Reviewerly/exHarmony huggingface dataset:
Transforming NoSQL Database to Relational Database: An Algorithmic Approach

Use of NoSQL to maintain databases has gained popularity in recent times. However, traditional relational databases provide structured data which helps in quick response time to execute certain queries. Currently there exist many algorithms that convert relational to unstructured databases, but vice versa is not available extensively. In this paper we propose an algorithm to convert NoSQL databases to relational databases, i.e., conversion of MongoDB databases to MySQL databases. Five datasets taken from GitHub, namely mobile accessories, bookstore, university database, restaurants and ecommerce store, are used as case studies to show the performance when stored as MongoDB database and as MySQL database. The input is given in JSON format and the output is obtained as MySQL tables. The execution time of the proposed algorithm and the time taken to execute queries are tabulated. It is observed that the execution time of the group-by queries improves by 75.33% when the database is converted to SQL.
Refining Design Spaces in Knowledge Distillation for Deep Collaborative Learning

Knowledge distillation is one of the most widely utilized methods to improve the performance of a model. The knowledge transfer graph has been proposed for deep collaborative learning that enables a rich diversity of bidirectional knowledge distillation. However, exploring a knowledge transfer graph is difficult due to the many potential combinations it can have, so it is not clear how accurate the resultant graphs will actually be. To address this issue, we propose a method for designing the search space with step by step and analyze the trends of graphs to design graphs with high accuracy on the basis of the acquired results. Experiments on the CIFAR-100 dataset show that we confirm that the accuracy of the best knowledge transfer graph in the search space is better than that derived using the asynchronous successive halving algorithm. We also demonstrate that the explored knowledge transfer graphs can be transferred to different datasets.
An intelligent traffic management system adapting image processing techniques in comparison with wireless sensor monitoring

Improving Economic growth of developing countries has promptedvarious vehicular movements in the town roads. Traffic management plays a huge part in regularizing vehicle movements and dodging blockings on roads. The pattern of traffic shall not be similar at all instants during the day and furthermore all the days. Huge traffics are constrained by traffic signals that allows and stops the vehicle movement for a pre-programmed time slots. This conventional method adopting the template have downsides. An intelligent electronic traffic controller can handle the traffic with sense consulting with the requirement at any instant of time. The paper briefs a Digital signal processor (controller) based smart traffic control system with the support of image processing techniques. The frameworkcapture the images from various roads meeting at a junction, recognizes the density of vehicles, analyzes between them, provide priorities, decides the dimensions of time slots for stop and permit and manages the traffic insightfully. Anextraordinarily mounted IR gadget on all the roads with the camera givesdata to the controller about vehicles proceeding onwardcrisis (such as ambulance) and deals with the traffic providing priority to such vehicles. The proposed framework was implemented on a proto mother board with TMS320 DSP processor, and the results are as expected, favorable and encouraging for implementing further developments.
A Reinforcement Learning based RRT Algorithm with Value Estimation

Path planning algorithm is always a heated area of robotics. Researchers propose various of algorithms to meet different requirements. Yet a remaining issue for path planning in unknown environment is that little information is available. For these occasions, researchers proposed sampling-based path planning algorithm such as Rapidly-Exploring Random Trees. This kind of method relies greatly on the sampled points. Noticing that reinforcement learning methods learn about the environment during the interaction process, it is manageable to combine these two methods to improve algorithm’s behavior. In this manuscript, a reinforcement learning based RRT algorithm is proposed to search path in environment with little previous information. The proposed method uses value estimation from reinforcement learning to encourage exploration and makes the agent sample points from less visited area. According to simulation results, the proposed algorithm has higher utilization of the tree nodes and explores more area comparing with RRT algorithm.
SpotFormer: A Transformer-based Framework for Precise Soccer Action Spotting

Action spotting and classification consist in detecting the exact moments at which events occur in long videos. The current mainstream spotting practices generally use a two-stage pipeline that performs feature collection and integration, then salient action detection and postprocessing. Following that, we present SpotFormer, a simple yet effective framework, capable of precise action spotting. Specifically, we employ several most advanced backbone networks as auxiliary feature extractors, and reduce feature dimensionality in a straightforward and efficient way. The frame-wise features are fed into a transformer-based spotting network devised to leverage spatiotemporal information. We obtain 0.609 tight mAP score via model ensemble and achieve the state-of-the-art performance on the SoccerNet-v2 dataset.
The Authentication of Electronic Evidence

As electronic evidence is easy to be changed, falsified or manipulated, it is necessary to ensure the authenticity of electronic evidence in order to be admitted by the courts or arbitral tribunals. This chapter seeks to analyse the method for authenticating electronic evidence. This analysis will be proceed in three steps. Firstly, it identify the requirements of authentication of electronic evidence and the general process of authenticating electronic evidence. It will then examine electronic signature as proof of the authenticity of electronic evidence and the law on electronic signature. Finally, it explains briefly the authentication of the significant electronic evidence in recent years, including: the authentication of electronic document and its printout, the authentication of emails and instant messages, the authentication of printouts or screenshots of websites, and the authentication of electronic evidence obtained from social networking sites.
A Self-Organizing IoT Service Perception Algorithm Based on Human Visual Direction-Sensitive System

The perception layer of the Internet of Things (IoT) needs to respond to service requests rapidly. In the existing IoT perception methods, it remains challenging to concurrently ensure high energy maintenance and perception rate. To address this issue, inspired from the human visual direction-sensitive system, this article proposes an intelligent directional sensitivity-based perception algorithm (DSPA) for IoT service. First, the perception range of each node is divided into multiple regions. The perception direction of the node is represented by an arrow. The perception orientation of the node indicates a region to which the direction belongs. Then, by imitating the human visual direction-sensitive mechanism, direction optimization is proposed for IoT service perception. Meanwhile, region weight is designed to assist in determining the optimal direction to prevent DSPA from getting stuck in local optimums. Finally, simulation results demonstrate that DSPA achieves better energy maintenance and obtains a high perception rate faster than the compared algorithms.
Efficient Mixed-Protocol Secure Four-Party Computation with Private Robustness

Secure honest-majority four-party computation (4PC) protocol was proposed for four mutually-distrusting parties to jointly evaluate a complex function on their private inputs with private robustness by cheater identification. This capability has great potential for distributed private computation, such as distributed privacy-preserving machine learning. However, the adoption of secure 4PC is hampered mainly by the computation and communication costs of online phase of protocol. In this work we propose an efficient secure 4PC protocol with private robustness. Specifically, by transferring hash verification operation for cheating detection to the fourth party, and with the help of the mutual constraint between the four parties, the communication rounds and communication costs of joint message passing primitive can be reduced. To further reduce communication cost, this paper proposes to divide the four parties into two groups and applies the method of two-party multiplication with truncation to avoid multiplication overflow. We implement secure 4PC protocol and demonstrate the efficiency of computation and communication in MNIST multi-class classification training. It achieves <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.1\times$</tex> reduction in computation cost and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.5\times$</tex> reduction in communication cost compared to prior works.
Applying Existing Datasets as a Pseudo Corpus for Sentiment Representation on Social Media

This paper proposes a method to represent the sentiment characteristics of opinions on social media by using some datasets as a pseudo corpus without any annotations. The widespread social media enables us to easily share our own opinions on the Web and communicate with each other. The more the data on social media increase, the more the demands for analysis of the data increase, e.g., text classification and sentiment analysis. Usually, the annotated data should be required in the existing text classification using the supervised machine learning method. However, it is reasonable to say that the criteria for the annotated labels should differ for each period, culture, and independent sense. Effective text classification for such different criteria needs the different types of annotations corresponding to each measure, and it requires much time and human resources. A pseudo corpus consists of multiple existing datasets with different characteristics in the proposed method. The classification model for each dataset is obtained as learning the pseudo corpus. The sentiment of the input text, which domain is different from the learned datasets, is represented as the likelihood distribution for the datasets in the pseudo corpus. This paper discusses the potential and limitations of this idea through the experiment.
A DAC-CLGD-Danet network based method for defaced image segmentation

Based on the problems of high noise, lower contrast, and complex features in defaced images and the low accuracy of existing defaced image segmentation techniques, this paper proposes a defaced image segmentation algorithm based on DAC-CLGD-Danet. Firstly, a CBDNet asymmetric blind denoising network is used for noisecontaining defaced images, and natural and synthetic images are trained together to model the image noise and enhance the denoising ability of natural noise. Secondly, Danet is used as the base network. A Dense Atrous Convolution module (DAC) is added to the dual attention mechanism module to extend the perceptual domain of deep convolution, reduce image feature loss, and enhance the representation of global information and edge features of defaced images; Cross-Level Gating Decoder module (CLGD) is introduced to lighten the segmentation network, enhance image context aggregation, and produce accurate semantic segmentation. The experimental results demonstrated that the method in this paper has a significant effect on the HRF dataset and Cityscapes dataset, with a significant improvement compared with FCN, UNet, and SETR models, with Intersection over Union (IoU) improved by 9.81% and Mean Intersection over Union (mIoU) improved by 3.01% compared with UNet.
Rotation Regularization Without Rotation

In various visual classification tasks, we enjoy significant performance improvement by deep convolutional neural networks (CNNs). To further boost performance, it is effective to regularize feature representation learning of CNNs such as by considering margin to improve feature distribution across classes. In this paper, we propose a regularization method based on random rotation of feature vectors. Random rotation is derived from cone representation to describe angular margin of a sample. While it induces geometric regularization to randomly rotate vectors by means of rotation matrices, we theoretically formulate the regularization in a statistical form which excludes costly geometric rotation as well as effectively imposes rotation-based regularization on classification in training CNNs. In the experiments on classification tasks, the method is thoroughly evaluated from various aspects, while producing favorable performance compared to the other regularization methods. Codes are available at https://github.com/tk1980/StatRot .
An Evolutionary Framework for Modelling Unknown Behaviours of Other Agents

Multiagent decision making becomes complicated when a subject agent interacts with other agents who could be either collaborative or competitive in their common environment. Generally the subject agent does not know what exact behaviours other agents will execute in the coming interactions. This occurs even when the agents are collaborative and cannot fully disclose their behaviours due to their privacy concerns. With the purpose of improving prediction of the true behaviours of other agents, conventional approaches often employ either an exhaust list or a guided search to expand the set of possible behaviours of other agents. In this article, we investigate an evolutionary approach guided by prior knowledge about other agents and develop a genetic algorithm based framework to modelling unknown behaviours, which may contain the true behaviours of other agents. We develop a new framework with a multi-population genetic algorithm in a general multiagent decision model - interactive dynamic influence diagrams, which represents how multiple agents optimise their interactive decisions from the viewpoint of individual agents. The new framework also provides an alternative way to solving the decision model. We demonstrate the performance of the proposed methods in two problem domains and provide empirical results in support.
Tensor-Based Type-2 Extreme Learning Machine with L1-norm and Liu Regression

In the paper, tensor-based type-2 extreme learning single layer neural network is extended with L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm and Liu regression. The L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm and Liu regression regularization methods are merged into tensor-based fuzzy learning system. The antecedent part of the tensor-based fuzzy learning system follows the structure of the tensor-based type-2 extreme learning single layer neural network. To the consequent part of the improved tensor-based type-2 extreme learning single layer neural network, it is a regression problem, the matrix equation that is unfolded from generated tensor is used to solve the learning problem in the consequent part, which provides a basis for the introduction of regularization concurrently. The regularization method is similarly constructed as elastic-net-ELM, the Lasso regression and Liu regression are applied to tensor-based type-2 extreme learning single layer neural network, and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -norm are used to enhance the tensor-based type-2 extreme learning single layer neural network. To the improved tensor-based type-2 extreme learning single layer neural network, the segment of variable selection of elastic-net-ELM is inherited by the tensor-based type-2 extreme learning single layer neural network with Lasso regression and Liu regression, the system performance is improved further by adjusting the two parameters of the tensor-based type-2 extreme learning single layer neural network.
Semantic Characterizations of AGM Revision for Tarskian Logics

Given the increasingly dynamic nature of knowledge in the era of Web-based information exchange, techniques to revise recorded knowledge – such as knowledge graphs or ontologies – with respect to new findings are more important than ever. For knowledge representation approaches based on formal logics, the AGM belief revision postulates by Alchourrón, Gärdenfors, and Makinson continue to represent a cornerstone in research related to belief change. Katsuno and Mendelzon (K &M) adopted the AGM postulates for changing belief bases and characterized AGM belief base revision in propositional logic over finite signatures. We generalize K &M’s approach to (multiple) base revision in arbitrary Tarskian logics, covering all logics with a classical model-theoretic semantics and hence a wide variety of logics used in knowledge representation and beyond. Our generic formulation applies to various notions of “base”; such as belief sets, arbitrary or finite sets of sentences, or single sentences. The core result is a representation theorem showing a two-way correspondence between AGM base revision operators and certain “assignments”: functions mapping belief bases to total — yet not transitive — “preference” relations between interpretations. We also provide a characterization of all Tarskian logics for which our result can be strengthened to assignments producing transitive preference relations as in K &M’s original work.
Realism Assessment for Synthetic Images in Robot Vision through Performance Characterization

Synthetic image generation plays a crucial role in the development of robot vision algorithms, circumventing manual data collection. However, the realism of synthetic images could affect the performance of the algorithms when applied in real-world settings. In this study, we propose a framework to quantitatively assess the realism of synthetic images using a set of realism metrics as a means of performance characterization. We use a commercial rendering engine as a test-bed for generating synthetic images and ascertain that a set of rendering parameters affect specific image metrics through statistical hypothesis testing. We demonstrate that this framework can be used to optimize rendering parameter values and generate synthetic datasets with improved performance on downstream robot vision tasks such as instance segmentation.
An Improved Practical Byzantine Fault Tolerance Consensus Algorithm

As the underlying technology of bitcoin, blockchain is essentially a distributed ledger that can only add data, which is shared and maintained among unreliable nodes. Consensus algorithm is one of the core technologies of blockchain, which is used to solve the consistency problem of distributed system. Aiming at the problems of Practical Byzantine Fault Tolerance consensus algorithm, such as high communication consumption, low scalability, and nodes can not to dynamically join and exit the system, an improved Practical Byzantine Fault Tolerance algorithm is proposed, which reduces the complexity of the algorithm and improves the efficiency of consensus on the premise of ensuring the reliability of the consensus mechanism, and designs the join and exit mechanism of nodes. The experimental results show that the improved Practical Byzantine Fault Tolerance algorithm can significantly reduce the completion time of transaction consensus and the number of communication between nodes, so as to support more nodes, reduce the system communication consumption, and CPU computing resource consumption, and increase the throughput of the blockchain system.
Evidence-Based Document-Level Event Factuality Identification

The existing Document-Level Event Factuality Identification (DEFI) work relies on the syntactic and semantic features of event trigger and sentences. However, focusing only on the relevant features of event trigger may omit the important information for event factuality identification, while finding critical information from the whole document is still challenging. In this paper, our motivation is that DEFI can be inferred from a complete set of evidential sentences rather than the event trigger. Hence, we construct a new Evidence-Based Document-Level Event Factuality (EB-DLEF) corpus, and introduce a new evidential sentence selection task for DEFI. Moreover, we propose a pipeline approach to solve the two-step work of evidential sentence selection and event factuality identification, which outperforms various baselines.
Structure-oriented filtering in Crossplotting and &lt;i&gt;k&lt;/i&gt;-means

Several authors have proposed new techniques using multi-attribute analysis and machine learning. Studying the influence of different data treatments on such techniques is essential. We analyze the results by applying two clustering techniques, Crossplotting, and k-means, in filtered data. In particular, we use structure-oriented filtered seismic data before calculating seismic attributes. We use a migrated section of the Buzios field from the Brazilian pre-salt in the Santos Basin. We find that combining filtering and clustering techniques can improve salt identification.
HashC: Making DNNs’ Coverage Testing Finer and Faster

Though Deep Neural Networks (DNNs) have been widely deployed and achieved great success in many domains, they have severe safety and reliability concerns. To provide testing evidence for DNNs’ reliable behaviors, various coverage testing techniques inspired by traditional software testing have been proposed. However, the coverage criteria in these techniques are either not fine enough to capture subtle behaviors of DNNs, or too time-consuming to be applied on large-scale DNNs. In this paper, we develop a coverage testing framework named HashC, which makes mainstream coverage criteria (e.g., NC and KMNC) much finer. Meanwhile, HashC reduces the time complexity of combinatorial coverage testing from polynomial time to linear time. Our experiments show that, 1) the HashC criteria are finer than existing mainstream coverage criteria, 2) HashC greatly accelerates combinatorial coverage testing and can handle the testing of large-scale DNNs.
Jerk-continuous Online Trajectory Generation for Robot Manipulator with Arbitrary Initial State and Kinematic Constraints

This work presents an online trajectory generation algorithm using a sinusoidal jerk profile. The generator takes initial acceleration, velocity and position as input, and plans a multi-segment trajectory to a goal position under jerk, acceleration, and velocity limits. By analyzing the critical constraints and conditions, the corresponding closed-form solution for the time factors and trajectory profiles are derived. The proposed algorithm was first derived in Mathematica and then converted into a C++ implementation. Finally, the algorithm was utilized and demonstrated in ROS & Gazebo using a UR3 robot. Both the Mathematica and C++ implementations can be accessed at https://github.com/Haoran-Zhao/Jerk-continuous-online-trajectory-generator-with-constraints.git