Reviewerly/exHarmony huggingface dataset:
Uncalibrated Image-Based Visual Servoing Control based on Image Occlusion using Dual Adaptive Strong Tracking Kalman Filter

Focusing on the challenge of visual servoing control subject to feature lost or occlusion, the scenarios of image features being lost or occluded with image features are analyzed. An adaptive strong tracking Kalman filter (ASTKF) is adopted to adjust the image information to improve the accuracy of state vector estimation of lost or occlusion. Another ASTKF is presented to estimate the image Jacobian matrix dynamically in an unstructured environment. Considering the kinematic behavior of visual servoing, combining with the uncertainties of the camera and the manipulator model, proportional-differential and sliding mode control (PD-SMC) method is employed to further enhance the accuracy and robustness of visual tracking. The simulation study is given to show the effectiveness of the proposed scheme.
A Dag Based Decentralized Oracle Model: Implementation and Evaluation

Blockchain comes up to solve centralization problems and to be an alternative solution to traditional finance. This technology seems to be very promising and has proven a big efficiency in ensuring trust among the network. It innovates and proposes new forms of governance and capital allocation, it promises decentralized financial services of credit, exchange, insurance, etc. It is the interweaving of computer programs, called smart contracts in a decentralized network, which replace traditional intermediaries and automate business processes. To make smart contracts in the blockchain context consistent and worthy, they must rely on solid data from the outside world, such as market prices observed on all exchange places for example. Blockchains are autonomous systems that are not natively designed to integrate data from the “outside”, thus, oracles are external systems that solve the problem of feeding an isolated network with data from the outside. On the other hand, these systems are centralized, consequently, they grant a high risk of data corruption and unreliability issues. In this paper, we present our decentralized oracle approach, based on a Dag (directed acyclic graph) model, its implementation as well as an evaluation of its accuracy in the context of live dynamic financial market data.
How fixation durations are related to the functional visual field and target guidance

Concepts like the span of the effective stimulus or the functional visual field (FVF) have been increasingly investigated in recent visual search and eye tracking studies. In particular, the FVF has been used to explain how many fixations are necessary to find a target and why observers sometimes miss a target even though their gaze fell close to the target position. FVF studies usually focus on the number of fixations or on saccade amplitude. In contrast, fixation durations received very little attention in the literature and are often assumed not to vary in a meaningful manner as a function of the current visual input. In the present study, we assume that the presence of the target within the FVF can speed saccade planning and that this registers in shorter fixation durations before a saccade is made to the target. We conducted a free viewing search experiment where participants had to find a specific target shape. Results showed shorter fixation durations for the last distractor fixation before a saccade was directed to the target. Fixation durations are not shorter when target discriminability is low, suggesting that the subsequent target saccade is not guided by target information. Moreover, shorter fixation durations only occur when the fixation is within a specific distance to the target. We discuss whether fixation durations are only shortened by facilitated saccade planning when the target falls within the functional visual field. If so, fixation durations could be used to estimate the size of the functional visual field or the span of the effective stimulus.
Recommendation Systems : A Comparative Analysis of Classical and Deep Learning Approaches

Recommendation systems bring in the lion’s share of e-business. Personalised recommendations make it easy for the user to take decisions quickly since the top recommended items will be produced after analysing user preferences, past user actions and current market trends. A very good recommendation engine is necessary for the e-business to ensure huge revenue. In this paper we quantitatively analyse the research works done on recommendation systems in the last decade(2010-2020) and qualitatively analyze the current state of the art(2018-2021). We find that towards the end of the last decade, deep learning based recommendations attracted more attention due to their ability to make more accurate recommendations.
Spam Detection on Indonesian Beauty Product Review

A product review is one of the most important sources of information that can help consumers to find the most suitable products for their needs. However, there is a chance a reviewer has other intentions than providing an honest review, including advertising the brand or other brands. A review that does not contain any information related to the product’s aspects/features could be considered spam. This paper presents our work on spam review detection, specifically in the domain of beauty products. We used SVM and Logistic Regression classifier and the following features: the review sentiments, product-related features, and review-centric features extracted from the reviews. We classified the beauty product review texts as spam and non-spam reviews. The experimental result showed that the best accuracy percentage was 81%, obtained when we used the sentiments and review-centric features with the SVM algorithm.
THE IDEOLOGICAL AND POLITICAL TEACHING PRACTICE OF THE COURSE "MECHANICAL MANUFACTURING TECHNOLOGY FUNDAMENTALS" UNDER THE BACKGROUND OF "NEW ENGINEERING"

To optimize the teaching content, it relies on the "New Engineering" project to revise the curriculum syllabus, and integrate the ideological and political elements such as craftsmanship, innovation, and May Fourth spirit into the course teaching of " Basic Mechanical Manufacturing Technology" through actual cases ; in order to finish the fundamental task of fostering virtue through education, training students establish correct outlook on life and values, and deliver compound talents that meet social needs.
DocVisor: A Multi-purpose Web-Based Interactive Visualizer for Document Image Analytics

The performance for many document-based problems (OCR, Document Layout Segmentation, etc.) is typically studied in terms of a single aggregate performance measure (Intersection-Over-Union, Character Error Rate, etc.). While useful, the aggregation is a trade-off between instance-level analysis of predictions which may shed better light on a particular approach’s biases and performance characteristics. To enable a systematic understanding of instance-level predictions, we introduce DocVisor - a web-based multi-purpose visualization tool for analyzing the data and predictions related to various document image understanding problems. DocVisor provides support for visualizing data sorted using custom-specified performance metrics and display styles. It also supports the visualization of intermediate outputs (e.g., attention maps, coarse predictions) of the processing pipelines. This paper describes the appealing features of DocVisor and showcases its multi-purpose nature and general utility. We illustrate DocVisor’s functionality for four popular document understanding tasks – document region layout segmentation, tabular data detection, weakly-supervised document region segmentation and optical character recognition. DocVisor is available as a documented public repository for use by the community.
Sentiment Preservation in Review Translation using Curriculum-based Re-inforcement Framework

Machine Translation (MT) systems often fail to preserve different stylistic and pragmatic properties of the source text (e.g. sentiment and emotion and gender traits and etc.) to the target and especially in a low-resource scenario. Such loss can affect the performance of any downstream Natural Language Processing (NLP) task and such as sentiment analysis and that heavily relies on the output of the MT systems. The susceptibility to sentiment polarity loss becomes even more severe when an MT system is employed for translating a source content that lacks a legitimate language structure (e.g. review text). Therefore and we must find ways to minimize the undesirable effects of sentiment loss in translation without compromising with the adequacy. In our current work and we present a deep re-inforcement learning (RL) framework in conjunction with the curriculum learning (as per difficulties of the reward) to fine-tune the parameters of a pre-trained neural MT system so that the generated translation successfully encodes the underlying sentiment of the source without compromising the adequacy unlike previous methods. We evaluate our proposed method on the English–Hindi (product domain) and French–English (restaurant domain) review datasets and and found that our method brings a significant improvement over several baselines in the machine translation and and sentiment classification tasks.
The Biased Coin Flip Process for Nonparametric Topic Modeling

The Dirichlet and hierarchical Dirichlet processes are two important techniques for nonparametric Bayesian learning. These learning techniques allow unsupervised learning without specifying traditionally used input parameters. In topic modeling, this can be applied to discovering topics without specifying the number beforehand. Existing methods, such as those applied to topic modeling, usually take on a complex sampling calculation for inference. These techniques for inference of the Dirichlet and hierarchal Dirichlet processes are often based on Markov processes that can deviate from parametric topic modeling. This deviation may not be the best approach in the context of nonparametric topic modeling. Additionally, since they often rely on approximations they can negatively affect the predictive power of such models. In this paper we introduce a new interpretation of nonparametric Bayesian learning called the biased coin flip process—contrived for use in the context of Bayesian topic modeling. We prove mathematically the equivalence of the biased coin flip process to the Dirichlet process with an additional parameter representing the number of trials. A major benefit of the biased coin flip process is the similarity of the inference calculation to that of previous established parametric topic models—which we hope will lead to a more widespread adoption of hierarchical Dirichlet process based topic modeling. Additionally, as we show empirically the biased coin flip process leads to a nonparametric topic model with improved predictive performance.
Digital Filter Algorithm based on Complex Weight and Pixel Matching in AWGN Environment

Influenced by the Fourth Industrial Revolution, modern society uses various communication devices. Accordingly, much attention has been paid to noise reduction in the data transfer process. This paper proposes a filtering algorithm to remove the additive white Gaussian noise (AWGN) that occurs in the digital image transmission process. The proposed algorithm removes the noise based on pixel matching and composite weight. Pixel matching compares the input pixel value and surrounding pixel pattern to find a pixel with a similar pattern, which is then used in the estimate calculation. The composite weight is intensified according to the difference in the pixel's spatial distance and pixel value to compensate for the loss that occurred in the noise removal process. To evaluate the proposed algorithm, simulations were conducted with existing AWGN removal algorithms and the proposed algorithm was analyzed by comparing peak signal-to-noise ratio and expanded image.
The ‘Two Suns’ of Eu Digital Copyright Law: Reconciling Rightholders’ and Users’ Interests via Interlegality

Copyright law is an emblematic example of the restless relationship between law and technology. The discipline fundamentally aims at striking a fair balance between the interests of copyright owners and users and, as the ongoing process of EU copyright reform demonstrates, digital technologies play a key role in pursuing this objective. The EU transition towards a digital-based copyright paradigm shows how achieving a balanced and context-sensitive legal framework requires taking into account elements from coexisting legal systems as well as from the technological normative ecosystem. Providing concrete examples of the intertwined nature of copyright law and digital technologies when it comes to protecting clashing interests, the article illustrates the ‘interlegal’ pattern emerging from the CJEU, which unveils a composite understanding of law and offers meaningful insights into the future of the EU digital copyright legal framework.
Algorithm for Key Classification Feature Selection of Big Data Based on Henie Theorem

With the extensive application of the database system, the available data of enterprises or individuals are expanding, and the existing technology is difficult to meet the data analysis requirements of the big data age. Therefore, the selection of key classification features of big data needs to be carried out. However, when the key classification features of big data are selected by the current algorithm, the distance between the samples can not be given accurately, and there is a large error in the classification. To solve this problem, a key classification feature selection algorithm based on Henie theorem is proposed. In this algorithm, the second programming algorithm is firstly used to make the weighted distance between the intra-class and the inter-class as the quadratic term and linear term parameter in the target function, and balance the relationship between the data features and the different categories. The optimized vector is used as the weight vector to measure the contribution of the feature to the classification. According to the feature importance degree, the redundancy feature is gradually deleted, and the problem of selecting the key classification features of big data into the resolution principle is fused into the Henie theorem. The function limit and sequence limit of the key classification features of big data are obtained. Based on this, the key classification features of big data are selected. Experimental simulation shows that the proposed algorithm has higher classification accuracy and can effectively meet the needs of data analysis in the era of big data.
Trust- and Rating- based Recommendations for On-line Social Networks

This paper analyzes the influence of incorporating trust rates between users, as well as objects’ ratings into the recommendation algorithms for bipartite on-line social networks. Three types of approaches are introduced: a trust-based approach that uses trust rates between users in the system to calculate the recommendation lists, a rating-based approach that uses exact ratings given to objects by users, and a trust-rating-aware approach that combines both of the mentioned above methods. The proposed approaches are introduced to probabilistic spreading and heat spreading recommendation algorithms. The first one strongly favors popular objects and focuses on accuracy metrics, but its diversity metrics are low. The latter one focuses on unpopular objects and diversity metrics but ignores accuracy metrics. Adding trust and object ratings to the recommendation algorithms changed a variety of their characteristics. Accuracy of results increased, and more personalized recommendations are obtained.
De-noising The Image Using Non Local Means Filtering

Because of the outstanding headway of data innovation, PC, capacity frameworks and systems administration innovation, clinical gadgets and clinical conclusion has gained huge prominence over the most recent twenty years. For the most part in clinical field including biomedical science, the impact of such advances is getting evident, permitting the discovery and determination in a significantly more striking manner. The critical obstacle in the indicative imaging study is to choose a picture with no significant subtleties being lost. All things considered, over the span of recovery or again resulting preparing stages, the information caught can be misshaped by commotions or curios. Clamor is characterized as the underlying pixel esteem being changed aimlessly. Commotion brings down the lucidity of the picture which is especially significant at whatever point the constructions are checked is more modest and indeed, even has relatively helpless force. De-noising of picture information is in this manner significant, and in clinical diagnostics it has consistently been an important pre-preparing level. An investigation of a few critical investigates in the field of picture de-noising is examined in this article. Since pictures were very fundamental in any space, picture de-noising is for sure a significant preprocess earlier towards more picture investigation, such as division, extraction of highlights, surface investigation, and so forth This examination proposed to play out the far reaching investigation of different de-noising systems for clinical imaging that includes MRI, CT and Retinal fundus pictures. An examination concentrate with many existing strategies approaches zeroed in on similarity tests, uncovers that the proposed approach is better in picture consistency than them.
Contingency Planning: Prioritizing Your Resources

Contingency planning has been a concept in technology for many decades and is a more prevalent sector of information technology. Over time, procedures have changed, but the object remains: “Always have a plan.” With the drastic and rapid changes in technology, companies have no choice but to have plans in case of major incidents such as natural disasters, blackouts, and certain cyberattacks. In exploring contingency planning as a whole, this paper discusses different topics such as common successful methodologies and new discoveries.
An Effective Implementation of Vehicle Document Verification Using Genetic Algorithm

Throughout the world, according to the act of motor vehicle , each citizen should carry the original records like (Insurance, License, and Registration certificates book). While driving citizens need to carry all the original documents , many of them fail to carry their documents and facing difficulties . To defeat this issue , the proposed system created an individual barcode and their details are stored in the RTO database which consists of personal details , license , registration books (RC), and insurance . By using MATLAB, the barcode scanner will be done and it will decode the barcode to find the documents in the RTO database , suppose any of the documents are missing in the RTO database or insurance is not up-to-date than the person who maintains the RTO database will generate a fine amount for the consenting person. After paying that he/she needs to update the insurance in the RTO database . Suppose the barcode is missing in the vehicle they need to pay the fine and if they did not pay the fine amount then their vehicle should be taken under the control of the RTO . This database is fully monitored by the RTO . Thi sproject helps to reduce the work of the police officer and help to follow the rules acco rding to the Motor Vehicle Act.
Probability Preserving Discriminative Nonnegative Matrix Factorization

Non-negative matrix factorization (NMF) has received increasing attention since it is a practical decomposition approach in computer vision and pattern recognition. NMF allows only additive combinations which leads to parts-based representation. Further, NMF and its variants often ignore the underlying local structure information. In this paper, we propose a novel objective which provides enough probabilistic semantics of intrinsic local topology via the probability preserving regularizer, together with the joint multiplicative update routine. Additionally, through the class indictor matrix coupled with the loss function, the generative and discriminative components with the property of local probability preservation can be simultaneously acquired which is rather optimal for the classification. The experimental results of both clustering and classification tasks demonstrate that performance of the proposed approach is clearly competitive with several other state-of-the-art algorithms.
Target Detection and Tracking of Ground Mobile Robot Based on Improved Single Shot Multibox Detector Network

To solve the problems of slow labeling speed of the traditional labellmg data set establishment method, and slow running speed of target classification and detection algorithm based on Single Shot Multibox Detector(SSD) deep learning network, this paper proposes a fast data set labeling algorithm and a fast SSD network for target real-time detection and tracking research. First, a data set is established quickly by using TLD target detection and tracking algorithms, cropping and mirroring methods are used to strengthen the data set. Then, SSD backbone network is improved based on depth-wise separable convolution to establish a fast SSD network. Finally, the ground mobile robot in RoboMasters(RM) competition is used as the detection and tracking target indoors and outdoors, as well as with other different scenarios with shield to test the real-time performance, accuracy and effectiveness of the algorithm. The results show that compared with traditional SSD network research, in terms of the analysis and processing system deployed on low-performance hardware, the improved fast SSD network can better meet the real-time requirements of target detection and tracking.
AttrHIN: Network Representation Learning Method for Heterogeneous Information Network

Network representation learning can map complex network to low dimensional vector space, capture the topological properties of network, and reduce the time complexity and space complexity of the algorithm. However, most of the existing network representation learning (NRL) methods are for homogeneous networks, while the real-world networks are usually heterogeneous, therefore, it is more practical to provide an intelligent insight into the evolution of heterogeneous networks. In this paper, we propose a novel heterogeneous network embedding method, called AttrHIN, which adopts weighted meta-path-based random walks strategy, and can make full use of the attribute information to capture the latent features. AttrHIN is suitable for the different types of nodes in heterogeneous networks. Extensive experimental results show that compared with the state-of-art algorithms, AttrHIN achieves better results in Macro-F1 and Micro-F1 for multi-class node classification and Link Prediction on several real-world network datasets.