Reviewerly/exHarmony huggingface dataset:
Power Triplet Extraction Based on Knowledge Enhanced Representation

The most critical step in the process of knowledge graph construction is entity relation extraction. However, most of the existing entity relationship extraction methods somewhat ignore common sense, resulting in poor results. In order to relieve the problem, in this paper, we propose a power triplet extraction method based on knowledge enhancement representation by combining text representation of domain and general knowledge enhancement. Firstly, the pre-training model is trained by using data sets in the power domain so that the pre-training model contains language features in the power domain. Then, through the graph embedding, the external general knowledge is embedded into the vector, and the output of the pre-trained model is stitched together so that the spliced vector contains both domain and general knowledge. Our method can relieve the problem of lack of common sense in the extraction of entity relationships. We conduct experiments on the power domain dataset and the results show that our model is better than the state-of-the-art baselines, and it has good performance in complex power application scenarios.
Semantic Reasoning Technology on Temporal Knowledge Graph

Semantic reasoning techniques based on knowledge graphs have been widely studied since they were proposed. Previous studies are mostly based on closed-world assumptions, which cannot reason about unknown facts. To this end, we propose the Two-Stage Temporal Reasoning Model (TSTR) for reasoning about future facts. In the first stage, probability of future facts occurring is reasoned using repeated information in history. In the second stage, the semantics of the neighborhood nodes are aggregated using the structural encoder and the temporal information is captured using the temporal encoder. The predicted probabilities are obtained by the decoder. Finally, the candidate entity probabilities of the two-stage reasoning are weighted to achieve the prediction of the two-stage fusion. We tested the performance of the TSTR on public datasets and the results demonstrated the effectiveness of the TSTR.
Using Image Processing and Optical Character Recognition to Recognise ID cards in the Online Process of Onboarding

A broad range of computer vision studies have been conducted on the recognition of identity documents using mobile devices. A portfolio of techniques and algorithms for solving problems like face recognition, document detection and correction, text field recognition, and so on has been developed, and the lack of data sets has become a critical challenge. The electronic ID card has been commonly used to verify or recognize an individual since its discovery in 2011. Take note of several issues, such as high requirements for detecting the id-card field and challenges recognizing the data in the id-card. In this research, we realized an approach for identifying electronic ID cards using image detection and Optical Character Recognition (OCR). This paper provides a short outline of how banking institutions and other fintech companies utilize highly developed OCR tools to automate their processes, enhancing not just service quality, as well as the precision, speed, and protection of their transaction data. As an outcome of our image processing and OCR techniques, we can achieve 99 percent ID detection. This research was based on the interface of a mobile application, such as those owned by one of Kazakhstan's banks.
Feature Guided Perturbation for Facial Attribute Classification

Pre-trained deep models are widely used in computer vision tasks ranging from face recognition to object classification and attribute prediction. The performance of these models depends heavily on the pre-training dataset, and a shift in the input data adversely affects the model performance. Existing techniques address the problem by updating the pre-trained model on the new dataset, most popularly via model fine-tuning. However, this requires updating millions of parameters in the model and is computationally expensive. Therefore, in this research, an algorithm is proposed to address the problem of data shift via perturbation learning without updating the pre-trained model parameters. The proposed algorithm shifts the data in input space to obtain <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Feature Guided Perturbed (FGP)</b> data such that when FGP data is given as input to the pre-trained model, it results in optimized feature space. Perturbation is learned by minimizing the intra-class distance and maximizing the inter-class separation among the classes in the feature space. The proposed algorithm is evaluated on three publicly available datasets, namely, LFW, CelebA, and MUCT. Different experiments and comparisons with existing algorithms, including fine-tuning the model, show the efficacy of the proposed approach. Most importantly, FGP requires fewer parameters to be learned compared to the traditional approaches.
Dual Siamese Channel Attention Networks for Visual Object Tracking

Siamese network based trackers have achieved remarkable performance on visual object tracking. The target position is determined by the similarity map produced via cross-correlation over features generated from template branch and search branch. The interaction between the template and search branches is essential for achieving high-performance object tracking task, which is neglected in previous works as features of the two branches are computed separately. In this paper, we propose Dual Siamese Channel Attentions Networks, referred as SiamDCA, which exploits the channel attentions to further improve tracking robustness. Firstly, a convolutional version of Squeeze and Excitation Networks (CSENet) is embedded in backbone to explicitly formulate interdependencies between channels to recalibrate channel-wise feature responses adaptively. Meanwhile, we propose a novel Global Channel Enhancement (GCE) module, which is capable of capturing attention weights of each channel in template branch, so as to normalize the channel characteristics in search branch. We experiment on benchmark OTB2015, VOT2016 and UAV123 where our algorithm demonstrates competitive performance versus other state-of-the-art trackers.
Information Architecture Design

This chapter presents two approaches for designing the information architecture. Thus, after introducing the topic and its similarities and differences, the top-down and the bottom-up information architecture design primitives are described. In the following section, using the design primitives, the top-down and the bottom-up approaches are presented, along with an example. Afterward, we discuss the benefits and challenges of each approach and suggest how to mix them. Finally, some information architecture design exercises are proposed.
Estimation of Generated Electricity for the Solar Power Plant by Using Polynomial Regression

The estimation of electrical energy that can be supplied from the renewable energy sources becomes a weighty matter regarding to increasing the share of these sources in the electrical energy sector all over the world. The installed power of power plants in Turkey has reached 100334 MW. Depending on the installed power, progress has been made in the electricity transmission and distribution sector over the years. Thus, there exist 21 distribution regions in electricity distribution in Turkey. Some distribution regions have reached 25 MWs in terms of solar installed capacity. While estimating the electrical energy demand for any distribution region, the total electrical energy that can be supplied by the solar power plants (SPP) in that region should be taken into account. In accordance with the legislation in Turkey, the authorized electricity retail sales company in each distribution region has to enter the daily estimation of the electrical energy, to be obtained from SPPs, located in its own region, on the web site of EPIAS (Energy Markets Operation Joint Stock Company). In this study, the estimation of electricity generation of the 990 kW solar power plant belonging to Osmaniye Special Provincial Administration and located in the town of Cevdetiye for the days 24.08.2021 and 25.08.2021 was carried out with the polynomial regression algorithm. Approximately 85% performance metric was got from the polynomial regression algorithm
Distantly Supervised Named Entity Recognition with Category-Oriented Confidence Calibration

Named entity recognition plays an important role in extracting valuable information from digital libraries, which can help stakeholders to take full advantage of large quantities of documents to boost the development of scholarly knowledge discovery. Nevertheless, there aren’t many annotated NER datasets aiming at scientific literature except medical domain, restricting to utilize abundant of advanced deep learning models. As an alternative solution, distant supervision provides a feasible way to eliminate the need of human annotations by automatically generating annotated datasets based on external resources such as knowledge base, while introducing noise inevitably. In this work, we study the noisy-labeled named entity recognition under distant supervision setting. Considering that most NER systems based on confidence estimation deal with noisy labels ignoring the fact that model has different levels of confidence towards different categories, we propose a Category-oriented confidence calibration (Coca) strategy with an automatically confidence threshold calculation module. We integrate our method into a teacher-student self-training framework to improve the model performance. Our proposed approach achieves promising performance among advanced baseline models and can be easily integrated into other confidence based model frameworks (Our code is publicly available at: https://github.com/possible1402/BOND_Coca ).
An Efficient Privacy-Preserving Scheme for Traffic Monitoring Services in Vehicular Networks

Traffic monitoring services show great potential for improving the traffic efficiency in vehicular networks. Drivers can obtain the latest information on their upcoming routes by sending location-based queries to the traffic monitoring server. However, it is inefficient to query all events on the route due to the timeliness of traffic events. Besides, sending location-based queries will expose drivers’ privacy. Existing research does not consider both issues under the traffic monitoring scenario. In this paper, we propose an efficient privacy-preserving scheme that ensures privacy is protected in two dimensions. Both location privacy and identity privacy are preserved, such that attackers cannot observe the real location as well as the identity via continuous queries. Specifically, the scheme first segments the whole route into multiple ones. Then drivers send endpoints in each segmented route to satisfy the timeliness requirement. We propose a location obfuscation mechanism based on geo-indistinguishability and utilize it on every segmented route. We address an issue in geo-indistinguishability where the obfuscated location is unreasonable. Additionally, continuous queries may expose the driver’s identity. We thus define a type of attack called identity linking attack and propose two possible solutions. We finally conduct experiments on the real dataset. Experimental results demonstrate the efficiency of our proposed scheme.
Conditional Visual Servoing for Multi-Step Tasks

Visual Servoing has been effectively used to move a robot into specific target locations or to track a recorded demonstration. It does not require manual programming, but it is typically limited to settings where one demonstration maps to one environment state. We propose a modular approach to extend visual servoing to scenarios with multiple demonstration sequences. We call this conditional servoing, as we choose the next demonstration conditioned on the observation of the robot. This method presents an appealing strategy to tackle multi-step problems, as individual demonstrations can be combined flexibly into a control policy. We propose different selection functions and compare them on a shape-sorting task in simulation. With the reprojection error yielding the best overall results, we implement this selection function on a real robot and show the efficacy of the proposed conditional servoing. For videos of our experiments, please check out our project page: https://lmb.informatik.uni-freiburg.de/projects/conditional_servoing/
Open-set Pets Facial Recognition Using Deep Learning and Statistical Learning

The open set facial recognition problem has always been a major challenge in applying deep learning from theory to practice. In this paper, we propose to treat a pet individual as a class, build a multi-classification model and train a feature extraction network in the training phase; use the trained feature extraction network to extract features in the testing phase, use statistical learning methods to rank the feature similarity, and finally achieve the purpose of querying pet identity information by pet face. Finally, to verify the effectiveness of the proposed method in this study, we built an image dataset containing dogs and cats as the main subjects. The results show that the method using improved feature extraction of ResNet50 with KNN algorithm feature matching achieves 94.4% accuracy for open-set animal facial recognition under certain conditions. The method has good performance in terms of recognition accuracy and speed, and provides important technical support for individual recognition of open set animal faces.
UCC: Universal and Committee-based Cross-chain Framework

One most potential solution to enhance the interoperability of various kinds of blockchains is to utilize the cross-chain mechanism. This mechanism, however, still faces some challenges. One is to find a universal cross-chain solution, independent of the underlying heterogeneous blockchains. The other is the lack of formal security definitions. To address these challenges, we propose UCC, a universal cross-chain framework using the cross-chain committees. We provide the formal security definition of the cross-chain framework and prove that UCC is secure when each well-defined module meets its security requirement. To demonstrate the feasibility of UCC, we implement it with efficient components and make the experimental evaluation. The result shows that UCC achieves a secure cross-chain with a low cost in comparison to the underlying chains.
Credit Card Fraud Using Adversarial Attacks

Banks lose billions to fraudulent activities every year, affecting their revenue and customers. The most common type of financial fraud is Credit Card Fraud. The key challenge in designing a model for credit card fraud detection is its maintenance. It is pivotal to note that fraudsters are constantly improving their tactics to bypass fraud detection checks. Several fraud detection methods for identifying fraudulent credit card transactions have been developed. However, in order to further improve on the existing strategies, this paper investigates the domain of adversarial attacks for credit card fraud. The goal of this work is to show that adversarial attacks can be implemented on tabular data and investigate if machine learning approaches can get affected by such attacks. We evaluate the performance of adversarial samples generated by the LowProfool algorithm in deceiving the classifier.
Towards Autonomous Cyber Defense: Predictions from a cognitive model

We propose that cognitive models will help advance autonomous cyber defense. Congitive models, in contrast to many other sophisticated approaches, such as machine learning models, have the ability to represent human actions, account for human cognitive constraints, and make more accurate predictions of the decisions a human would make. The success of cyber defense largely depends on the ability to predict the attacker’s actions, and expert cyber defenders acquire such an ability through experience. We propose a cognitive model of a cyber defender and demonstrate the model’s predictions in a simple but realistic scenario against two types of attack strategies and under various conditions of noise and feedback frequency regarding the attacker’s actions. The results of the simulations illustrate the expected impact on defense losses when attackers are more knowledgeable,fast, and directed in their attacks compared to when an attacker meanders around; results show how losses increase in the presence of normal activity and how a defender can benefit from receiving feedback less often to reduce losses. We discuss the implications of these results for the future of autonomous cyber defense.
Image Quality Assessment for Wood Images

This work proposed the implementation of subjective and objective assessment on wood images to analyse the quality of wood images for wood species recognition purposes. Several distorted images are generated from the reference images by applying Gaussian White Noise (GWN) and Motion Blur (MB) at various levels of distortions for comparison purposes. Ten subjects from Negeri Sembilan Forestry Department were selected to assess the distorted images for the subjective evaluation. In the objective evaluation, five Full Reference-IQAs (FR-IQAs) were used to evaluate the distorted images. The subjective scores were used as the benchmark to determine the most suitable objective FR-IQA to assess wood images. The relationship between the subjective scores and objective FR-IQAs are examined using performance metrics, namely PLCC and RMSE. It was found that FSIM is the most suitable FR-IQA to assess wood images distorted with GWN and MB.
Research on Vehicle Distance Estimation Model based on Deep Learning

With the continuous development of artificial intelligence technology, intelligent vehicle technology in driverless scenarios has become an important direction for a new round of technological change. As an important part of the driverless car, the vehicle distance estimation module is of great significance for improving the intelligent car in the driverless environment, and the reliability and safety of the system are of great significance. However, in the field of unmanned driving, traditional vehicle distance estimation algorithms have the problems of poor real-time performance and insufficient accuracy, and cannot achieve end-to-end distance estimation tasks. Aiming at the above problems, this paper proposes a deep learning-based vehicle distance estimation model. The distance estimation module is added to the traditional mainstream deep learning network so that the distance estimation task and the original classification and detection task can achieve feature fusion, and multi-task joint learning is performed to realize the end-to-end distance estimation task. The simulation results show that the model proposed in this paper can effectively make up for the lack of real-time performance of traditional distance estimation methods based on machine learning, and achieve higher distance estimation accuracy and better real-time performance.
Influence of external factors on the information needs of general education teachers

Introduction. The problem of information deficits in a situation of total digitalisation and intensification of communication processes has taken on a new meaning. The field of education is becoming not only the place where new models of working with information emerge and a predictor of success in the information world, but also the field for actualization of information inequality. The information gap between certain socio-demographic groups of educators can not only perpetuate information inequality within one professional group, but also affect the entire education system and its outcomes, exacerbating other forms of inequality in society. Purpose: Study the influence of factors external to the education system on the information needs and deficits of general education teachers. Materials and methods. The empirical basis of the study was the results of a remote survey of 29,393 general education teachers from 80 constituent entities of the Russian Federation in April 2022. The questionnaire used both closed and open questions, which made it possible to use for analysis not only the tools of mathematical statistics, including descriptive analysis, but also methods of semantic analysis, including typing. Results. The survey revealed the influence of external factors in relation to the education system on the level and structure of information deficits of general education teachers. According to the respondents, external factors influence both the activities of the education system as a whole (88.1%) and the professional activities of each teacher (77.0%), determining the demand for certain information. The survey recorded the presence of both conscious, presented and latent, unconscious information request among teachers. Thus, 49.9% of general education teachers reported no information deficit related to the use of ICT in April 2022, and only 22.7% responded in assessing the demand for information in specific areas related to ICT, saying that they had no questions about the use of ICT infrastructure and software in 2022. Recorded information deficits of general education teachers, influenced by external factors, concerned various aspects of professional activity, such as changes in the content of the educational and training process, ICT support for educational activity and opportunities for psychological help and emotional support for educational workers and/or students. Conclusion. According to the survey results, the key factors exacerbating the information gap among Russian general education teachers and leading to information inequality, are territorial, settlement, professional specialization and age factors.
Digital Manufacturing for the Enhancement of Heritage. The Case of Spazio Geco

This article presents and disserts the multiple activities carried out by Spazio Geco to innovate museum exhibits and made them more interactive and effective. A number of projects aimed at improving in various Italian museums the exploration and fruition by the visitor of the exhibited cultural heritage have been set up in Spazio Geco. These projects are based on advanced interactive methods and augmented reality systems specifically developed for virtual exploration and fruition of cultural heritage and are thoroughly described and illustrated with many pictures in the present paper. In perspective, the group of young and creative Geco researchers and collaborators intend to take advantage of the skills acquired in the last five years to kick off new projects aiming at further development and implementation of the interactive multimedia communication forms within Italian as well as foreign museums.