Reviewerly/exHarmony huggingface dataset:
Project C – Contact Tracing with Firebase

This document describes a new idea for tracking Corona virus affected people and alerting its users if they got contacted with any affected one in past several days. As we all know we are passing by a very critical situation where the main reason of getting affected by Corona virus is get in touch with a affected person and it is also proved that if an individual comes within 6 feet of an affected person also increase the possibility of getting affected by Corona virus for that individual very high. So here we are, If we can build a system that can track everyone who come within 6 feet and if any of them got affected by Corona virus we will get an alert without knowing their identity is sufficient for us to get conscious at the right time. So, now let us look on what technologies and APIs we are going to use for this project. We are willing to build this system with the help of Firebase, Nearby Messages API and some basic Android SDK tools. We will scan every incoming message and if we find any message that is similar to the format of notifying someone that he tested Corona positive the software will update database with that user’s firebase auth UID that he tested corona positive and every user will continuously scan database if any of the UID the got contacted within last 14 days has a status of Corona positive the software will notify that someone got Corona positive that got nearby with the user in last 14 days. Now let’s have a look on the detail descriptions of the system in this paper.
Intelligent Question Answering System Based on Knowledge Graph of Beijing Opera

In order to improve the digital service effect of traditional Chinese culture, this paper focuses on the field of Beijing Opera, completes the research and construction of the knowledge graph of Beijing Opera, and further builds an intelligent question-answering system. For Chinese questions input by users, the system uses the BERT + Bi-LSTM + CRF model to recognize named entities, then uses the BERT-Sentence Pair Classification model to calculate text similarity to complete relational attribute mapping, and combines character string matching to improve efficiency, finally gets the answer. On the Beijing Opera data set of this paper, the F1 values of the two models are 91.01% and 91.17%. Finally, this paper proposes an ontology construction method of the domain knowledge graph, the built question-answering system has also achieved good application effect on the retrieval and utilization of Beijing Opera knowledge, and realizes an effective form of digital cultural services.
SDAPNet: End-to-End Multi-task Simultaneous Detection and Prediction Network

Accurate detection of objects in perception system is a basic task for autonomous vehicles to operate reliably. The current mainstream paradigms for perception and prediction are able to decompose into three sequential subtasks: detection, tracking and prediction. Much of the previous works focus on one of these subtasks and internal relationship between the various subtasks are therefore ignored. In this work we propose an end-to-end model that is able to jointly carry out 3D detection and motion prediction in the context of autonomous vehicles, which can benefit from joint optimization of two tasks. Considering computational consumption and memory cost, our approach utilizes 2D convolutions across space over a bird's eye view (BEV) representation of the 3D world, not 3D convolutions which require more inference time for a single point cloud. In this paper, the key model is the sequential multi-frame point clouds channels based on BEV, which can help perform object detection and learn temporal information at the same time. Experiments on nuScenes dataset show that the proposed network achieves state-of-the-art results, and the trade-off between accuracy and efficiency of two tasks are obtained. Additionally, we validate the effectiveness of our models through the ablation studies. Specifically, by sharing network, parameters and detection results we can perform two tasks within 32 FPS.
Graph Convolutional Network Based Patent Issue Discovery Model

With the increasing attention on the protection of intellectual property rights, a large number of patents need to be processed. However, since patent is a kind of complicated technical text, it is difficult to understand patents. How to quickly understand a patent by computer is the problem. To solve the above problem, our method is to tag the issue sentences, these sentences describe problems to be solved in patents. Tagging the issue sentences is a very important research topic in patent understanding, because a patent revolves around issue sentences, issue sentences are the key to understand a patent. There are two challenges in our task: (1) How to extract issue sentences to get corpus? (2) What kinds of features and models are better for our task? In order to solve the above challenges: (1) We find that the issue sentences mainly exist in the “technical background” section of patent, so that we can extract issue sentences from this section to get corpus. (2) We split the “background technology” section into sentences, and obtain two sets of features from a sentence include: 1) Part-of-speech features of a sentence. 2) Association information feature between the sentence and the claim of patent. Then we construct graph according to above two sets of features, and use Graph Convolutional Neural Network to train and test.
Decoupling and Coupling Transformation for Arbitrary Style Transfer

Style transfer is a task that extracts the style from the style image to synthesize an output that has the content of the content image. The latest method with better effect transfers style image onto content image via a transformation matrix. These algorithms are based on a variety of assumptions to model the style. However, there are no accurate assumptions of how style and content are combined. They model the features covariance as style and produce copy and paste results in synthesized images which we called texture mapping effect. In this work, we define the coupling form as the multiplication of the style matrix and the content matrix. Then, we derive the form of transformation theoretically and present an arbitrary style transfer approach that decouples content and style from features based on our definition. By this assumption, the stylized image will not introduce a texture mapping effect. We demonstrate the effectiveness of our approach comparisons with the state-of-the-art methods in both style transfer and photorealistic image stylization. The results show that our method has no texture mapping effect and is more in line with the definition of stylization.
CLADA: Contrastive Learning for Adversarial Domain Adaptation

This paper focuses on the challenging problem of unsupervised domain adaptation of synthetic data for the semantic segmentation task of autonomous driving scenes. It is motivated by the generative adversarial methods that apply image-to-image translation by learning a mapping between the source and target domains. Fully supervised training of deep models for semantic segmentation do not generalize well to unseen target data. By applying domain adaptation, a model can be fit that generalizes to the target domain.Previous work has shown that combining generative adversarial networks with cycle consistency is effective for mapping images between domains, which can then be used to train a domain invariant semantic segmentation model. However, this requires additional networks to implement the cycle-consistency constraint. This paper proposes replacing this with a more efficient contrastive objective for the semantic segmentation task. By reducing the training time and computational resources, more complex end-to-end domain adaptation architectures may be used.
Knowledge-Enabled Generation of Semantically Annotated Image Sequences of Manipulation Activities from VR Demonstrations

This work presents a cloud-to-edge framework capable of collecting and annotating synthetic images from human performances in virtual environments with the purpose of enabling the training and deployment of robot vision models. The virtual environment is capable of providing close-to-reality image data using state of the art rendering capabilities of game engine technologies. The human performances in the virtual world are fully recorded and segmented into meaningful motion phases of action models from cognitive science. The recorded performances are stored as fully re-playable episodes enabling multi-camera post-processing to acquire fully labeled vision data. The data is represented using KnowRob acting as an extension of the robot’s knowledge base, making it robot understandable and accessible using it’s built in logic based query language.
Forensics of Decompressed JPEG Color Images Based on Chroma Subsampling

Identification of the type of chroma subsampling in a decompressed JPEG color image stored in a lossless format is important in forensic analysis. It is useful in many forensic scenarios like detecting localized forgery and estimating the quantization step sizes in the chroma planes for source camera identification. In this work, we propose a machine learning-based method capable of identifying the chroma subsampling used in the compression process. The method is based on detecting the change in adjacent pixel correlations due to upsampling process in JPEG decompression. These changes in the correlation are measured using the two-sample Kolmogorov-Smirnov (KS) test statistic in different directions. The experimental results show the efficacy of the proposed method in identifying the chroma subsampling scheme.
Roles of fiber birefringence and Raman scattering in the spontaneous four-wave mixing process through birefringent fibers

We investigate the impact of fiber birefringence and spontaneous Raman scattering on the properties of photon pairs that are generated by the spontaneous four-wave mixing process in birefringent fibers. Starting from the formulation of the theory of four-wave mixing, we show a theoretical model for a generated optical field with the consideration of the Raman scattering and a Gaussian-distributed pump. The theoretical model is then applied for deriving the closed expressions of the photon-pair spectral properties as a function of the fiber birefringence. Also, with the modeled Raman gain, we evaluate the reduction of the pair production rate due to the presence of the Raman effect as well as the contributions of the Raman-scattered photons over a broad wavelength range. The predictions are experimentally verified with a commercial polarization-maintaining fiber.
Design and Implementation of Smart Old Age Home

In modern era, smart old age homes are identified as a proper medium for an independent and comfortable life of older and disable persons. In this project, the wireless home automation system allows voice control switching, location tracking, health monitoring and fall detection. The voice control switching system can do remote controlling of home appliance such as TV, light, fan etc. The health monitoring and location tracking system are used in clinical perspectives and intensive care. A System of fall detection is added to abstain from unintentional injury and death. This paper will focus on integration of automation system and health monitoring system to develop smart old-age home.
Modified Prototypical Networks for Few-Shot Text Classification Based on Class-Covariance Metric and Attention

This paper propose a modified Prototypical Networks based on the Class-covariance metric and Attention (PNCA) against the few-shot text classification issue. In this method, we use the Mahalanobis distance as the classification of class-specific feature vectors, furthermore, we add the word-level features and class-level features of attention mechanism, which highlight the standout knowledge of few-shot data and learn a more discriminative prototype representation. Our experiments based on the Chinese News Classification(CNC) Dataset show this model almost reach state-of-the-art, and the accuracy is 1%~2% higher than the existing models.
Words in Motion: Slurs in Indirect Report

Summary Slurs are pejorative epithets that express negative attitudes toward a class of individuals sharing the same race, country of origin, sexual orientation, religion, and the like. The aim of this paper is to show what happens in communication when slurs are reported. It focuses on the derogatory content of such expressions and on the persistence of their performative effects in reported speech. In this respect, the question concerning the attribution of responsibility for the derogatory content conveyed by the slurs is relevant. Indeed, reporting a slur involves quoting not only the content but also the speaker’s personal commitment and (negative) attitude. Different theories on the status of the derogatory component of slurs make different predictions about their offensiveness in reported speech and about the speaker’s “responsibility” for the attitude and feelings conveyed by that word, be she the original speaker or the reporter. The results of a questionnaire show empirically that no single theory can provide a conclusive statement on this matter.
Predictive Analytics Framework for Lung Cancer with Data Mining Methods

The complex nature of the Non-Small cell Lung Cancer (NSCLC) dataset was a serious concern for enhanced performance of prediction. The motivation is to develop a Framework that reduces the complexity of the lung cancer dataset by reducing the irrelevant features and forming a testing set that comprised of best features that will provide accurate solution for the problem. The objective of the research paper is to propose a predictive analytics based framework using data mining techniques that could possibly provide prediction of NSCLC with enhanced performance. The Possible Materials and methods that could be used to design the framework and the performance evaluation parameters using confusion matrix to test the enhancements after applying the framework are determined in the research study. The goal is to prove that utilizing Preprocessing, Dimensionality Reduction and Feature Extraction techniques to reduce the complex lung cancer dataset are essential for predictive analytics of NSCLC.
The Needs Analysis of Virtual Exergaming

Due the maturity of virtual reality (VR) products and related technologies, VR has been widely applied to exergaming. The COVID 19 epidemic also fastens applying VR to fitness and sports. However, as with all innovative products, to ensure that the presented product can be successful, understanding the user's actual or potential needs will be one of the key factors for the success of the product. To ensure we can know the players' needs of VR exergaming, Kano model will be employed in this study. We propose 12 design functions for VR exergames. Through Kano analysis, we can understand how these design elements are categorized in the user's needs. Results of Kano analysis could help VR exergaming manufacturers to enhance game design functions, so that VR sport games can be closer to the needs of users.
Employer-Employee Network for Conversational Recommendation

Traditional recommendation systems model user preferences based on past historical behaviors, thus unable to obtain dynamic user preferences. The conversational recommendation system (CRS) combines the conversational module with the recommendation module and overcomes the limitations by directly asking the user's preference for attributes. However, the existing CRS methods lack effective information propagation among various modules, making the model lack the basis for making correct decisions. In this paper, we propose an Employer-Employee network, which decomposes the actions into two stages, which are completed by two networks respectively. The Employer Network is responsible for analyzing information and making decisions (query or recommend), and the Employee Network is responsible for collecting information and performing tasks. Our contributions can be highlighted in three aspects: We first emphasize the importance of information propagation among multiple modules in the conversational recommendation system. Secondly, we propose an Employer-Employee (EE) network, which transforms each turn of action into a two-stage decision-making task handed over to two networks to complete. Thirdly, we conduct experiments on multiple datasets, and the experimental results show that our model achieves competitive performance compared with state-of-the-art baselines.
A novel file carving algorithm for docker container logs recorded by json-file logging driver

In recent years, the container technique has gained increasing attention and there is an urgent demand to improve the methods of forensically investigating the attacked containers. Container logs involve sensitive information and are included in most containers. However, there are few researches on recovery methods of container logs. In this paper, we propose a novel carving algorithm for container logs in json-file format. To realize this, the json-file data have to be identified based on the intrinsic structure of the log files and the spatial locality of the storage. Then, the identified data is reassembled utilizing the prior knowledge of json-file format and the similarity between log contents. What's more, the index structure used to retrieve log content is modified, which takes less computations during each search. Experiments results show that our proposed algorithm performs well and is capable of recovering more json-file log lines than existing carving tools. • Make available datasets by downloading popular images and running them as containers. • Accurate discrimination of json-file data from all other types of data. • Reliable reassembly for identified json-file fragments. • Modified the vp-tree based on similar patterns among the log contents.
Equality and Apartness in Bi-intuitinistic Logic

In the present paper we argue that a symmetric picture of the relationships between equality and apartness can be attained by considering these notions on the background of bi-intuitionistic logic instead of the usual intuitionistic logic. In particular we show that, as the intuitionistic negation of a relation of apartness is an equality, the dualintuitionistic co-negation of an equality is a relation of apartness. At the same time, as the intuitionistic negation of equality is not an apartness, the co-intuitionistic negation of an apartness is not an equality.
Reducing Complexity of Wavelet Based EMI Source Discovery via Bayesian Hash Coding

Locating the electromagnetic interference (EMI) sources of an electronic system remains to be one of the most critical issue for electromagnetic compatibility (EMC) design. Computational models were developed to help designers to locate existing EMI sources. In this paper we present a Bayesian model to formalize the process, and an optimized hash coding method to reduce the inference complexity. After wavelet transformation, high dimensional sub-signals are mapped into low dimensional hash vector. Similar vectors are then stored in a hash bucket and this can be viewed as a preprocessing step whose prior knowledge of the signals can be adopted into subsequent discovery step through Bayesian equation. Experimental results prove that the convergence of our method is at least an order of magnitude faster than other state-of-the-art models.
Context-based Fine Hierarchical Object Detection with Deep Reinforcement Learning

We present a method for object detection by adopting an agent that follows a fine hierarchical search strategy. The key idea of this strategy is that the agent detects the target on region proposals by analyzing the image information of the search region at each level. Start from the entire image, the agent detects the subregion that contains the object level by level until it confirms the target has been found. We model this process as a Markov Decision Process (MDP) to use reinforcement learning to train the agent to learn the optimal strategy. By utilizing a hierarchical search strategy, we can avoid the generation of a large number of region proposals. Besides, we add the ReNet layer that extracts context information to improve the agent’s ability to understand the information conveyed by the image. The experiments we conducted have demonstrated the efficiency of our proposed method. Experiments demonstrate the efficiency of our proposed method, the agent only needs 2 or 3 steps to detect the target in images under most circumstances.
Deep Layer and Spatial Aggregation neural network for human pose estimation

Abstract The simplebaseline model achieves high performance of human pose estimation with simple network structure. But the model lacks the layer and spatial information fusion. In this paper, we propose DLSAnet, which fuse layers and spatial information efficetively. DLSAnet uses DLA as backbone which has excellent feature extraction capabilities in the field of object detection. In addition, a modified spatial pyramid pooling is introduced to pool and connect multi-scale local area features, allowing the network to learn object features more comprehensively. Using a four-branch SPP module instead of a single-branch SPP module connected by a single hopping layer. This method is effective in alleviating the problem of slow loss drop late in training. Experiments show that DLSAnet can achieve better accuracy.
Automated Educational Program Mapping on Learning Standards in Computer Science

There are many lectures for students or working adults. Educational institutions have created a table that associates educational courses with learning standards in order to understand the content of these courses. Learning standards such as SFIA (Skills Framework for an Information Age) indicate the skills that learners should learn. The mapped table can make contents clear for students or other educational institutions.
A Double Feature Fusion Network with Progressive Learning for Sharper Inpainting

Deep learning methods in computer vision have shown tremendous progress recently. Most image restoration methods are based on single feature extraction. However, image restoration requires various types of information to reconstruct the properties such as structure, color, texture details. Low-quality pictures are widespread in the real world because of the natural conditions and unstable photography. Therefore, we propose a double feature fusion network (DFFNet) to overcome this challenge in image inpainting. DFFNet extracts deblurring and structure features simultaneously and adopts multipath refinement framework aided by multipath contextual attention modules to restore the mask region in a coarse-to-fine manner. Thus, our work can restore blurred and large masked images into sharp and complete images and neglect the meaningless occlusions. It outperforms the state-of-the-art combined methods for sharper and smarter inpainting. First, we design a multi-path refinement network that can extract multi-features for further refinement and coarse inpainting. Second, multipath contextual attention modules are reconstructed for receiving edge and deblur features and finer inpainting results. Third, multi-stage synthesis loss function and double feature fusion units ensure that the restored images have the same structure and texture similarity as the original ground-truth counterparts. Therefore, the progressive learning framework DFFNet adopts key elements of image restoration information such as structure and texture, and refines the background for removing meaningless noise for sharper and smarter inpainting.