Reviewerly/exHarmony huggingface dataset:
Policy and Value Deep RL for Temporal Language-Agnostic Street Image Captioning

Dashcams and street cameras capture a huge amount of street and traffic video data. Temporal image captioning for such video data has been approached with an encoder-decoder framework and achieved substantial success in captioning accuracy. Most recently, policy and value deep reinforcement learning (PVRL) emerged as an outperformer over other decision-making frameworks for image captioning. In this paper, we design a framework that utilizes PVRL on an inhouse dataset containing temporal images of East Asia streets as a step towards designing a language-agnostic street image captioning framework that is capable of captioning temporal images of any street regardless of location. For language- invariance, the framework includes cross-modal retrieval at the character level so that similar words in different languages but in the same word-embedding space are grouped together. Our results show that PVRL can be applied successfully to temporal video captured the streets and achieve natural semantic captions; preliminary studies on our dataset suggest that the framework is capable of use in multi-language scenarios.
Research on Library Information Management under Computer

Abstract With the development of science and technology, computer technology is becoming more and more advanced. Nowadays, computers have been widely used in people’s lives, gradually changing people’s way of life. The application of computer technology has brought great convenience to people’s life. The combination of computer and library information management makes library management more convenient and fast. At the same time, the use of computer software also saves a lot of manpower and material resources, and the book management work becomes very simple.
A Hybrid Approach to Predict Election Candidate Success Using Candidate Speech and Voter Opinion

Election candidate success is a prediction of the winning rate of the candidate. Sentiment analysis on user's social media data plays a prominent role in prediction. It refers to a classification problem where the main goal is to classify data into positive and negative sentiments. Sentiment analysis over user's Twitter data offers an effective way to measure voter opinion towards the candidate. As election forecasting based on the only opinion of the voter is difficult, the proposed system come up with a hybrid approach in which sentiment analysis on user's Twitter data and personality prediction on candidate's speech data is performed. The system highlights the performance of various classifiers. Experimental results show that the logistic regression classifier outperformed the other classifiers.
Coherent Dialog Generation with Query Graph

Learning to generate coherent and informative dialogs is an enduring challenge for open-domain conversation generation. Previous work leverage knowledge graph or documents to facilitate informative dialog generation, with little attention on dialog coherence. In this article, to enhance multi-turn open-domain dialog coherence, we propose to leverage a new knowledge source, web search session data, to facilitate hierarchical knowledge sequence planning, which determines a sketch of a multi-turn dialog. Specifically, we formulate knowledge sequence planning or dialog policy learning as a graph grounded Reinforcement Learning (RL) problem. To this end, we first build a two-level query graph with queries as utterance-level vertices and their topics (entities in queries) as topic-level vertices. We then present a two-level dialog policy model that plans a high-level topic sequence and a low-level query sequence over the query graph to guide a knowledge aware response generator. In particular, to foster forward-looking knowledge planning decisions for better dialog coherence, we devise a heterogeneous graph neural network to incorporate neighbouring vertex information, or possible future RL action information, into each vertex (as an RL action) representation. Experiment results on two benchmark dialog datasets demonstrate that our framework can outperform strong baselines in terms of dialog coherence, informativeness, and engagingness.
GCNNIRec: Graph Convolutional Networks with Neighbor Complex Interactions for Recommendation

In recent years, tremendous efforts have been made to explore features contained in user-item graphs for recommendation based on Graph Neural Networks (GNN). However, most existing recommendation methods based on GNN use weighted sum of directly-linked node’s features only, assuming that neighboring nodes are independent individuals, neglecting possible correlations between neighboring nodes, which may result in failure of capturing co-occurrence signals. Therefore, in this paper, we propose a novel Graph Convolutional Network with Neighbor complex Interactions for Recommendation (GCNNIRec) focused upon capturing possible co-occurrence signals between node neighbors. Specifically, two types of modules, the Linear-Aggregator module and the Interaction-Aggregator module are both inside GCNNIRec. The former module linearly aggregates the features of neighboring nodes to obtain the representation of target node. The latter utilizes the interactions between neighbors to aggregate the co-occurrence features of nodes to capture co-occurrence features. Furthermore, empirical results on three real datasets confirm not only the state-of-the-art performance of GCNNIRec but also the performance gains achieved by introducing Interaction-Aggregator module into GNN.
A Continuous Control Method for Task Stability of Visual Servo System

A robotic visual servo system using the sensor image as task target provides the possibility to expand the applications of industrial robots. However, the stability of the robot servo motion is not sufficient for industrial applications that have high precision requirements. In this paper, an adaptive visual servo algorithm based on second-order non-homogeneous linear differential equations (SNLDE) is proposed. By achieving a smooth transition during the change of robot joint velocities between two iterations of consecutive image tasks, this algorithm aims to improve the stability and reliability of the robotic system. Velocity changes of the robotic system under a traditional algorithm, first-order non-homogeneous linear differential equations (FNLDE) based algorithm and the SNLDE algorithm are compared in MATLAB, and the theoretical performance is verified by these tests. Subsequently, the stability of a robot under the three aforementioned algorithms is tested in a laboratory using six-axis industrial robots. The test results corroborate the effectiveness of the algorithm proposed in this paper.
Labmat: Learned Feature-Domain Block Matching For Image Restoration

Grouping of similar patches, called block matching, has been widely used in image restoration applications. Popular block matching algorithms exploit image non-local similarities in spatial or a fixed transform domain, e.g., wavelets and DCT. However, applying these methods on corrupted patches usually leads to degraded matching accuracy, thus limiting the image restoration performance. In this work, we develop a novel methodology for performing block matching in a supervised way by learning multi-layer sparsifying transforms. The proposed learned transform-domain block matching method for image restoration, dubbed LABMAT, is shown to have better accuracy in terms of clustering similar blocks in the presence of noise, and it also achieves an improved denoising performance when it is incorporated into popular non-local denoising schemes.
RumorClock: Visual Representation of Online Rumor Spreading

Information visualization has brought huge boons to multifarious areas such as sociology analysis, large hierarchies’ representation, and big data demonstration. Specifically, online rumors spreading has become rampant due to the advanced development of social media. Effective governance of online rumors serves as a pivotal requirement for tycoons in social media areas like Facebook, Twitter, and Instagram. However, due to the tremendous volume of everyday rumors and their dynamic essence, pure texts or simple graphs can hardly demonstrate them accurately and comprehensively. Thus, in this study, we proposed a new intuitive visualization model called RumorClock, which can handle large scale of rumors as well as compare holistically between different rumors across time.
Modular quantum computing and quantum-like devices

The two essential ideas in this paper are, on the one hand, that a considerable amount of the power of quantum computation may be obtained by adding to a classical computer a few specialized quantum modules and, on the other hand, that such modules may be constructed out of classical systems obeying quantum-like equations where a space coordinate is the evolution parameter (thus playing the role of time in the quantum algorithms).
The Path Choice of Liaoning Province's Industrial Transformation and Upgrade

research-article The Path Choice of Liaoning Province's Industrial Transformation and Upgrade Share on Authors: Xiaoxue Lu Dalian Neusoft University of Information, China Dalian Neusoft University of Information, ChinaView Profile , Xiaozhu Wang Dalian Neusoft University of Information, China Dalian Neusoft University of Information, ChinaView Profile , Lin Wei Dalian Municipal Statistics Bureau, China Dalian Municipal Statistics Bureau, ChinaView Profile Authors Info & Claims ICIMTECH 21: The Sixth International Conference on Information Management and TechnologyAugust 2021 Article No.: 50Pages 1–5https://doi.org/10.1145/3465631.3465681Online:19 August 2021Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An improved gravitational search algorithm for distributed generation optimization

Abstract In order to optimize allocation and sizing of distributed generation, this paper puts forward an improved gravitational search algorithm based on particle swarm optimization. For the sake of preventing the gravitational search algorithm from falling into local optimum, mutation is added to the formula of position, and only Kbest particles are searched. The objective function to be optimized is the minimization of the power losses, and the IEEE-33 bus system is adopted to test the efficiency of the improved algorithm. The results indicate that the improved gravitational search algorithm has better convergence accuracy than the basic gravitational search algorithm, and that the voltage of the system can be improved.
Recover The Residual Of Residual: Recurrent Residual Refinement Network For Image Super-Resolution

Benefiting from learning the residual between low resolution (LR) image and high resolution (HR) image, image super-resolution (SR) networks demonstrate superior reconstruction performance in recent studies. However, for the images with rich texture information, the residuals are complex and difficult for networks to learn. To address this problem, we propose a recurrent residual refinement network (RRRN) to gradually refine the residual with a recurrent structure. Instead of directly reconstructing the residual between LR image and HR image, each sub-network in our framework reconstructs the residual between SR image from previous stage and HR image, i.e. recovers the residual of residual (RoR). Considering the domain gap between the image feature and the RoR feature, we introduce a residual projection block to explicitly transform the feature from image domain to RoR domain. The RoR feature is further optimized in an iterative up- and down-sampling manner with a residual learning block. We construct the structure of each block based on the optimization methods of conventional SR and improve our network with dense connections. Experimental results prove that our method improves the quality of super-resolution images on different datasets with variable scenes.
Detection method and system design for bubble defects of glass insulator

Bubble defect is a common defect of glass insulator, which needs to be detected and removed before putting into operation. In this paper, two kinds of defect detection methods base on uniform light and laser are studied, the characteristics of the two methods are compared and analyzed. Combine its advantages, a set of bubble defect detection scheme using uniform light source rapid screening and laser light source accurate verification is determined, and the defect detection system is designed. The system’s physical verification shows that the system can quickly and effectively complete the identification and location of bubble defects in glass insulators, has high accuracy and anti-interference ability, can meet the needs of automated detection, and has certain application value.
Investigating Overlapped Strategies to Solve Overlapping Problems in a Cooperative Co-evolutionary Framework

Cooperative co-evolution is recognized as an effective approach for solving large-scale optimization problems. It breaks down the problem dimensionality by splitting a large-scale problem into ones focusing on a smaller number of variables. This approach is successful when the studied problem is decomposable. However, many practical optimization problems can not be split into disjoint components. Most of them can be seen as interconnected components that share some variables with other ones. Such problems composed of parts that overlap each other are called overlapping problems. This paper proposes a modified cooperative co-evolutionary framework allowing to deal with non-disjoint subproblems in order to decompose and optimize overlapping problems efficiently. The proposed algorithm performs a new decomposition based on differential grouping to detect overlapping variables. A new cooperation strategy is also introduced to manage variables shared among several components. The performance of the new overlapped framework is assessed on large-scale overlapping benchmark problems derived from the CEC’2013 benchmark suite and compared with a state-of-the-art non-overlapped framework designed to tackle overlapping problems.
Intra To Inter: Towards Intra Prediction for Learning-Based Video Coders Using Optical Flow

Traditional video coders often rely on a block structure for transmission. Here each block is coded separately and sequentially and for each block the encoder can decide whether to use intra or inter prediction. This way, inter and intra prediction can be mixed within a single frame. This has advantages when new areas are uncovered, which were not present in the reference frame, and can hence not be predicted well. These areas are typically predicted using intra prediction. Currently much research goes into end-to-end-trained video coders which do not operate on a block level and typically use dense motion fields for inter prediction. There it is more difficult to incorporate intra prediction for uncovered regions. In this paper we propose a novel concept which enables us to reinterpret classical angular intra prediction in a way that we can transmit it as part of the dense motion field. We can save an average of 18% rate for the transmission of the motion vectors for the same quality of the prediction image.
Virtual assistance using question generation Answering

Question Generation and Answering, being a complex undertaking, has gained significant attention in the earlier years. Although significant leap forwards are accomplished, it needs imperative optimization when used in a real-time system. The paper proposes an approach to enhance the orthodox methods to support the collegiate programs by introducing a virtual assistant that answers any questions pertinent to the context; without letting the users read the internal document(s). With the help of Twilio API, a cloud-based architecture capable of generating a question-answer pair is brought into the WhatsApp interface, offering a user-friendly experience.
The method of calibration for the robot arm capable of adjusting position of the center of gravity and the evaluation of the motion in the air

The motion of an arm installed to a multicopter can possibly disturb its orientation. To realize an aerial manipulation by using a multicopter with a robot arm, a control of its center of gravity (COG) is required. In previous study, we proposed a design method for the mechanism of a robotic arm suitable for a multicopter. In the proposed design method, the arm can adjust its COG due to redundancy degrees of freedom. For safe flight, however, it is necessary to obtain its parameters utilized for multicopter control before take-off. In this research, we propose a method to estimate parameters to adjust COG with simple experiment before take-off. Moreover, to suppress the number of times of measurements and parameters variation, we obtain appropriate calibration orientations by simulation and confirm the effectiveness by calibration experiment using the obtained orientations. Finally, we fly a multicopter with the developed arm and observe a rotational speed of the propeller using a high-speed camera and evaluate the effect of the arm movement on the multicopter.
Shock wave

The EVOQ tool offers researchers in social sciences a set of text analysis tools relying on the post-structuralist approach. This analysis approach relies on the identification of association and opposition relations between terms (words or expressions). The so-defined graph is presented in EVOQ by a node-link diagram. The Shock Wave is a placement algorithm specifically designed to be combined with a classical force-directed algorithm to produce a graph layout which meets the interpretability needs of the text analysts while preserving efficiency on large numbers of nodes. It structures the nodes on a circular placement with transversal opposition relations to highlight oppositions within the text concepts. Beyond our use case, the interest of Shock Wave lies in the fact that it is a novel method to present graphs of text with a strong emphasis on underlying semantic fields.
Analysis the Value of Characters on the Short Story in the Students 'Book of Class III SD Theme 8 Praja Muda Karana

This research is motivated by findings during initial field observations that the majority of teachers do not place too much importance on character values in literature learning in elementary schools. In fact, literary learning is an educational tool that plays a major role in the world of education to shape and develop children's personalities. literature plays a role as character building which is believed to have a major role in the development of character in children so that they are able to develop human beings who are completely fun from an early age.Short stories, as a part of literary work that will be taught to children, actually can be analyzed the character values they have so that the positive energy can be transmitted to children. The purpose of this study was to analyze the character values in the short stories in the third grade students' book of the theme 8 Praja Muda Karana. The approach used is a qualitative approach in the form of analysis of short stories in class III SD books. The subjects of this study were all short stories contained in the third grade students' book of 8 Praja Muda Karana theme. The short stories in the third grade students' book of the theme 8 Praja Muda Karana consist of eight short stories. The short story is entitled I am an independent child, independently doing homework, independent during worship, practicing independently, I like adventure, cycling with friends, being a receptionist, holding charity activities. After being read and analyzed, it was found that there are several character values that can be transmitted to children including: independence, friendly / communicative, religious, tolerance, discipline, hard work, curiosity, love for the country and several other noble characters.
Device–Cloud Collaboration

We are currently witnessing a rapid increase in the popularity of mobile and wearable devices.
Wireless Sensor Networks Management Using Differential Evolution and Minimum Dominating Sets

Challenges of managing and controlling wireless sensor networks (WSNs) includes efficient routing, increasing sensor node lifetime by conserving energy consumption, and base stations fault-tolerance. These challenges can be solved by help of Minimum Dominating Set (MDS) algorithms. By applying MDS to WSNs, only base stations (dominating sensors) take the burden of the communication instead of all sensors, and consequently saving the communication bandwidth, energy consumption, and increase the network lifetime. In addition to that, having multiple MDS improve base stations fault tolerance by scheduling different MDS periodically. In this research, a new Differential Evolutionary (DE) is composed to solve the general MDS problem. First, this problem is formulated as a binary optimization problem. Then, new evolutionary operations are introduced to fit the considered problem and wireless sensor network applications. Beside these evolutionary operations, other search enhancement operations are invoked to improve the search diversification and intensification process in addition to enabling the search process to find several distinct solutions. Those solutions can help in scheduling the network control nodes in order to increase its lifetime. Several experimental simulations over benchmark networks are carried out to test the efficiency of the proposed method. The results demonstrate the quality of the proposed method for obtaining minimum dominating sets for the considered test networks.
Code is Law –Deterritorialisation and Reterritorialisation of Law, Law is Code – Cyberspace, Personalisation Algorithms and Human Cognition

This narrative is a theoretical exploration of the encounter between law and cyberspace. It is a conversation about what lines of de/reterritorialisation can do with that encounter. It is becoming in contact with Gilles Deleuze and Felix Guattari’s concepts: rhizome, assemblage, becoming, territorialisation, deterritorialisation, and reterritorialisation and with Lawrence Lessig’s theory code is law. Code may be law, but perhaps law can also become code. This paper thinks about democratic states in which there is a desire to let law proceed from the people, and about how the law and code seem to affect human behaviour in similar ways. As an example, I explore how code in personalising algorithms affects human cognition; how code that enables certain cognitive processes can disable certain others; and how code regulates human cognition. The paper thinks about how law is territorialised in the encounter with cyberspace, deterritorialised when code challenges legal sovereignty, and how it can reterritorialise through ‘code is law’ or through ‘law is code’. I am suggesting that, in this process, a need is emerging: a need to let code proceed from the people.