Reviewerly/exHarmony huggingface dataset:
Research on Computer Evaluation Model and Big Data Analysis of Green Reconstruction in the Index System of Intelligent Building

The article “Urban Renewal” with the transformation of old communities as an important content will become the main development path to improve the quality of the city and meet the needs of residents in the future. Starting from the five dimensions of cost, economic benefit, technical benefit, ecological benefit and social benefit, the article establishes and perfects the comprehensive benefit evaluation index system for the green renovation of old communities. At the same time, we combine big data with police work and social governance work. Taking the construction of smart communities in old communities as an example, analyse its measures and experience in mining and integrating public security governance resources, providing data support for public security governance, and improving the level of public security prevention in old communities. The evaluation results show that the green renovation of the old community has better comprehensive benefits than the traditional renovation.
Reconstruction and Upscaling of 3D Models from Single or Multiple Views

In recent years research related to 3D reconstruction from 2D images has gained traction and several approaches have been introduced. However, most conventional methods for 3D reconstruction are time consuming and tedious. Additionally, they produce low resolution results and have their own limitations. Our approach attempts to resolve these limitations by using a modified encoder-decoder architecture which generates a low resolution 3D coarse volume from a set of 2D images of an object. In order to improve the quality of the generated model, a pseudo high resolution 3D volume is generated by upsampling the low resolution volume which has multiple missing features. Parallelly, RGB-D images from different angles are generated using the Blender software. Furthermore, these RGB-D images are upscaled to high resolution images using a CNN-image upscaler and a depth map is extracted. These newly generated depth values assist in identifying the missing features from the pseudo 3D volume thereby generating a final high quality 3D coarse volume. Our results show that this approach outperforms the existing methods.
Sensor Fusion-Based Occupancy Detection Using Gaussian Awareness Network and Spatio-Temporal Fusion Approach

The importance of computer vision has grown drastically in many applications especially in development of smart cities, autonomous vehicles and medical fields. In this work, intelligent object detection-based computer vision is used in which the proposed system uses the Gaussian Awareness Network, a kernel-based Convolutional Neural Network (CNN) architecture, along with temperature and humidity sensors to provide better accuracy in predicting the occupancy. Gaussian Temporal Awareness Network (GTAN) and sensor fusion-based detection approach is proposed which can detect the static and dynamic movement of humans. The proposed model for occupancy detection is implemented by combining the detection output of proposed deep learning architecture by identifying key points based on feature maps, and human occupancy based on overlapping of prospective Gaussian kernels from the feature embeddings. The proposed system also incorporates the depth of objects in addition to the Spatio-temporal occupancy profile learning. The two main processes, GTAN-based occupancy profile and sensor-based occupancy profile processes run in a distributed fashion and the outputs from these are combined to produce more accurate detection results using weighted feature fusion algorithm. These results are communicated through IoT platforms and processed in master controller system thereby control the appliances in the occupant area. The proposed system is tested in real time environment inside the building of the institution and are proved to be more accurate in detecting the human occupancy. The proposed model is also validated using performance metrics namely Receiver Operating Characteristics Curve and Precision Recall Curve under different test conditions.
Design of TT&amp;C data Exchanging Ground System Based on Restrictive Publish/Subscribe Pattern

Focus on enhancing the TT&C data exchange rate and flexibility, this paper put forward a kind of TT&C data exchanging ground system based on restrictive publish/subscribe pattern. System architecture was designed, the core data exchange software was discussed, the data subscribe mechanism and data matching filter mechanism were also studied.
Densely Enhanced Semantic Network for Conversation System in Social Media

The human–computer conversation system is a significant application in the field of multimedia. To select an appropriate response, retrieval-based systems model the matching between the dialogue history and response candidates. However, most of the existing methods cannot fully capture and utilize varied matching patterns, which may degrade the performance of the systems. To address the issue, a densely enhanced semantic network (DESN) is proposed in our work. Given a multi-turn dialogue history and a response candidate, DESN first constructs the semantic representations of sentences from the word perspective, the sentence perspective, and the dialogue perspective. In particular, the dialogue perspective is a novel one introduced in our work. The dependencies between a single sentence and the whole dialogue are modeled from the dialogue perspective. Then, the response candidate and each utterance in the dialogue history are made to interact with each other. The varied matching patterns are captured for each utterance–response pair by using a dense matching module. The matching patterns of all the utterance–response pairs are accumulated in chronological order to calculate the matching degree between the dialogue history and the response. The responses in the candidate pool are ranked with the matching degree, thereby returning the most appropriate candidate. Our model is evaluated on the benchmark datasets. The experimental results prove that our model achieves significant and consistent improvement when compared with other baselines.
Machine Learning Neural Network Handwriting Recognition

The paper talks about one of the applications of machine learning, neural network handwriting recognition. For decades machine learning had shown its application in various things. Whether it is medical science, management companies, stocks or computer science. It is accurately shown results in the industries. Some applications even saved human time and money. From mathematical concepts to computer science, Machine learning has been applied to various things.
Handwritten Digits Recognition Based on Water Drop Algorithm and CNN

Handwritten digits recognition is an important research in pattern recognition and artificial intelligence, and it has broad application prospects in data processing. With the continuous development of deep learning, handwritten digits recognition technology has been widely improved. This work investigates the potential of the water drop segmentation algorithm and CNN in handwritten digits recognition, via deliberating the experiments on MNIST dataset. After selecting the appropriate starting point, specifying the moving rules and determining the direction, the water drop segmentation achieves excellent performance, especially for adhesion characters. The self-built neural network based on VGG NET which is realized with PyTorch framework is more targeted at the low-level features of the image and avoids the influence of the high-level feature factors on the pattern recognition results. Changing the multiple parameters of the network, this research is explicitly manifested, achieving the recognition accuracy of 0.99 on the handwritten digits data set MNIST.
Analysis of Twitter User’s Sentiment Against COVID-19 Vaccination Using the Lexicon Based Method

To accelerate the handling of the spread of COVID-19 in Indonesia, the Government of the Republic of Indonesia has issued a vaccination discourse for the people of Indonesia; as of January 20, 2021, there are more than 939.000 incidents and more than 26.000 incidents resulting in mortality. With the quick transmission of COVID-19 and the dangers it caused, the Indonesian government has taken precautions by vaccinating the people. This vaccination information spread on various social media, including Twitter which has a comment feature on its posts; it cannot automatically verify the volume of user sentiment regarding positive or negative comments. Sentiment analysis does partially of what text mining does for text splitting in knowing the polarity of an idea using a particular algorithm. It is positive or negative polarity. This study aims to provide results of public opinion on sentiment analysis of COVID-19 Vaccination using the Lexicon Based method. This research found that 95.4% of Tweets or messages related to Vaccination on Twitter are messages with neutral sentiments, 1.95% Slightly Positive, 1.81% positive sentiments, 0.56% Slightly Negative, and 0.28% are negative sentiments.
(De)composing the Mongrel

Improvisation is an attempt to transcend the self in a moment of intuitive action. It is an expression of knowledge beyond ordinary consciousness, the embodied wisdom of emptiness. Unlike composition, as a musical practice, improvisation is a way to express the depths of the (un)known and the knowing in the (un)knowing. As a framework for artistic research, improvisation can provide insight into knowledge that is embodied yet not easily articulated. In this article, I will document the development of my research practice through what I call (de) composition strategies: multi-modal approaches to improvisation that combine my experiences both as a musician and scholar. The article will mirror the multi-faceted nature of my work through interweaving academic discourse on consciousness and critical improvisation studies (Sarath 1996; Oliveros 2004; Fischlinn 2009) with musical examples, auto-ethnography, poetry and visual art inspired by dreams.
Dynamical Quantum Indistinguishability

We observe that quantum indistinguishability is a dynamical effect dependent on measurement duration. We formulate a quantitative criterion for observing indistinguishability in quantum fluids and its implications including quantum statistics and propose a viscoelastic function describing both long-time and short-time regimes where indistinguishability and its implications are operative and inactive, respectively. On the basis of this discussion, we propose an experiment to observe a transition to the short-time non-equilibrium state where the implications of indistinguishability become inoperative.
Transductive Zero-shot Learning with New Semantic Embeddings

Zero-shot learning (ZSL) is an important machine learning paradigm that trains models to handle samples from classes that are unseen during training. Most ZSL methods transfer knowledge about the seen to unseen classes using some semantic representations, such as annotated attribute vectors or word embeddings. However, such information is either difficult to acquire or lack of linkage to visual information. We thus propose a novel transductive ZSL paradigm that makes use of feature maps to distill less expensive but more informative semantic embeddings of seen and unseen classes. Experimental results on different datasets show that this method can significantly improve the performance compared to the latest ZSL models.
DABU-Net: Dilated Convolution and Attention U-Net with Boundary Augment for Medical Image Segmentation

U-Net has a good representation learning capability in medical image segmentation because it can extract contextual information from an image. However, U-Net also has two shortcomings. First, U-Net can only extract features on fixed scales, which limits its ability. Second, the feature maps from the same-scale encoder and decoder are semantically dissimilar, so that the shortcut connections of U-Net may cause semantic gap between the low- and high-level layers. In this paper, we propose a deep end-to-end network dubbed Dilated Convolution and Attention U-Net with Boundary Augment for Medical Image Segmentation (DABU-Net). In the encoding path, to increase the receptive field and capture the multi-scale information, we use dilated convolution to design the dilated convolution block (DCblock). In the decoding path, we design the spatial and channel attention block to narrow the semantic gap, and we use sobel operator to enhance the segmentation area. We evaluate the proposed network on three medical image datasets: TCGA Brain MRI dataset, LiTS 2017 liver segmentation dataset, and ISIC 2018 skin lesion segmentation dataset. Experimental results show that the DABU-Net has achieved better performance compared with other methods.
Deep learning for reliable detection of epileptogenic lesions

Deep learning techniques are ideally suited to find patterns in high-dimensional and complex imaging data, which means that it has the potential to change medicine and healthcare. In this chapter, we will highlight how deep learning can improve the lives of people living with treatment-resistant focal epilepsy by identifying brain surgery targets in this burdensome neurological disease. We will discuss topics that should be investigated to ensure a successful clinical translation of deep learning, by reliably detecting epileptogenic lesions—that is, targets for brain surgery—in high-dimensional brain imaging data such as magnetic resonance imaging or positron emission tomography. As deep learning techniques perform well with imaging data, we envisage that deep learning can support the identification of epileptogenic lesions and may therefore aid the presurgical process in treatment-resistant focal epilepsy.
Algorithm Research on Fuzzy Test Method of CAN Bus Based on Field Weight

The communication network in the vehicle, especially the CAN bus network, is under serious security threat. The security of CAN bus network can be improved by fuzzy test technology. At present, there is little research on the fuzzy test technology of automobile can bus. The test case generation method mostly adopts the random generation method, without considering the application layer protocol format of CAN bus, which reduces the passing rate of CAN bus message, It affects the efficiency of fuzzy testing. This paper compares the advantages and disadvantages of the fuzzy testing algorithm based on the improved wgan-gp and the fuzzy testing algorithm based on the field weight. Both algorithms can find the vulnerabilities in the CAN bus and affect the vehicle to produce abnormal response. The fuzzy testing algorithm based on the improved wgan-gp has shorter discovery time and response time and better effect.
Disentangled Representation Learning from Videos for Facial Action Unit Detection

Facial Action Unit (AU) detection is a challenging task for the reason that AU features extracted from videos always entangle other inevitable variations, including head posture motion characteristics, which is even much more intense than facial actions, and individual facial features because of race, age, gender or face shape. These AU-unrelated features would adverse the AU detection performance. To achieve better performance of AU detection, we proposed a novel Feature Disentangled Autoencoder (FDAE) to learn more discriminative facial action representation from large amounts of videos. Different from previous approaches, FDAE disentangled AU-related features, head posture motion characteristics and identity code characteristics to eliminate the impact of irrelevant factors. At the same time, we added two classifiers to discriminate the AU embedding and identity code embedding respectively to accelerate training process and make the model more stable and robust. Experiments on BP4D and DISFA demonstrated that the learned representation is discriminative, where FDAE outperformed or was comparable with existing representation learning method for AU detection.
Throughput Prediction by Radio Environment Correlation Recognition Using Crowd Sensing and Federated Learning

We propose an approach using federated learning for predicting Wi-Fi and LTE transmission control protocol (TCP) throughput to reduce the delay between the output of prediction results and the problem of security risks by sharing the datasets, which is a problem with conventional machine learning methods. The proposed method collects measurement datasets such as received signal strength index from distributed edge devices. Then, a shared learning model is created using the measured dataset on the server. The created model is retrieved by edge devices at any time and used to predict TCP throughput. To evaluate the effectiveness of the proposed method, we perform the emulation evaluation using measured datasets obtained in a real environment. The emulation results reveal that the proposed method can skillfully predict the TCP throughput in the realistic communications. Additionally, the prediction accuracy of the TCP throughput can be improved by creating a learning model for each network area.
Human Pose Estimation Based on Multi-branch Convolutional Neural Network

With the development of the convolutional neural network, significant progress has been made in human pose estimation. Recent human pose estimation approaches adopt a single branch network, which can only provide limited surveillance information. Aiming at the problem, we propose to adopt a multi-branch network for both 2D human pose estimation and 3D human pose estimation. Leading results on multiple datasets verify the effectiveness of our multi-branch network.
An Evaluation of Best Practices in an Air Quality Student Science Project in Ethiopia

A science project as part of a student’s curriculum, which we call student science, might be the answer to two problems for a low-income country like Ethiopia: conventional science can be expensive and many students lack training in practical skills. Earlier studies have been conducted with respect to lay people (citizens or students) conducting (parts of) research (e.g. citizen science), but rarely in the context of a public university in a low-income country. A student science project at Arba Minch University (Ethiopia) has been evaluated in three steps. Firstly, best practices for student science projects are derived from the literature. Secondly, it is evaluated to what extent these best practices were executed in an air quality student science project executed by 33 groups of undergraduate students at Arba Minch University. Thirdly, the scientific contribution of the project is evaluated by assessing the quality of the data in comparison to studies in similar scenarios, as well as its relation to a knowledge gap and a problem for the community. We find that the best practices from earlier studies are feasible in the study context. Furthermore, we find a scientific contribution, as most of the students’ work resulted in quality data that relates to knowledge gaps which are a problem for the Arba Minch community. Student science at a public university in a low-income country is feasible and can, as such, serve both scientific and educational needs. It is recommended that public universities in low-income contexts implement part of their curriculum goals in such projects.
A Comparative Study on Utilization of Semantic Information in Fuzzy Co-clustering

Fuzzy co-clustering is a technique for extracting co-clusters of mutually familiar pairs of objects and items from co-occurrence information among them, and has been utilized in document analysis on document-keyword relations and market analysis on purchase preferences of customers with products. Recently, multi-view data clustering attracts much attentions with the goal of revealing the intrinsic features among multi-source data stored over different organizations. In this paper, three-mode document data analysis is considered under multi-view analysis of document-keyword relations in conjunction with semantic information among keywords, where the results of two different approaches are compared. Fuzzy Bag-of-Words (Fuzzy BoW) introduces semantic information among keywords such that co-occurrence degrees are counted supported by fuzzy mapping of semantically similar keywords. On the other hand, three-mode fuzzy co-clustering simultaneously considers the cluster-wise aggregation degree among documents, keywords and semantic similarities. Numerical results with a Japanese novel document demonstrate the different features of these two approaches.
Establishment of Japanese Continuous Speech Recognition System Based on Association Rules Mining

Voice technology is to make these devices and machines recognize and understand correctly through a series of processing of human voice signal information. With the increasing application demand of speech recognition, speech recognition technology is gradually becoming the key technology in human-computer interaction. In this paper, the technology based on ARM (Association Rule Mining) is applied to the establishment of Japanese continuous speech recognition system. Considering the characteristics of Japanese hiragana, katakana and Japanese Chinese characters, the effects of different modeling units on recognition performance are discussed through experiments on Japanese data sets. On this basis, Apriori algorithm and FP-growth algorithm are used to extract speech emotion prosodic features, and the advantages and disadvantages of these two algorithms are analyzed and compared from the number of effective rules generated by the algorithm and the running time, and finally Apriori algorithm is selected to extract speech emotion prosodic features.
An Investigation of Hidden Shared Linkages Among Perceived Causal Relationships in Cognitive Maps

This study investigates cause and effect relationships in cognitive maps and the coexistence of pairs of such relationships in cognitive maps of a chosen group of decision-makers. We call the existence of a pair of causal relationships shared by the group of decision-makers in their cognitive maps inter-causal relationship. We investigate the coexistence of the chosen pairs of causal relationships in the maps in terms of one of the causal relationships being a necessary and/or sufficient condition for the existence of the other using the tools of fuzzy-set qualitative comparative analysis. We develop and propose a framework to extract and examine the inter-causal relationships from the cognitive maps. The proposed method is based on set-theoretic consistency and coverage measures. We used empirical data (of 71 cognitive maps) collected from a cognitive mapping approach performed by individuals in management teams within a strategic decision-making simulation process to test the proposed approach. Empirical results show that our method can identify inter-causal relationships and provide analytic results for a more complex interpretation if the information arises from the structure of cognitive maps.
Autonomous Bidding &amp; Coordinated Acceptance in One-to-Many Negotiations

This work presents the Autonomous Bidding & Coordinated Acceptance framework (ABCA): an agent-team design that allows general bilateral agents to engage in oneto-many negotiations in a setting where (possibly overlapping) deals with multiple opponents are desirable. We propose also a coordinated acceptance strategy that uses the estimated outcomes of its bilateral negotiations while deciding to accept a deal.