Reviewerly/exHarmony huggingface dataset:
Manipulator Motion Planning via Centralized Training and Decentralized Execution Multi-Agent Reinforcement Learning

Motion planning aims to solve that the manipulator can find the path from the initial position to the target position under the condition of satisfying the constraints. Traditional motion planning methods usually transform the problem into finding the optimal solution of objective function under constraint conditions or some sampling-based method, but these methods can’t cope well with the unstructured and incomplete observation environment. In recent years, reinforcement learning has proved to be a promising direction to solve this problem, but sports planning based on reinforcement learning also faces many problems, such as low efficiency of data utilization, dimension disaster, and insufficient robustness. We try to compress the dimensions of the motion space. Instead of using the manipulator directly as the object of the motion planning policy output action, consider the action output of each joint motor locally. We propose an approach in which each joint of the manipulator is modeled as an agent, and use multiagent centralized training and decentralized execution multiagent reinforcement learning method to plan the motion of the manipulator. The experiment shows that we can greatly shorten the training time of the motion planning task and obtain better positioning accuracy and environmental robustness.
Human Action Recognition using Skeleton features

Many research works proposed in the field of action recognition based on the pose estimated in each frame. But most of these research works demanded a large amount of computing to store and process the collection of video frames. So we observed that Action recognition is computationally expensive. Our main objective was to find suitable features and classify human behavior based on action recognition. So to address these problems of storing and processing video input RGB frames, we came up with a new approach that is computationally not expensive. We convert the videos into skeleton-based Spatial-temporal Graphical (ST_GCN) frames that can detect human action more accurately than the direct RGB input. To derive solid development designs from these diagrams, a broad range and multi-scale setting total and perceptual demonstrating region unit significant parts of a solid feature extractor is required. We initially used tensor flow pose estimation CMU, Mobile-Net Pre Trained-Models by Open Pose and Tensor flow are used to train on the COCO dataset to extract key feature points. Then, NTU RGB D 60 3D skeleton data from Rapid-Rich Object Research Lab and MS_G3D model, which has more substantial generalization capability, have opted, and performance measured based on the model's accuracy on test data. Moreover, our models could profoundly classify a human's abnormal and normal behavioral patterns of a human based on their actions.
Is open source eating the world's software?

That open source software comprises an increasingly large percentage of modern software applications has become conventional wisdom. The exact extent to which open source software constitutes today's applications is indeterminate, however, at least by the standards of the academic software engineering research community. This paper proposes a methodology and associated tool that can analyze Java binaries and determine the proportion of open source that comprises them. This paper also presents empirical measurements of 5 commercial Java software systems, reporting OSS proportions between 76.2% to 99.9% among these 5 systems, including a historical analysis covering 6 versions and 12 years for one of the subject systems.
Random Deep Graph Matching

Graph matching endeavors to find corresponding nodes across two or more graphs, which plays a fundamental role in many vision and pattern matching tasks. However, existing graph matching algorithms often meet abnormal graphs with missing node features and suffer from numerous cluttered outliers in practical applications. To address these, we propose a novel deep graph matching method called Random Deep Graph Matching (RDGM). Different from the deterministic affinity inference in existing deep graph matching methods, RDGM performs message passing in a random manner during model training through randomly masking some available node features in the source or target graph, so that the affinity inference between nodes is insensitive to specific neighborhoods. In addition, a hierarchical attention graph neural network framework is devised in the node embedding process of RDGM, which can obtain more sufficient high-order structural information to reduce the impact of latent noise on affinity learning. Extensive experiments suggest that the proposed RDGM outperforms state-of-the-art graph matching methods, and demonstrates strong robustness and generalization performance.
Augmented Reality-Based Lifelogging System for Reminiscence

In recent times, Human–Computer Interaction(HCI)-related research has shown an increasing interest in systems designed for supporting human memory. As regards, the extent to which Augmented Reality (AR) being used to experience the reality is blooming and being applied to each and every sector of HCI thoroughly. In this paper, we are presenting a mobile-based system and its design, build-out, and procedure to reminisce past memories using lifelogging system where users store images with related details in cloud storage based on location. When the user comes back to the location, using the concept of geo-location-based AR, the stored images are augmented into the pre-existing reality. As a result of tapping onto the objects, the pre-stored images with provided details are overlaid on the view screen. We propose that such approaches can encourage more mindful engagement with lifelogging system and AR promoting positive effects linked to reminiscing with a little to no cognitive work.
Visualization for Machine Learning

As machine learning has increased in popularity, visualization has taken an important role in analyzing and communicating aspects of machine learning models. Increasingly, visualization techniques are being used across a broad set of domains and in business-critical use cases. Oftentimes, these visualizations convey non-trivial machine learning concepts, utilize complex visual representations, or demand user interaction. In this tutorial, we seek to provide a foundational understanding, to a broad audience, of the ways in which we can use visualization for machine learning tasks. In particular, we detail visual techniques for model assessment, model understanding, and dimensionality reduction. Furthermore, we outline foundations and recent work in emerging visualization topics such as topological data analysis and understanding deep learning model internals.
Semantic Web Technologies: Issues and Possible Ways of Development

Semantic digital technologies are as useful as they are difficult to implement. During its existence, the concept of the Semantic Web has still found very limited application and extremely slow development. The reasons for this are serious differences in the structure of hypertext and the Semantic Web as a superstructure above it, and the rules that circumscribed the development of the internet. Semantic search engines still do not implement semantic search in its pure form: various data organization schemes and data visualization formats make it a little easier to analyze facts, add them to databases, but do not make knowledge bases out of the latter, do not allow working with facts as with knowledge. In addition, the user interfaces of such systems tend to be either very complex or counter-intuitive. Nevertheless, there is every reason to believe that with the use of modern technologies, the ideas of semantization of the web and its content can already be improved and gradually implemented: for this it is necessary to get rid of some false expectations of semantic technologies, to direct efforts towards the development of web native formats of semantic markup and try to take advantage of the increasingly common voice input of household devices.
Using Query-Based System with Slicing for Web Services Optimization

The contender web services are increasing explosively over the internet. The ability to publish, find, and construct web services in an open, dynamic network environment has emerged as among the most significant difficulties for web-services technologies. Therefore, we need an optimization system to effectively access and use web services. The strategy for achieving web service optimization proposed in this paper is based on program slicing. It provides an efficient means of using queries to access Web Services. This work offers customers a sophisticated, easy, intuitive, and robust query-based invocation system by abstracting away the core complications in the specification and toolkits of ML-based Web services. The execution and invocation of Web services are automated by our system using the Program Slicing and Caching approach. Experiment findings demonstrate the viability and adaptability of our web services optimization technique.
Deep Learning Approach for Emotion Recognition Analysis in Text Streams

Social media sites employ various approaches to track feelings, including diagnosing neurological problems, including fear, in people or assessing a population public sentiment. One essential obstacle for automatic emotion recognition principles is variable with fluctuating limitations, language, and interpretation shifts. Therefore, in this paper, a deep learning-based emotion recognition (DL-EM) system has been proposed to describe the various relational effects in emotional groups. A soft classification method is suggested to quantify the tendency and allocate a message to each emotional class. A supervised framework for emotions in text streaming messages is developed and tested. Two of the major activities are offline teaching assignments and interactive emotion classification techniques. The first challenge offers templates in text responses to describe sentiment. The second activity includes implementing a two-stage framework to identify live broadcasts of text messages for dedicated emotion monitoring.
Secure outsourced VANET data in clouds with designated supervisor

With the rapid development of information technology, a large number of vehicles and roadside units can be communicated through vehicular ad-hoc network (VANET). The information collected by vehicles can be shared with other entities such as vehicles and traffic management department to improve traffic condition. In order to facilitate the storage of massive data, it is a better way to maintain data at clouds, and allow some supervisors to audit data integrity. This paper introduces a secure cloud storage scheme with designated supervisor (SODS) on bilinear groups, which allows vehicles to upload the collected data to clouds, and only the designated supervisor to verify data integrity before using these data. Security and performance analyses demonstrate that the proposed scheme is secure and efficient.
Word-Level Fine-Grained Story Visualization

Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story with a global consistency across dynamic scenes and characters. Current works still struggle with output images' quality and consistency, and rely on additional semantic information or auxiliary captioning networks. To address these challenges, we first introduce a new sentence representation, which incorporates word information from all story sentences to mitigate the inconsistency problem. Then, we propose a new discriminator with fusion features and further extend the spatial attention to improve image quality and story consistency. Extensive experiments on different datasets and human evaluation demonstrate the superior performance of our approach, compared to state-of-the-art methods, neither using segmentation masks nor auxiliary captioning networks.
Quora Question Pairs Identification and Insincere Questions Classification

Quora is a question-answering site where people ask questions and reply to the existing questions which makes Quora a great interactive platform but it also has few challenges such as the occurrence of duplicate questions which lead to ambiguity and insincere questions that degrade the value of the site. In this research work, we have proposed a method to overcome these two challenges by using techniques of Natural Language Processing (NLP) and Deep Learning (DL). Five different word embeddings were used for both the problems, Bidirectional Long Short-Term Memory (BiLSTM) and Bi-Gated Recurrent Unit (BiGRU) architecture with attention mechanism were used for insincere question classification and Siamese Manhattan Long Short-Term Memory (MaLSTM) architecture were used for question pairs identification. The implemented models are performing well, in terms of accuracy, precision, recall, and F1 Score. Our research work has achieved the highest accuracy of 90% and highest F1 score of 0.89 by using Paraphrase-MiniLM-L6-v2 + Siamese MaLSTM for Quora Question Pairs Identification and for Insincere Questions Classification our model achieved the highest accuracy of 95% and highest F1 score of 0.82 by using FastText + BiLSTM + BiGRU. Our results were compared with literature and our research work has outperformed baseline models.
Piecemeal Clustering: a Self-Driven Data Clustering Algorithm

Various approaches have been discussed in the literature for the clustering of data, such as partitioning, hierarchical, and machine learning methods. Most of the approaches require some prior knowledge about the clusters, such as their total number. Furthermore, some previous algorithms are not robust enough to process higher-dimensional data or require a large amount of memory for computations. We propose, herein, a data clustering algorithm, Piecemeal Clustering, that successfully clusters data without prior knowledge of the number of clusters. The proposed clustering algorithm uses the similarity and density of the data to identify the number of clusters in the data set and works with both low- and high-dimensional data. We demonstrate the power of the proposed Piecemeal Clustering algorithm with two real-world data sets. It is found that the proposed algorithm outperforms seven other state-of-the-art algorithms on both of these data sets.
Understanding Uncertainty Maps in Vision with Statistical Testing

Quantitative descriptions of confidence intervals and uncertainties of the predictions of a model are needed in many applications in vision and machine learning. Mechanisms that enable this for deep neural network (DNN) models are slowly becoming available, and occasionally, being integrated within production systems. But the literature is sparse in terms of how to perform statistical tests with the uncertainties produced by these overparameterized models. For two models with a similar accuracy profile, is the former model's uncertainty behavior better in a statistically significant sense compared to the second model? For high resolution images, performing hypothesis tests to generate meaningful actionable information (say, at a user specified significance level <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha=0.05)$</tex> is difficult but needed in both mission critical settings and elsewhere. In this paper, specifically for uncertainties defined on images, we show how revisiting results from Random Field theory (RFT) when paired with DNN tools (to get around computational hurdles) leads to efficient frameworks that can provide a hypothesis test capabilities, not otherwise available, for uncertainty maps from models used in many vision tasks. We show via many different experiments the viability of this framework.
IoT Based Dual Axis Solar Tracker and Irradiation Forecasting Using Machine Learning

This work presents the development of a Dual axis solar tracker along with an IoT-based fully automated data logger and a computer program for the prediction of solar irradiance. The proposed work aims to measure, record, analyse the performance of the solar irradiance all day without the use of traditional methods and any human effort and further use the stored data for the solar irradiance forecasting for the forth-coming months by using eXtreme Gradient Boosting(XGBoost) machine learning algorithm. The hardware includes a pair of Arduino Uno microcontroller, a current sensor, voltage sensor, relay and Wi-Fi module along with the two servo motors acting as actuators, Light- Dependent Resistor (LDRs) are used as light detection sensors which detects the solar rays. Further, the motor drives the solar panel towards the position of maximum incoming light. The IoT platforms “Thingspeak” and “IFTTT” are used to record and track performance of the solar irradiance respectively.
Improved hyperspectral target tracking algorithm

Aiming at the problem of material-based background sensing correlation filter in target deformation, the original algorithm MHT is improved. Firstly, the abnormal judgment of the response diagram is added to the original MHT algorithm. If there is no abnormal response diagram of the tracking target, the original MHT algorithm is used for target tracking. If the response graph of the tracking target is abnormal, the abnormal response graph is suppressed by the anomaly suppression algorithm, and then the suppressed response graph is input back to the MHT algorithm. Finally, the Whisper hyperspectral video dataset is used to track the algorithm. The test results show that the improved algorithm improves the tracking success rate and tracking accuracy compared with the original algorithm MHT.
Design and implementation of IPv6-based face recognition in attendance management system

An IPv6-based face recognition attendance management system is proposed to improve the lack of intelligence of the existing system and improve the attendance accuracy and efficiency of attendance. This system integrates the technologies of internet of things, face recognition, IPv6, data analysis, web services, etc., and upgrades a layered software architecture, mainly including face data collection and transmission layer, face comparison information processing layer, and attendance information dissemination layer. The system has functions such as automatic face capture, face platform recognition and comparison, real-time statistics query information, etc. The application testing shows that the system can effectively collect face data of teachers and students for attendance, and has good stability of data transmission and stable human-computer operation interface. The system can provide campus service with the face as the identity mark for all teachers and students, which is of great significance for promoting the automation, intelligence and informatization of attendance.
A Unified Engine for Accelerating GNN Weighting/Aggregation Operations, With Efficient Load Balancing and Graph-Specific Caching

Graph neural networks (GNNs) analysis engines are vital for real-world problems that use large graph models. Challenges for a GNN hardware platform include the ability to 1) host a variety of GNNs; 2) handle high sparsity in input vertex feature vectors and the graph adjacency matrix and the accompanying random memory access patterns; and 3) maintain load-balanced computation in the face of uneven workloads, induced by high sparsity and power-law vertex degree distributions. This article proposes GNNIE, an accelerator designed to run a broad range of GNNs. It tackles workload imbalance by 1) splitting vertex feature operands into blocks; 2) reordering and redistributing computations; and 3) using a novel flexible MAC architecture. It adopts a graph-specific, degree-aware caching policy that is well suited to real-world graph characteristics. The policy enhances on-chip data reuse and avoids random memory access to DRAM. GNNIE achieves average speedups of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7197\times $ </tex-math></inline-formula> over a CPU and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$17.81\times $ </tex-math></inline-formula> over a GPU over multiple datasets on graph attention networks (GATs), graph convolutional networks (GCNs), GraphSAGE, GINConv, and DiffPool. Compared to prior approaches, GNNIE achieves an average speedup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> over HyGCN (which cannot implement GATs) for GCN, GraphSAGE, and GINConv. GNNIE achieves an average speedup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.3\times $ </tex-math></inline-formula> over AWB-GCN (which runs only GCNs), despite using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.4\times $ </tex-math></inline-formula> fewer processing units.
Calibration of the Multiple Choice Machine Reading Comprehension

Prediction calibration devotes to making the model produce correct prediction probability, corresponding with the model's empirical measurement (i.e., the accuracy on benchmark). It plays a crucial role in the Multiple-Choice based Machine Reading Comprehension (MCRC) task. Once the model gives the wrong candidate answer with a high probability, users or downstream applications will not trust the model easily. However, few works pay attention to the prediction calibration of MCRC models. In this paper, we study the prediction calibration of the MCRC models and introduce the self-supervised target label softening (SS-TLS) training method to develop a well-calibrated MRC model while improving its performance. Specifically, the proposed SS-TLS method softens the target label to train the MCRC model instead of the standard cross-entropy objective. It employs the self-supervised confidence signal to monitor the soften scale adaptively at the instance level. Experimental results on several multiple-Choice style MRC datasets illustrate that the proposed method can improve both model prediction calibration and performance.
Detection of Cluster Anomalies With ML Techniques

Due to the increasing complexity of computing clusters, it becomes more challenging to identify erroneous behavior inside them. Monitoring systems collect large amounts of data to provide analysis of cluster behavior. Their manual study is expensive and sometimes even impossible. Hence, a system capable of analyzing the gathered data and then, based on those data, detecting anomalies within the cluster is essential. Our paper proposes a system for detecting an anomaly in a Kubernetes cluster (KAD - Kubernetes Anomaly Detector). KAD uses machine learning techniques to diagnose problems that may arise. The novelty of our solution lies in proposing the concept of using various machine learning models that facilitate the detection of different types of anomalies. The user can choose which model to use for anomaly detection in the given situation. The KAD system can also automatically select the appropriate model. The selection is based on scoring procedures. The system trains the models using historical data that describe the usual behavior of the cluster. Then it detects anomalies based on the predictions of the trained model or signal reconstructions. Finally, in two groups of experiments, we evaluate the proposed concepts. The experiments confirm the importance of selecting the proper ML model to detect anomalies in different situations. Furthermore, the experiments assess the latency of the responses in a production-ready cluster.