At each accentuation, the system subsequently makes situating courses of action of structure fragments and the customer by then incorporates the favored structure parts into the request structure. The situating framework relies upon the got customer inclination. A customer can correspondingly stack the request structure and submit request to see the inquiry result at each accentuation. Thusly, a request structure could be viably refined till the customer satisfies with the inquiry results. A probabilistic model is made for measure the respectability of an inquiry structure in DQF. Exploratory appraisal and customer study demonstrate the sufficiency and adequacy of the structure are finished up with an experimentation results.
A Multi-scale Framework for Visual Grounding

Visual Grounding aims to ground the right region of the image according to the query that describes the target. Generally, this task can be formalized as selecting the target with the highest matching score between the image regions and the language queries. This task is based on object detection, word embedding and multi-modal data fusion. At present, this task has a wide range of needs in real life. For example, it is necessary to search the matching content of surveillance video based on text information to check whether certain violations have occurred. Based on the current advanced one-stage algorithm ReSC, this paper studies the algorithm details of the object detection algorithm, word embedding and multi-modal data fusion. On this basis, this paper exploits multi-scale features to improve performance.