Reviewerly/exHarmony huggingface dataset:
Fuzzy based fast non local mean filter to denoise Rician noise

MRI images suffer from a type of multiplicative noise called Rician noise. The traditional smoothing filters are not effective to reduce this type of noise. Non local Means (NLM) filters with bias correction is a promising technique for such type of signal dependent noise. The NLM denoising is based on self-similarity and is computationally very expensive. Many researchers are working to improve its time complexity. In most cases there is a tradeoff between speed and quality of denoised image. This paper proposes a speed enhanced NLM method that use integral image and fuzzy Jacord similarity measure to find the self-similarity of sub windows. This helps to find the weights of similar pixel at a faster rate than the traditional NLM algorithm and more accurately than the existing fast NLM method. Consequently, these similar pixels are used to generate the noise free pixels. At the end the conventional bias subtraction method is used as post processing step. The proposed scheme is tested with the standard brain web data set with different noise levels and compared with existing Fast NLM techniques and basic NLM using Root mean square error (RMSE), peak signal noise ratio (PSNR), Structure similar index (SSIM), quality index and computational time parameters methods. The proposed method gives better result than existing Fast NLM technique with high density Rician noise in the image and is 20 times faster than traditional NLM.
The Object Tracking System At The Service Delivery Center Of The Traveling Salesperson Problem Method

Abstract Search object delivery is essential to investigate, especially for package delivery. The technology is beneficial to check whether the goods sent have arrived or not. Otherwise, it will be known where in real-time. This study aims to provide information to the shipping service, to choose the right and optimal shipping routes using the problem of the Travel Salesperson’s problem, because of that way. This method is rather difficult when viewed from a computational point of view, but TSP is made easy by trying all possible routes to get a solution or approaching the optimal path. The problem is how to determine the route according to the package that can get to the destination city in the shortest time possible by using that route, package which can reach a town or delivery center to each subsequent branch that matches the package to the postal receiving town with a minimum time limit. In other words, the shortest path and the path that the packet must pass from the sending post to the recipient must be determined.
3D Skeletal Human Action Recognition Using a CNN Fusion Model

Smart homes have become central in the sustainability of buildings. Recognizing human activity in smart homes is the key tool to achieve home automation. Recently, two-stream Convolutional Neural Networks (CNNs) have shown promising performance for video-based human action recognition. However, such models cannot act directly on the 3D skeletal sequences due to its limitation to the 2D image video inputs. Considering the powerful effect of 3D skeletal data for describing human activity, in this study, we present a novel method to recognize the skeletal human activity in sustainable smart homes using a CNN fusion model. Our proposed method can represent the spatiotemporal information of each 3D skeletal sequence into three images and three image sequences through gray value encoding, referred to as skeletal trajectory shape images (STSIs) and skeletal pose image (SPI) sequences, and build a CNNs’ fusion model with three STSIs and three SPI sequences as input for skeletal activity recognition. Such three STSIs and three SPI sequences are, respectively, generated in three orthogonal planes as complementary to each other. The proposed CNN fusion model allows the hierarchical learning of spatiotemporal features, offering better action recognition performance. Experimental results on three public datasets show that our method outperforms the state-of-the-art methods.
Andromeda: A Dataset of Ansible Galaxy Roles and Their Evolution

Cloud-native applications increasingly provision infrastructure resources programmatically through Infrastructure as Code (IaC) scripts. These scripts have in turn become the subject of empirical software engineering research. However, an often-overlooked part are the software ecosystems that have grown around the IaC languages. For example, Galaxy is an ecosystem for the popular Ansible IaC language. Galaxy features a large number of so-called "roles", which are reusable collections of Ansible code akin to libraries for general-purpose languages. In contrast to, and despite their similarities, such IaC ecosystems have enjoyed far less attention in the literature than library ecosystems for general-purpose languages. In this data showcase paper, we present Andromeda, the first dataset capturing the Ansible Galaxy ecosystem, its roles, and their evolution. Andromeda provides structural representations of more than 125 000 role versions, and upwards of 800 000 concrete changes between such versions extracted from the underlying git repositories. Andromeda aims to provide an extensive view of the contributor side of the Galaxy ecosystem, which we hope will stimulate additional research on IaC ecosystems.
A three-step hybrid specification approach to error prevention

Effectively preventing errors in requirements analysis and design is extremely important for enhancing software productivity and reliability, but how to fulfill this goal remains an open problem. In this paper, we propose a concept of hybrid specification and describe a novel three-step hybrid specification approach to address this problem. We discuss how the three-step approach can be used to effectively prevent errors in the early phases of development. The expected effect of the approach is to strike a good balance between enhancing productivity and ensuring the reliability of the program implemented. We present a controlled experiment to evaluate the effectiveness of the approach. The result of the experiment shows that our method can detect and prevent 28.36% more errors than a comparable traditional requirements analysis method.
Towards Video Streaming Analysis and Sharing for Multi-Device Interaction with Lightweight DNNs

Multi-device interaction has attracted a growing interest in both mobile communication industry and mobile computing research community as mobile devices enabled social media and social networking continue to blossom. However, due to the stringent low latency requirements and the complexity and intensity of computation, implementing efficient multi-device interaction for real-time video streaming analysis and sharing is still in its infancy. Unlike previous approaches that rely on high network bandwidth and high availability of cloud center with GPUs to support intensive computations for multi-device interaction and for improving the service experience, we propose MIRSA, a novel edge centric multi-device interaction framework with a lightweight end-to-end DNN for on-device visual odometry (VO) streaming analysis by leveraging edge computing optimizations with three main contributions. First, we design MIRSA to migrate computations from the cloud to the device side, reducing the high overhead for large transmission of video streaming while alleviating the server load of the cloud. Second, we design a lightweight VO network by utilizing temporal shift module to support on-device pose estimation. Third, we provide on-device resource-aware scheduling algorithm to optimize the task allocation. Extensive experiments show MIRSA provides real-time high quality pose estimation as an interactive service and outperforms baseline methods.
SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection

Recently, various object detection frameworks have been applied to text detection tasks and have achieved good performance in the final detection. With the further expansion of text detection application scenarios, the research value of text detection topics has gradually increased. Text detection in natural scenes is more challenging for horizontal text based on a quadrilateral detection box and for curved text of any shape. Most networks have a good effect on the balancing of target samples in text detection, but it is challenging to deal with small targets and solve extremely unbalanced data. We continued to use PSENet to deal with such problems in this work. On the other hand, we studied the problem that most of the existing scene text detection methods use ResNet and FPN as the backbone of feature extraction, and improved the ResNet and FPN network parts of PSENet to make it more conducive to the combination of feature extraction in the early stage. A SEMPANet framework without an anchor and in one stage is proposed to implement a lightweight model, which is embodied in the training time of about 24 h. Finally, we selected the two most representative datasets for oriented text and curved text to conduct experiments. On ICDAR2015, the improved network’s latest results further verify its effectiveness; it reached 1.01% in F-measure compared with PSENet-1s. On CTW1500, the improved network performed better than the original network on average.
GSAP: A Global Structure Attention Pooling Method for Graph-Based Visual Place Recognition

The Visual Place Recognition problem aims to use an image to recognize the location that has been visited before. In most of the scenes revisited, the appearance and view are drastically different. Most previous works focus on the 2-D image-based deep learning method. However, the convolutional features are not robust enough to the challenging scenes mentioned above. In this paper, in order to take advantage of the information that helps the Visual Place Recognition task in these challenging scenes, we propose a new graph construction approach to extract the useful information from an RGB image and a depth image and fuse them in graph data. Then, we deal with the Visual Place Recognition problem as a graph classification problem. We propose a new Global Pooling method—Global Structure Attention Pooling (GSAP), which improves the classification accuracy by improving the expression ability of the Global Pooling component. The experiments show that our GSAP method improves the accuracy of graph classification by approximately 2–5%, the graph construction method improves the accuracy of graph classification by approximately 4–6%, and that the whole Visual Place Recognition model is robust to appearance change and view change.
A Stochastic Control Strategy for Safely Driving a Powered Wheelchair

In this paper we deal with model-based control design in the presence of uncertainties. We use Stochastic Dynamic Programming to solve two problems, called longitudinal and lateral vehicle control. The goal is to allow safe driving (navigation) of a moving vehicle in an environment with static obstacles. We show how to define the optimization problems given the stochastic driver behavior and environment. The vehicle dynamics model is deterministic (obeys physical laws) and is explicitly integrated into the optimization problem. In terms of results, the numerically computed control policies provide best-on-average performance (according to the expected value operator). In simulation, it is shown that the vehicle effectively avoids obstacles, thus ensuring a safe drive experience.
XAI-N: Sensor-based Robot Navigation using Expert Policies and Decision Trees

We present a novel sensor-based learning navigation algorithm to compute a collision-free trajectory for a robot in dense and dynamic environments with moving obstacles or targets. Our approach uses deep reinforcement learning-based expert policy that is trained using a sim2real paradigm. In order to increase the reliability and handle the failure cases of the expert policy, we combine with a policy extraction technique to transform the resulting policy into a decision tree format. We use properties of decision trees to analyze and modify the policy and improve performance of navigation algorithm including smoothness, frequency of oscillation, frequency of immobilization, and obstruction of target. Overall, we are able to modify the policy to design an improved learning algorithm without retraining. We highlight the benefits of our approach in simulated environments and navigating a Clearpath Jackal robot among moving pedestrians. (Videos at this url: https://gamma.umd.edu/researchdirections/xrl/navviper)
Fast GLR parsers for extended BNF grammars and transition networks

The Tomita’s Generalized LR(1) parsing algorithm (GLR), later improved in many ways, runs in a linear time on LR(1) grammars, and degrades to a polynomial-time bound if the grammar is not deterministic. We address a useful feature not present in the current GLR(1) methods: the ability to accept grammars of the Extended BNF type (EBNF), the rules of which contain regular expressions. An EBNF grammar is conveniently represented by a collection of finite automata called a Transition Net (TN). We define, analyze and evaluate a new GLR(1) algorithm, called GELR, that combines the recent LR(1) parsing algorithm for TNs with the classical GLR data structures: the Graph-Structured Stack representing multiple stacks, and the Shared Packed Parse Forest for multiple syntax trees. The GELR algorithm is proved correct and an efficient implementation incorporating the state-of-the-art Right-Nulled parsing optimization is available. Experimental measures of the GELR parser size, speed and memory footprint are reported for current programming and web languages, and are compared with those of other parsing algorithms. The findings prove that directly parsing EBNF grammars does not penalize speed. Performance comparisons for different computer languages should also be of interest.
Teaching Mode of Psychology and Pedagogy in Colleges and Universities Based on Artificial Intelligence Technology

Abstract At present, artificial intelligence(AI) technology has penetrated into all aspects of the education field, and the teaching system has been reformed. In the background of AI, deep learning, personalized learning and human-computer collaborative learning have gradually become the mainstream learning methods. The change of learning style directly promotes the transformation of curriculum and teaching paradigm. This paper analyzes the current situation of the use of AI technology in the teaching mode of psychology and Pedagogy in Colleges and universities, and puts forward the research on the teaching mode of psychology and Pedagogy in Colleges and Universities Based on AI technology. In this paper, 290 teachers and students were investigated and analyzed, and their recognition of AI technology reached more than 90%. This paper makes a comparative analysis on the satisfactory learning environment, learning methods and learning effect after the use of AI in psychology teaching, and discusses and analyzes the results. It also puts forward the use suggestions of AI in psychology teaching, which provides guarantee for the development of intelligent teaching system. The research in this paper is of great significance for the further integration and development of the two.
FINN: Feedback Interactive Neural Network for Intent Recommendation

Intent recommendation, as a new type of recommendation service, is to recommend a predicted query to a user in the search box when the user lands on the homepage of an application without any input. Such an intent recommendation service has been widely used in e-commerce applications, such as Taobao and Amazon. The most difficult part is to accurately predict user’s search intent, so as to improve user’s search experience and reduce tedious typing especially on mobile phones. Existing methods mainly rely on user’s historical search behaviors to estimate user’s current intent, but they do not make full use of the feedback information between the user and the intent recommendation system. Essentially, feedback information is the key factor for capturing dynamics of user search intents in real time. Therefore, we propose a feedback interactive neural network (FINN) to estimate user’s potential search intent more accurately, by making full use of the feedback interaction with the following three parts: 1) Both positive feedback (PF) and negative feedback (NF) information are collected simultaneously. PF includes user’s search intent information that the user is interested in, such as the query used and the title clicked. NF indicates user’s search intent information that the user is not interested in, such as the query recommended by the system but not clicked by the user. 2) A filter-attention (FAT) structure is proposed to filter out the noisy feedback and get more accurate positive and negative intentions of users. 3) A multi-task learning is designed to match the correlation between the user’s search intent and query candidates, which can learn and recommend query candidates from user interests and disinterests associated with each user. Finally, extensive experiments have been conducted by comparing with state-of-the-art methods, and it shows that our FINN method can achieve the best performance using the Taobao mobile application dataset. In addition, online experimental results also show that our method improves the CTR by 8% and attracts more than 7.98% of users than the baseline.
Weighted voting of multi-stream convolutional neural networks for video-based action recognition using optical flow rhythms

Two of the most important premises of an ensemble are the diversity of its components and how to combine their votes. In this paper, we propose a multi-stream architecture based on the weighted voting of convolutional neural networks to deal with the problem of recognizing human actions in videos. A major challenge is how to include temporal aspects into this kind of approach. A key step in this direction is the selection of features that characterize the complexity of human actions in time. In this context, we propose a new stream, Optical Flow Rhythm, besides using other streams for diversity. To combine the streams, a voting system based on a new weighted average fusion method is introduced. In this scheme, the weights of classifiers are defined by an optimization process led by a metaheuristic. Experiments conducted on the UCF101 and HMDB51 datasets demonstrate that our method is comparable to state-of-the-art approaches.
Text-to-Text Multi-view Learning for Passage Re-ranking

Recently, much progress in natural language processing has been driven by deep contextualized representations pretrained on large corpora. Typically, the fine-tuning on these pretrained models for a specific downstream task is based on single-view learning, which is however inadequate as a sentence can be interpreted differently from different perspectives. Therefore, in this work, we propose a text-to-text multi-view learning framework by incorporating an additional view---the text generation view---into a typical single-view passage ranking model. Empirically, the proposed approach is of help to the ranking performance compared to its single-view counterpart. Component analysis is also reported in the paper.
Robust Face Alignment via Deep Progressive Reinitialization and Adaptive Error-driven Learning

Regression-based face alignment involves learning a series of mapping functions to predict the true landmarks from an initial estimation of the alignment. Most existing approaches focus on learning efficacious mapping functions from some feature representations to improve performance. The issues related to the initial alignment estimation and the final learning objective, however, receive less attention. This work proposes a deep regression architecture with progressive reinitialization and a new error-driven learning loss function to explicitly address the above two issues. Given an image with a rough face detection result, the full face region is first mapped by a supervised spatial transformer network to a normalized form and trained to regress coarse positions of landmarks. Then, different face parts are further respectively reinitialized to their own normalized states, followed by another regression sub-network to refine the landmark positions. To deal with the inconsistent annotations in existing training datasets, we further propose an adaptive landmark-weighted loss function. It dynamically adjusts the importance of different landmarks according to their learning errors during training without depending on any hyper-parameters manually set by trial and error. A high level of robustness to annotation inconsistencies is thus achieved. The whole deep architecture permits training from end to end, and extensive experimental analyses and comparisons demonstrate its effectiveness and efficiency. The source code, trained models, and experimental results are made available at https://github.com/shaoxiaohu/Face_Alignment_DPR.git.
Multi-domain Dialogue State Tracking with Recursive Inference

Multi-domain dialogue state tracking (DST) is a critical component for monitoring user goals during the course of an interaction. Existing approaches have relied on dialogue history indiscriminately or updated on the most recent turns incrementally. However, in spite of modeling it based on fixed ontology or open vocabulary, the former setting violates the interactive and progressing nature of dialogue, while the later easily gets affected by the error accumulation conundrum. Here, we propose a Recursive Inference mechanism (ReInf) to resolve DST in multi-domain scenarios that call for more robust and accurate tracking capability. Specifically, our agent reversely reviews the dialogue history until the agent has pinpointed sufficient turns confidently for slot value prediction. It also recursively factors in potential dependencies among domains and slots to further solve the co-reference and value sharing problems. The quantitative and qualitative experimental results on the MultiWOZ 2.1 corpus demonstrate that the proposed ReInf not only outperforms the state-of-the-art methods, but also achieves reasonable turn reference and interpretable slot co-reference.
Link Prediction on N-ary Relational Data Based on Relatedness Evaluation

With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction on n-ary relational data. It has even proven that they may cause loss of structure information. To overcome these problems, in this paper, we represent each n-ary relational fact as a set of its role and role-value pairs. We then propose a method called NaLP to conduct link prediction on n-ary relational data, which explicitly models the relatedness of all the role and role-value pairs in an n-ary relational fact. We further extend NaLP by introducing type constraints of roles and role-values without any external type-specific supervision, and proposing a more reasonable negative sampling mechanism. Experimental results validate the effectiveness and merits of the proposed methods.