Reviewerly/exHarmony huggingface dataset:
Low Light Image Enhancement for Color Images Combined with Sky Region Segmentation

To solve the problems of low contrast and blurred details in low light images, a brand-new low light image enhancement algorithm combined with sky region segmentation is proposed for color images in this paper. In the proposed algorithm, the image enhancement is processed in HSV space to achieve color consistency. An adaptive global enhancement method is adopted in brightness enhancement. In addition, a simple nonlinear transformation is applied on the saturation component combined with sky region segmentation. Experimental results verify the effectiveness of the proposed algorithm, and reveal the proposed algorithm could improve the brightness, the contrast and details of low light images, while maintaining the color consistency to make the images more in line with the visual effects of human eyes.
Tourgether360: Exploring 360° Tour Videos with Others

Contemporary 360° video players do not provide ways to let people explore the videos together. Tourgether360 addresses this problem for 360° tour videos using a pseudo-spatial navigation technique that provides both an overhead “context” view of the environment as a minimap, as well as a shared pseudo-3D environment for exploring the video. Collaborators appear as avatars along a track depending on their position in the video timeline and can point and synchronize their playback. In this work, we describe the intellectual precedents for this work, our design goals, and our implementation approach of Tourgether360. Finally, we discuss future work based on this prototype.
Improved Scan Path Model Using Visual Saliency Dataset

Based on PathGAN network, this paper improves it in many aspects, and obtains a more excellent deep neural network for detecting scan path. Scan path is a sequence of fixation points generated by human eyes on stimuli such as images or videos in scene exploration tasks. In this paper, we use the line of sight landing point data on the visual saliency dataset mit1003 to convert it into scan path. Then, the scan path is used in the improved PathGAN for training, and the scan path generated by the model is more diverse. At the same time, the new scan path representation method is used to increase the information of scan path, which can be used to predict the scan path of stimulating pictures in other datasets, and expand the scope of application of the model.
Realize English Teaching Optimization Based on B/S System

In today’s era of rapid development of science and technology, people want to have more portable devices for learning and work in life and study. For this, an English learning system based on Android is proposed. This article first introduces the relevant technologies of the Android system and gives a detailed introduction according to the characteristics, architecture, development type, life cycle, network communication technology, and database technology of the Android platform and studies the functional requirements and overall architecture design of the English learning system. The detailed design and development of the user management module, online examination module, online word search module, and online video module are carried out in accordance with the functional requirements, and the detailed design and implementation methods of the user management module, online examination module, online word search module, and online video module are designed. It is tested proof that the Android-based English learning system has user learning.
Redactable Blockchain Trust Scheme Based on Reputation Consensus for MEC

Blockchain technology can build trust, reduce costs, and accelerate transactions in the mobile edge computing (MEC) and manage computing resources using the smart contract. However, the immutability of blockchain also poses challenges for the MEC, such as the smart contract with bugs cannot be modified or deleted. We propose a redactable blockchain trust scheme based on reputation consensus and a one-way trapdoor function in response to the problem that data on the blockchain, which is an error or invalid needs to be modified or deleted. The scheme calculates each user's reputation based on their currency age and behavior. The SM2 asymmetric cryptography algorithm is used as the one-way trapdoor function to construct a new Merkle tree structure, which guarantees the legitimacy of the modification or deletion after verification and vote. The simulation experiments show that the modification or deletion does not change the existing blockchain structure and the links of blocks. Furthermore, the consensus verification accurately passes after the modification or deletion operations, which indicates the proposed scheme is feasible.
Calmbots: Exploring Possibilities of Multiple Insects with On-hand Devices and Flexible Controls as Creation Interfaces

We introduce ”Calmbots” that are insects-based interfaces comprising multiple functions, i.e., drawing, display, transportation, or haptics. We explore possibilities of multiple insects as co-creation interfaces making artworks through insects indirectly, in contrast with BioArt where artworks are made by biological technology. Considering scalability, sustainability, or promotability of applying insects-based devices on creation activities in daily life situations, we utilize on-hand and efficient control system by AR markers and radio-based station, and propose efficient ways of controlling multiple insects reaching goals and transporting objects, and customize flexible option parts. In robust experimental trials, our results showed effective control on a group of three or five cockroaches with at-least 60% reaching accuracy, mobility on carpeted or cable-lines floor, and possible continuous control under certain time duration. Participants in a user study felt positive with Calmbots’ functions and expected more performance or appearance on activities or creation in daily life.
Analysis on the Relationship between Convolutional Neural Network Layers and Model Degradation

The solution space of the deeper network structure includes the solution space of the shallow network structure, which means the deeper network structure shall get better solutions, and the performance shall also be better than the shallow network. The deep network may be worse than the shallow network either in training or test errors because of degradation. This paper starts with a set of experiments with convolutional neural networks (CNN) trained on pre-trained word vectors for a regression task to assess the Humor in Edited News Headlines. Our research aims to find the influence of the number of convolutional layers on the convolutional neural network and find a model performance best to solve the task. Try to tunning hyperparameters first and then modify the number of layers. The result is that different layers give different results, and it is not the most complex model that produces the best results. When the number of model layers increases, it encounter a degradation problem. Previous research has focused more on solving degradation. Our research is more inclined to explore the regular degradation pattern and find the appropriate number of layers. The final experimental results show that the gradient decline with the increase of the number of layers and time is not very good, and the phenomenon of degradation is encountered. The experiment found the regular pattern of the number of layers and degradation to a certain extent.
Faithful coherent states

We propose the notion of faithful coherent states based on the fidelity-based coherence witness. The criterion for detecting faithful coherent states can be restricted to a subclass of the fidelity-based criterion under unitary transformations for single and bipartite systems. We can realize these unitary transformations by using quantum gates and circuits, and establish the connection between faithful coherence states and coherence distillation as well as maximum relative entropy of coherence.
Aspect-level sentiment analysis model incorporating part-of-speech

Aspect-level sentiment analysis aims to identify the sentiment polarity of various aspects in the text. Existing deep learning models generally use the syntactic dependency tree as the model input when extracting the grammatical information of the text. In this paper, we argue that the part-of-speech sequence of the text carries certain grammatical information. We concatenate the word vector and the part-of-speech vector and inputs it into Bi-directional Long Short-Term Memory (Bi- LSTM), and then combined the aspect-specific masking method and attention mechanism for sentiment analysis. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.
A Novel Block-wise Attention Method: Focus-and-Association Networks

Deep residual networks have the problem of diminishing feature reuse, meaning that as the network depth increases, the accuracy does not increase linearly but gradually becomes smooth. Recent work has demonstrated that in addition to increasing the depth, the performance of networks can also be improved by explicitly embedding learning mechanisms. A broad range of prior research has investigated channel and spatial attention mechanisms to strengthen the representational power of convolutional neural networks (CNNs). In this paper, we focus instead on the block relationship. Inspired by the human visual attention mechanism, we propose a novel block-wise attention module, which we term the "focus-and-association" (FA) module, that performs dynamic block-wise feature recalibration by explicitly modelling interdependencies between blocks. We show that FA module can be applied to various existing state-of-the-art CNNs with few additional parameters and slight computational burden, and it effectively generalizes across different datasets. Extensive experiments show the effectiveness of the focus-and-association network (FANet). In addition, we further demonstrate the compatibility of the FA module by combining it with the existing state-of-the-art embedded algorithm unit, and achieve further improvements in accuracy. Finally, we visualize part of the details from the network to further explore the mechanism of action of the FA module.
Research on Defect Recognition of Ceramic Chips for High Temperature Fuel Cells Based on Improved Faster R-CNN

Defect detection of high temperature ceramic chips is an important part of fuel cell production and quality management. Therefore, considering problems that ceramic chip itself is complex and there is not sufficient existing defect detection technology, a defect detection method of ceramic chip based on improved Faster R-CNN is proposed in this paper. After the data is first enhanced, the feature pyramid network structure (FPN) will be introduced into the backbone network, thus generating feature maps suitable for multi-scale target detection to strengthen the feature extraction ability of small target defects. Then, the ROI Align algorithm is used to replace the ROI Pooling algorithm, the maximum pooling operation is performed to obtain a fixed-dimensional ROI output so that more accurate defect location information can be gained. Experimental results show that compared to SSD, Faster R-CNN deep learning model and YOLOv5 model based on VGG-16, ResNet-50, ResNet-101 feature extraction network, the improved model converges fast and detects better in small target defects. In addition, compared with the original model based on VGG-16, its detection accuracy, recall rate and mAP value are respectively increased by 4.46 %, 2.46 % and 6.78 %, meeting the requirements of detection tasks.
AcousticType: Smartwatch-Enabled Cross-Device Text Entry Method Using Keyboard Acoustics

The integration of various technologies with our daily lives, such as smartwatches, smartphones, smart TVs, etc. has proven beneficial. However, the HCI community is still in search for more intuitive text entry methods for smart devices. In this study, we explore a new text entry method—AcousticType for smart devices where a smartwatch is leveraged to infer what a user is typing on a physical keyboard employing the acoustic signals as a secondary input mechanism. AcousticType employs four modules: Noise Cancelling, Keystroke Detection, Key Identification and Word Correction. Our results show AcousticType recovers up to 98% of the typed text. We also showed that utilizing the noise cancellation, AcousticType is robust to the changes in the environment, which further boosts the practicality. The findings are promising and call for further investigation into new types of text entry methods that utilize acoustic signals to cope with the usability issues in smart devices.
Memory Portal: Investigating Data Meaning-making through Spatio-temporal Experiences

The data collected in shared spaces are usually used to understand how occupants use the space. However, these data could also inform the experience of the occupants in these spaces over time. How could we leverage spatial and temporal memories as mediators among occupants of a shared physical space? We developed Memory Portal, an interactive mirror that reflects the shadows and sounds of past occupants of a shared physical space in the present. We conducted a short user study to explore how the occupants of a shared space perceive the interactions with the reflected memories. One of our key finding is that the occupants formed a novel perspective about their actions and experience in the space through these memories. Our contributions include introducing a novel technical system and new approaches of data engagement in shared physical spaces through the visualization and sonification of memories of the presence of its occupants.
Towards Conversationally Intelligent Dialog Systems

Spoken dialog systems, lacking the means to address the complex phenomena of spontaneous speech and conversational dynamics, force users into a constrained mode of dialog that resembles text-based interaction more closely than spoken conversation. Turn-taking is simplified and discourse-related information is lost, as discourse markers are largely ignored and prosodic information is not captured or utilized. We hypothesize that incorporating a few of these key conversational phenomena at specific points in a dialog will reduce cognitive load in spoken human-computer interaction and expand the potential application areas of dialog systems to tasks requiring more complex interactions. In this paper, we describe our approach to adding conversational intelligence to dialog systems and our work to date validating the hypothesis that adding conversational intelligence to existing dialog systems will significantly reduce users’ cognitive load.
Database Development Based on Deep Learning and Cloud Computing

In this research, the author develops databases based on deep learning and cloud computing technology. On the basis of designing the overall architecture of the database with the distributed C/S mode as the core, use J2EE (Java 2 Platform, Enterprise Edition) as the development tool, apply Oracle server database, extract data features with in-depth learning technology, allocate data processing tasks based with cloud computing technology, so as to finally complete data fusion and compression. Finally, the overall development of the database is completed by designing the database backup scheme and external encryption. The test results show that the database developed by the above method has low performance loss, can quickly complete the processing of subdatabase and subtable, and can effectively support the distributed storage of data.
Eliminate Bias in Word Embedding based on Matrix Decomposition

The word embedding model is widely used in the natural language processing field. However, strong stereotypes embedded in pre-training reflecting social structure have not been considered and addressed promptly. The generation of word embedding relies on the co-occurrence frequency and context relationship of words, which inevitably leads to prejudice in human society being brought into the word embedding. In this case, a possible approach is to obtain and eliminate the feature dimensions that lead to bias by matrix decomposition. This paper tries to take racial discrimination as an instance to discuss the possibility of the proposal. Firstly, the data-set will be built based on stereotypes in the society at first and then apply T-SNE for checking the biased preference of the filtered data-set. Secondly, the Glove model was used to train words with stereotypes to obtain their vectors and explore the correlations between these words and white or black people with different proportions of neutral words in the model according to the ratio value. Finally, the result shows that Singular value decomposition probably can eliminate bias in the model to some extent. It can be predicted that the bias may be eliminated in a particular Glove model.
A Novel Cross-Ratio Based Gaze Estimation Method

Eye movement is the most natural expression of human thoughts, therefore it plays an important role in human-computer interaction. Over the past few decades, researchers have been pursuing for high-accuracy, easy-to-use eye-tracking techniques. The gaze tracking technology based on cross-ratio is a hot topic in the field due to its advantages of simple hardware device and allowing free head movement. Given the complexity of the user calibration procedure and the error caused by the coplanar assumption in the cross-ratio based method, we propose a new method to approximate the pupil plane step by step through constructing the front and rear planes, and then estimate the point-of-regard based on cross-ratio invariance. Simulation results show that the proposed method not only simplifies the system configuration and user calibration procedure, but also avoids the error caused by the non-coplanarity between the corneal surface reflection points and the pupil center.
Text Mining Of English Grammar Books In 19th ~ 20th Century

English grammar books are important materials for English learning, in order to systematically analyze the themes and trends of English grammar books in the 19th and 20th centuries, unstructured data and structured information are correlated, in this paper, text mining, co-word analysis and other research methods were adopted to study 15 books named English Grammar in the Gutenberg Project, which lasted from 1812 to 1918 and had a total of 1863536 English words. Wordstat9.0.4, KH CODER3 and Excel were used to analyze the collected information. This paper presents the results of text mining, such as word frequency analysis, co-occurrence analysis, keyword analysis and part of speech analysis.
Face-Centered Spatial User Interfaces on Smartwatches

Many smartwatches are now equipped with front-facing cameras that can detect users’ faces and track the devices’ spatial location relative to the faces in mid-air space. This space can be used as virtual placeholders for user interfaces where users can access them by moving their watch. In this paper, we present a novel face-centered spatial user interface for smartwatches that leverages the mid-air space for augmenting virtual user interfaces. With a pilot study, we first examine the suitable mid-air space that users can access during active use while wearing smartwatches. Next we conduct an online survey investigating users’ attitudes towards using the space to access mid-air user interfaces. More specifically, we focus on the social acceptance of face-centered spatial user interfaces in different locations and in front of different audiences. Results indicate that participants welcomed the idea of face-centered spatial user interfaces, however, the acceptance varied based on where and in front of whom they are using the space.
Research on Student’s Devotion Evaluation Method of Online Cooperative Learning

Online cooperative learning is a structured teaching strategy in which team members jointly complete knowledge construction or solution acquisition through online discussion. The process evaluation of cooperative learning mainly about how well a student focus on the discussion. However, the evaluation mostly focuses on subjective evaluation, lacking quantitative and objective evaluation. This paper develops a video-based online cooperative learning quantitative evaluation method. Firstly, Considering that there are few online cooperative learning video data sets at present, we have established the online cooperative learning Video data set through classroom practice(100 students, 8000minutes), secondly, we proposed an artificial intelligence strategy combining intensive trajectory tracking, feature map extraction, VLAD feature encoding, and linear classifiers to evaluate students’ devotion in the online discussion. 76.21% accuracy was obtained on the self-built data set, which laid a good foundation for subsequent online cooperative learning research.
Research on Intelligent Question Answering System under Operation and Maintenance Knowledge Graph

At present, many intelligent question answering based on knowledge graphs can answer simple questions, but there is no good solution to complex multi-hop problems. In response to this, relying on the knowledge graph of the operation and maintenance domain, an intelligent question answering system based on the classification strategy and subgraph matching idea is proposed, which can better solve the complex question answering under the knowledge graph intelligent question answering; especially for multi-hop questions.Firstly, the multi-hop question is divided through the classification model, and then the corresponding subgraph is recalled through the entity mention and the multi-hop category, and finally the original question and the candidate subgraph are matched for similarity to obtain the answer to the question. The experimental results show that the system has achieved high accuracy and application value in map question and answer in the field of operation and maintenance.
Chinese Entity Linking with Two-stage Pre-training Transformer Encoders

To solve the Chinese entity linking problem, this paper employs a two-stage deep pre-training transformer model. The model is broken down into two stages. The first stage separates the input text and entity information and encodes them in the CN-DBpedia (a popular and large-scale open-source Chinese knowledge base) with bi-encoder. The candidate entities are reranked in the second stage by a deep pre-training transformer encoder (bi-encoder, cross-encoder or polyencoder). To investigate whether the model performs better with different encoders in the re-ranking stage, we examined five Chinese datasets that contained entity link information. Compared to other encoders, the cross-encoder outperforms others, and due to the complexity of Chinese, the difference in performance among different encoders is huge. We noticed that some of the labeled entities in the dataset did not exist in the knowledge base before training, so we added the encyclopedia information to the knowledge base entities. The modified CN-DBpedia contains 9,396,679 entities that could provide better support for the experiment.