Reviewerly/exHarmony huggingface dataset:
A Novel Rain Removal Approach for Outdoor Dynamic Vision Sensor Event Videos

As bio-inspired vision devices, dynamic vision sensors (DVS) are being applied in more and more applications. Unlike normal cameras, pixels in DVS independently respond to the luminance change with asynchronous output spikes. Therefore, removing raindrops and streaks from DVS event videos is a new but challenging task as the conventional deraining methods are no longer applicable. In this article, we propose to perform the deraining process in the width and time (W-T) space. This is motivated by the observation that rain steaks exhibits discontinuity in the width and time directions while background moving objects are usually piecewise smooth along with both directions. The W-T space can fuse the discontinuity in both directions and thus transforms raindrops and streaks to approximately uniform noise that are easy to remove. The non-local means filter is adopted as background object motion has periodic patterns in the W-T space. A repairing method is also designed to restore edge details erased during the deraining process. Experimental results demonstrate that our approach can better remove rain noise than the four existing methods for traditional camera videos. We also study how the event buffer depth and event frame time affect the performance investigate the potential implementation of our approach to classic RGB images. A new real-world database for DVS deraining is also created and shared for public use.
Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain

Navigating off-road with a fast autonomous vehicle depends on a robust perception system that differentiates traversable from non-traversable terrain. Typically, this depends on a semantic understanding which is based on supervised learning from images annotated by a human expert. This requires a significant investment in human time, assumes correct expert classification, and small details can lead to misclassification. To address these challenges, we propose a method for predicting high- and low-risk terrains from only past vehicle experience in a self-supervised fashion. First, we develop a tool that projects the vehicle trajectory into the front camera image. Second, occlusions in the 3D representation of the terrain are filtered out. Third, an autoencoder trained on masked vehicle trajectory regions identifies low- and high-risk terrains based on the reconstruction error. We evaluated our approach with two models and different bottleneck sizes with two different training and testing sites with a fourwheeled off-road vehicle. Comparison with two independent test sets of semantic labels from similar terrain as training sites demonstrates the ability to separate the ground as low-risk and the vegetation as high-risk with 81.1% and 85.1% accuracy.
Multiview Reconstruction Using Phase Retrieval of Light Field Signals

Light field rendering is an effective multi-view reconstruction technology. The current multi-view reconstruction methods still has the problem of low view synthesis quality in complex scenes, such as occlusion and non Lambertian surfaces. In this article, we consider undistorted multiview reconstruction and develop a phase retrieval method for light field (PRLF) signals to improve the rendering quality. Our approach utilizes the phase spectrum in both the time domain and frequency domain of the discrete light field because the phase of the light field carries more scene information. The phase corrections are &#x201C;light field truncation&#x201D; in the time domain and &#x201C;phase replacement&#x201D; in the frequency domain. The iterative algorithm used provides high-quality estimated results for the relative phase of the light field. By the estimated phase results, we derive a multiview reconstruction method to improve the rendering quality. Finally, to verify the claimed performance, we also compare the PRLF with the most advanced light field reconstruction algorithms. The experimental results show that the proposed PRLF outperforms other known reconstruction schemes.
Research on rehabilitation training system for autistic children based on virtual reality

Aiming at the problems of insufficient therapists in traditional rehabilitation training for autistic children, difficult to persist in training for a long time and limited training venues, a rehabilitation training system for autistic children based on virtual reality technology was designed. The system uses the Unity3D engine combined with VR equipment to build a rehabilitation training scene, and controls the interaction between the VR equipment and the virtual scene through the C# programming language. Children with autism only need to wear 3D glasses and a tracking locator to perform rehabilitation training in the virtual scene. The results show that the training method in the system can effectively improve the autism degree of autistic children, and has a positive effect on their rehabilitation.
Design of Intelligent Fusion Terminal System with Fog Computing Capability in Distribution Area based on Large Capacity CPU

The intelligent fusion terminal in the distribution area usually adopts the mode of cooperation between the cloud and the edge, and the workload of manual operation and maintenance is large.Therefore, an intelligent fusion terminal system in the distribution area with fog computing capability based on a high-capacity CPU is proposed.Follow the "cloud pipe edge end" construction framework of smart IOT system, and take this framework as the edge computing node of distribution station area and power consumption side.Mt7622b chip in Linux operating system with openwrt firmware is used as the main control chip of edge agent gateway equipment, and the recursive least square method is used to realize the data fusion of power acquisition service in distribution station area and power distribution demand terminal.The test results show that the designed system can realize real-time monitoring of power consumption and distribution data and power quality management in the distribution station area, and the data processing delay is less than 100ms, which provides a reference for the intelligent fusion terminal system in the distribution station area.
MODELING DIABETES PATIENTS' ATTENDANCE AT BAGAMOYO DISTRICT HOSPITAL USING AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS.

This study presented the use of Autoregressive Integrated Moving Average (ARIMA) technique to modeling the diabetes patients' attendance at Bagamoyo district hospital using monthly time series data. The data used in the analysis of this study are monthly reading of diabetes patients data covered the period from January 2014 to December 2021. The data were retrieved from the hospital electronic health management information system. The Autoregressive Integrated Moving Average (ARIMA) approach was applied to the diabetes patients' data through the model identication, estimation, diagnostic checking, stationary and forecasting in R statistical software. The study identied Autoregressive Integrated Moving Average ARIMA (0, 1, 1) model to be the best one to t for the monthly total number of diabetes patients' attendance hospital outpatient department for seven years of monthly data. This was veried by Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) of the model selection criteria, Autoregressive Integrated Moving Average ARIMA (0, 1, 1) model shown the smallest values, hence this was selected as an adequate model to represent the Bagamoyo district hospital diabetes patients data. The forecast values indicate clearly that diabetes patients' attendance at Bagamoyo district hospital would be on an increase rate per month during the year 2022. Therefore, the government needs to put in place more and to engage necessary requirements for a satisfying healthcare system by increasing the medical supplies to the Bagamoyo district hospital.
Light field image quality assessment method based on deep graph convolutional neural network

This paper contains the research proposal of Sana Alamgeer that was presented at the MMSys 2022 doctoral symposium. Unlike regular images that represent only light intensities, Light Field (LF) contents carry information about the intensity of light in a scene, including the direction light rays are traveling in space. This allows for a richer representation of our world, but requires large amounts of data that need to be processed and compressed before being transmitted to the viewer. Since these techniques may introduce distortions, the design of Light Field Image Quality Assessment (LF-IQA) methods is important. The majority of LF-IQA methods based on traditional Convolutional Neural Network (CNN) have limitations, i.e. they are unable to increase the receptive field of a neuron-pixel to model non-local image features. In this work, we propose a novel no-reference LF-IQA method that is based on Deep Graph Convolutional Neural Network (GCNN). Our method not only takes into account both LF angular and spatial information, but also learns the order of pixel information. Specifically, the method is composed of one input layer that takes a pair of graphs and their corresponding subjective quality scores as labels, 4 GCNN layers, fully connected layers, and a regression block for quality prediction. Our aim is to develop the quality prediction method with maximum accuracy for distorted LF content.
MULTIPROFESSIONAL CARE IN CHRONIC WOUNDS

Chronic wounds are those that produce greater difficulty in the healing process and tissue repair, and in addition, require longer treatment. Reflective research, based on full readings of several studies, in the year 2022. The main results are humanized care, qualified and multiprofessional listening are highlights that deserve attention in the context of long-term treatment of chronic wounds, due to being through these actions that achieve a holistic view of the individual needs of each patient, having materials and professionals trained to continue the care that needs to happen to promote health to this group.
Research on YOLOv5s Highway Pedestrian Detection Algorithm Integrating Attention Mechanism

Pedestrian detection is widely used in daily life, but it is difficult to study in highway environment, such as occlusion overlap.In order to reduce error detection rate of highway pedestrian detection, an algorithm based on YOLOv5s was proposed.Since vehicle occlusion leads to the reduction of effective features of targets, CBAM attention and SE-NET mechanism module is introduced in the network of YOLOv5s to maximize the extraction of effective features.In order to prevent the spatial characteristic information in the trunk network from being damaged, CBAM module is added at the beginning and end of the structure, and SE-Net attention module is added in the neck network, that is, after the detection layer C3 module, the weight information obtained is connected with the subsequent Conv module, so that the model pays more attention to the pedestrian area.Due to low detection accuracy caused by pedestrian overlap.YOLOv5s was designed by combining DIOU_NMS candidate box screening mechanism.The results show that the mean average precision of YOLOv5s (IOU=0.5)increases by 0.48, and the value of Recall of the improved algorithm increases by 0.51 respectively.The improved pedestrian detection algorithm improves the accuracy of target box regression.Thus, the confidence of pedestrian detection is improved.Based on the improvement strategies mentioned above, the detection speed is 32fps, which meets the requirements of real-time detection.
Personalized Dialogue Generation Model Based on BERT and Hierarchical Copy Mechanism

Despite the great advances in generative dialogue systems, existing dialogue generation models are still unsatisfactory in maintaining persona consistency.In order to make the dialogue generation model generate more persona-consistent responses, this paper proposes a model named BERT-HCM (Personalized Dialogue Generation Model Based on BERT and Hierarchical Copy Mechanism).The model uses an encoder based on BERT initialization to encode persona information and dialogue queries and subsequently uses a Transformer decoder incorporating a hierarchical copy mechanism to dynamically copy the input-side content to guide the model in generating responses.The experimental results show that the proposed model improves on both automatic and human evaluation metrics compared to the baseline model and is able to generate more fluent, relevant and persona-consistent responses.
Case Characterization and Discipline Measurement Method Based on Discipline Inspection and Supervision Knowledge Graph

As an important link to realize intelligent discipline inspection and supervision, the term "case characterization and discipline measurement" refers to the automatic extraction of material facts from case description and the conclusion of conformity and nonconformity after comparison in accordance with legal norms.In response to the problem that there was no special method for the task of case characterization and discipline measurement, the paper combined the practical case handling process of the staff and proposed a method of case characterization and discipline measurement based on discipline inspection and supervision knowledge graph.The method uses the knowledge graph as auxiliary information and aligns the entities of regulations and cases using knowledge fusion technology to construct the discipline inspection and supervision knowledge graph.For the newborn case descriptions, named entity recognition technology is used to extract the key elements that determine the verdict outcome.Similar cases were identified with the same discipline breach nature.Then, text classification technology is used to predict the severity of case circumstances.Combined with the disciplinary violation facts, the disciplinary result is given according to the party discipline rules.Experiments were carried out with a dataset of typical cases notified by the discipline inspection and supervision.According to the experimental results, the proposed method shows its validity, which improves the interpretability of case characterization and discipline measurement and fills the field gap.
Enhanced phase estimation in parity detection based Mach-Zehnder interferometer using non-Gaussian two-mode squeezed thermal input state

While the quantum metrological advantages of performing non-Gaussian operations on two-mode squeezed vacuum (TMSV) states have been extensively explored, similar studies in the context of two-mode squeezed thermal (TMST) states are severely lacking. In this paper, we explore the potential advantages of performing non-Gaussian operations on TMST state for phase estimation using parity detection based Mach-Zehnder interferometry. To this end, we consider the realistic model of photon subtraction, addition, and catalysis. We first provide a derivation of the unified Wigner function of the photon subtracted, photon added and photon catalyzed TMST state, which to the best of our knowledge is not available in the existing literature. This Wigner function is then used to obtain the expression for the phase sensitivity. Our results show that performing non-Gaussian operations on TMST states can enhance the phase sensitivity for significant ranges of squeezing and transmissivity parameters. We also observe that incremental advantage provided by performing these non-Gaussian operations on the TMST state is considerably higher than that of performing these operations on the TMSV state. Because of the probabilistic nature of these operations, it is of utmost importance to take their success probability into account. We identify the photon catalysis operation performed using a high transmissivity beam splitter as the optimal non-Gaussian operation when the success probability is taken into account. This is in contrast to the TMSV case, where we observe photon addition to be the most optimal. These results will be of high relevance for any future phase estimation experiments involving TMST states. Further, the derived Wigner function of the non-Gaussian TMST states will be useful for state characterization and its application in various quantum information protocols.
Adapting Pretrained Representations for Text Mining

Pretrained text representations, evolving from context-free word embeddings to contextualized language models, have brought text mining into a new era: By pretraining neural models on large-scale text corpora and then adapting them to task-specific data, generic linguistic features and knowledge can be effectively transferred to the target applications and remarkable performance has been achieved on many text mining tasks. Unfortunately, a formidable challenge exists in such a prominent pretrain-finetune paradigm: Large pretrained language models (PLMs) usually require a massive amount of training data for stable fine-tuning on downstream tasks, while human annotations in abundance can be costly to acquire.
First Workshop on Content Understanding and Generation for E-commerce

Shopping experience on any e-commerce website is largely driven by the content customers interact with. The large volume of diverse content on e-commerce platforms, and the advances in machine learning, pose unique opportunities for gathering insights through content understanding and applying these insights to generate content better shopper experience. The purpose of the first edition of this workshop was to bring together researchers from industry and academia on questions surrounding e-commerce content understanding and generation.
The KDD 2022 Workshop on Causal Discovery (CD2022)

Causal relationships have been utilized in almost all disciplines, and the research into causal discovery has attracted a lot of attention in the last few years. Traditionally, causal relationships are identified by making use of interventions or randomized controlled experiments. However, conducting such experiments is often expensive or even impossible due to cost or ethical concerns. Therefore, there has been an increasing interest in discovering causal relationships based on observational data, and in the past few decades, significant contributions have been made to this field by computer scientists.
THE CORRELATION BETWEEN STUDENTS’ READING STRATEGIES AND STUDENTS’ READING COMPREHENSION AT ELEVENTH GRADE OF MADRASAH ALIYAH NEGERI (MAN) 2 NORTH LAMPUNG ACADEMIC YEAR 2021/2022

Abstract: This research was aimed to investigate whether there is significant correlation between students’ reading strategies and students’ reading comprehension at eleventh grade of Madrasah Aliyah Negeri (MAN) 2 North Lampung Academic Year 2021/2022. The kind of the research was quantitative research and used correlational research design. The samples of the research were 27 students which were taken by using proportional random sampling technique. The instruments used in this research were questionnaire and test. Questionnaire was used to measure students' reading strategies, meanwhile the test which was used to measure students' reading comprehension. Then, to prove the hypothesis of the research and the data was analyzed by using Pearson Product Moment correlation, the result showed that = 0,969, and = 0,381. It means that is rejected, and is accepted. From the result of significant test, it was found that = 19,513 and with dk = 27-2 by using significant level 5% is 2,052. Based on the result of the data analysis, it can be concluded that there is significant correlation between students’ reading strategies and students’ reading comprehension at the eleventh grade of Madrasah Aliyah Negeri (MAN) 2 North Lampung Academic Year 2021/2022.
The Correlation Between Students'Listening to English Songs Habit and Their Vocabulary Size at the Eleventh Grade of SMKN 03 Kotabumi Academic Year 2021/2022

Vocabulary is important for learning a language. To enrich vocabulary, one way is listening to English songs as a habit. This research aimed to find out the correlation between students’ listening to English songs habit and their vocabulary size at the eleventh grade of SMKN 03 Kotabumi. The research instrument used to collect the data were multiple choices test to measure the students’ vocabulary size that consists of 46 items of test with five options (a, b, c, d and e) in each item, and questionnaire to measure the students’ listening to English songs habit that consists of 36 items. The result of this research showed that Ha is accepted with the result of hypothesis test robserved is higher than rtablewhich is 0,402 ≥ 0,334. Furthermore, the result of t test was tobserved is higher than ttable which is 2,520 ≥ 1,692, it means that there is significant correlation between students’ listening to English songs habit and their vocabulary size at the eleventh grade of SMKN 03 Kotabumi academic year 2021/2022.
Research on Emotional Tendency Analysis of Weibo Comments Based on Deep Neural Network

With the rapid development of China's economy, social media has also developed rapidly, among which Weibo has grown into a more popular social media. Users will comment on hot events on the Weibo platform. Therefore, how to conduct sentiment analysis on user comments has become necessary research for relevant departments. Through natural processing technology, practical information can be extracted from the text of Weibo, which can provide appropriate decision-making for China's network security monitoring, prediction of potential problems, and product analysis. This paper proposes a method based on Word2Vec and a recurrent neural network improved for long and short-term memory networks to conduct sentiment analysis of Chinese Weibo comments through the integration of deep learning. And through experiments, the accuracy of sentiment analysis of Weibo comments in this article has reached 92%. Through the research of this article, we can better guide and supervise the sentiment tendency of public opinion for relevant departments.
Perception Model and Intervention Mechanism of Academic Emotions empowered by Artificial Intelligence: A systematic Literature Review

Academic emotion is an important non-intellectual factor affecting learning behavior, learning motivation and academic achievement. In the field of AI-enabled learning, multi-source heterogeneous data such as physiology, psychology and behavior can be used to comprehensively outline the implicit emotional state of learners. This research adopts the meta-analysis method to sort out the expression methods and corresponding indicators of academic emotion and constructed an artificial intelligence perception model including data layer, technology layer, presentation layer and presentation layer. Based on this, according to the Gross emotion regulation process model, this research explored the intervention mechanism of academic emotions at different timings (situation selection, situation modification, attention allocation, cognitive change and response adjustment) and from different levels (the process level, the way level and the tool level) to provide some reference for researchers in related fields.
Research on Project Duration Prediction Based on Artificial Neural Network

In power grid construction project management, effective prediction of construction process duration is one of the key factors affecting project quality and cost. Based on the historical capital construction project data, this paper studies the main impact of the project process duration, and predicts the expected completion time of each process of the current project based on BP artificial neural network system. Using network planning technology, by analyzing the project Gantt chart, draw the network node diagram of the project, and determine the key path of project implementation. Finally, the total duration prediction range of the project is obtained based on the duration calculation method of fuzzy membership function.