Reviewerly/exHarmony huggingface dataset:
Fact-condition statements and super relation extraction for geothermic knowledge graphs construction

Researchers utilize information from the geoscience literature to deduce the regional or global geological evolution. Traditionally this process has relied on the labor of researchers. As the number of papers continues to increase, acquiring domain-specific knowledge becomes a heavy burden. Knowledge Graph (KG) is proposed as a new knowledge representation technology to change this situation. However, the super relation is not considered in the previous KG, which bridges the geological phenomenon (fact) and its precondition (condition). For instance, in the statement (“the late Archean was a crucial transition period in the history of global geodynamics”), the condition statement (“crucial transition for global geodynamics”) works as the complementary fact statement (“the late Archean was a crucial transition period”), which defines the scale of crucial transition accurately in the late Archean. In this study, fact-condition statement extraction is introduced to construct a geological knowledge graph. A rule-based multi-input multi-output model (R-MIMO) is proposed for information extraction. In the R-MIMO, fact-condition statements and their super relation are considered and extracted for the first time. To verify its performances, a GeothCF dataset with 1455 fact tuples and 789 condition tuples is constructed. In experiments, the R-MIMO model achieves the best performance by using BERT as encoder and LSTM-d as decoder, achieving F1 80.24% in tuple extraction and F1 70.03% in tag prediction task. Furthermore, the geothermic KG with super relation is automatically constructed for the first time by trained R-MIMO, which can provide structured data for further geothermic research.
A consumer-centered security framework for sharing health data in social networks

• We proposed a framework for securely sharing healthcare consumer information with healthcare team in social networks. • Our proposed protocol is resistant to security attacks. • The proposed framework enables healthcare consumers to control every data access and modifications after data sharing. Healthcare consumers should have control over their shared health data in social networks and cloud-based systems through specifying their data protection policies, and preventing unauthorized data access and modifications. This consumer-centered approach requires a completely secure framework and protocol that enables consumers to easily revoke users and have control over their data after data sharing. We aimed to develop a security framework that enables consumers to securely share data with the healthcare team, revoke users, and control their data after sharing, considering a complete authenticity between all parties involved in data sharing. Using the ElGamal and the ciphertext-policy attribute-based encryption (CP-ABE) algorithms, we developed a hardware-independent, role-based, and data type-based framework. We evaluated our framework in terms of “Secret x ”, “Alive”, “Niagree, “WeakAgree” and “Nisynch” security claims. We also evaluated the secrecy of the suggested protocol by using an automated security verification tool (ProVerif). We evaluated the scalability of our solution to measure the maximum workload that it can tolerate. We also evaluated our framework in terms of the required time and volume of data to encrypt and access the data. The security evaluation showed that our framework for the four roles of consumer, physician, cloud server, and proxy server are secure against the security claims. ProVerif simulation results indicated that the suggested protocol was safe for consumers’ private information. Our resource utilization experiment showed that on average for 1617.1015 KB of input data, 10.282 seconds were needed. In addition, on average for 1617.0742 KB of data consumed, 0.797 s were required. The scalability test showed that the proxy server could respond to 2885 concurrent requests in 60 s and the cloud server could respond to 777 concurrent requests in 60 seconds. In comparison with the state-of-the-art approaches, the evaluations demonstrate that our framework provides more features such as flexible access control after data sharing and is also more efficient in terms of the computational cost of user revocation.
Performance-tradeoff relation for locating two incoherent optical point sources

The optimal quantum measurements for estimating individual parameters might be incompatible with each other so that they cannot be jointly performed. The tradeoff between the estimation precision for different parameters can be characterized by information regret -- the difference between the Fisher information and its quantum limit. We show that the information-regret-tradeoff relation can give us not only an intuitive picture of the potential in improving the joint scheme of estimating the centroid and the separation, but also some clues to the optimal measurements for the sequential scheme. In particular, we show that, for two incoherent point sources with a very small separation, the optimal measurement for the separation must extract little information about the centroid, and vice versa.
Optimal Neural Network for Predicting Solar Energy in Sensor Units Based on a Cascaded Input/Structure Direct Optimization

The sensor units are considered one of the significant technologies that use solar energy as an assistant power source to the batteries. Despite their advantages over the other forms of renewables, solar energy has an intermittent nature which negatively affects the operation of these units. Reaching an effective operation ensuring sustainable units requires a prior prediction of the harvested solar energy. Artificial neural networks (ANNs) appeared recently as a promising prediction approach with those units. This is attributed to the high accuracy compared to the conventional stochastic and statistical ones. Till now, the optimal neural network that fits with sensor units has not been precisely determined. This paper is aimed at finding the optimal neural network that would be applied with solar-supplied sensor units. This is performed by applying a cascaded input/structure direct optimization. The optimization process handles the aspects of accuracy, computational efforts, and complexity. It mainly identifies the type and number of parameters that would be utilized as inputs in the first stage. Then, it optimizes the structure by addressing the number of hidden layers and hidden neurons. The corresponding analysis has been implemented for premeasured real data over five-year time period. The results showed that the optimal neural network can be achieved by using three input parameters which are the air temperature (AT), the relative humidity (RH), and the zenith angle ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>θ</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> </math> ). For the structure, it has been concluded that the proposed optimal ANN should have two hidden layers with ten neurons in each of them. Lastly, the proposed optimal ANN was verified against the associated prediction error which is minimized to less than 2%.
Runtime Assurance of Learning-Based Lane Changing Control for Autonomous Driving Vehicles

Learning techniques such as deep reinforcement learning have been increasingly used in the controller design for autonomous vehicles, e.g., lane changing controllers. Although the use of learning techniques can remarkably increase the level of autonomy, their presence poses a great challenge for safety assurance due to the black-box and data-driven nature of learning techniques. In this work, we study the problem of how to safeguard the learning-based lane changing controller for collision avoidance. Our solution leverages on the runtime assurance framework, in particular the Simplex architecture to bound the behavior of the learning-based controller and to provide safety guarantees. The basic idea is to encompass the learning-based controller with a safety-by-construction controller and a decision module, which monitors the output of the learning-based controller at runtime and implements a switching logic between these two controllers according to the changing environment. We present the detailed design of the decision module and formally prove its correctness for collision avoidance. We also carry out a comprehensive experimental evaluation in a set of realistic highway scenarios using the SUMO simulator. The simulation results show that our proposed solution can not only provide safety guarantee for the learning-based lane changing controller, but also maintain a considerable level of efficiency in different volumes of traffic flow.
Pedagogical conditions enabling the finalists of the Artificial Intelligence Olympiad to achieve great results

The article analyses the survey of the finalists in the first All-Russian Artificial Intelligence Olympiad for school students of the 8–11 grades of general education institutions. Artificial Intelligence is a strategically important area; it is designated as one of the end-to-end digital technologies in the national program “The Digital Economy of the Russian Federation”. Such technologies ensure accelerated development of priority sectors of the economy and the social sphere. In 2021 within the framework of achieving result 1.7 of the Federal Project “Artificial Intelligence” of the National Program “The Digital Economy of the Russian Federation’, the Ministry of Education of the Russian Federation organized and held an Artificial Intelligence Olympiad for school students of the 8–11 grades of general education institutions. The following objectives were pursued during the research: to obtain the Olympiad finalists’ “portrait” focusing on certain characteristic features; to examine the finalists’ attitude to the concept of “artificial intelligence”; to identify the pedagogical conditions enabling the finalists to succeed in the Olympiad; to provide an update on several modern trends in the development of general education. The systematization of the winners’ answers made it possible to develop an algorithm for achieving success that guaranteed the finalists the excellent results in the Artificial Intelligence Olympiad. This algorithm serves as a pedagogical condition for updating modern trends in general education. The implementation of correctly chosen pedagogical conditions can ensure the development and efficiency of the pedagogical system to achieve good results in the Artificial Intelligence Olympiad.
Blockchain and Artificial Intelligence Feasibility, Implementation and Sustainability in Supply Chain

This paper aims to study the current deficits in the supply chain and logistics industry and how cutting-edge technologies will be able to solve the problems. Ideas such as blockchains are being used for integrity &#x0026; immutability during logistic operations and to use Artificial intelligence to maximize the efficiency of the supply chain operations. Using pre-existing data and research papers we will determine the feasibility and sustainability of using these technologies in the supply chain and logistics industry. This paper will also discuss about the Blockchain and machine learning models and difficulties in implementing these technologies in the supply chain and logistic industry.
Investigation Of Comparison on Modified CNN Techniques to Classify Fake Face in Deepfake Videos

Deepfake is an advanced deep learning technology that will allow us to swap faces. With recent advancements in Neural network algorithms such as Generative Adversarial Neu- ral networks, hyper-realistic images and videos can be generated. It will be difficult to differentiate between fake and real images orvideos for naked eyes. These fake images and videos pose a real threat in various societal platforms. To reduce these issues various deep learning technologies can be used to classify deepfakes. Here we have compared three neural net models such as ResNext., Xception, and Ensemble of both. Our study has shown that the Ensemble method works better compared to others.
A Novel Scheme to Improve the Scalability of Bitcoin Combining IPFS With Block Compression

As of the end of September 2021, the size of the Bitcoin blockchain has reached 366.9GB and continues to increase at an average annual growth rate of 17.6%. The large-scale demand for storage space constrains new nodes from joining the network, which seriously hinders the development of blockchain technology. In this paper, to explore the data characteristics of the Bitcoin blockchain, we performed a comprehensive statistical experiment on the current 680,000 blocks. The analysis results indicated that in nearly 95% of blocks, the number of spent transaction output (STXO) accounts for more than 67% of the total transaction outputs. Inspired by this feature, we proposed a novel storage scheme to reduce the size of blocks by deleting the transaction data with the STXO ratio over 67% first and compressing fixed-length fields of those transactions. Then, the newly generated block files were deposited to the InterPlanetary File System (IPFS) private network to improve the scalability of the Bitcoin blockchain. The experiments and evalutions showed that the proposed scheme achieved a compression ratio of 96.9% and saved 330GB of storage space for the Bitcoin full nodes while guaranteeing the normal operation of the Bitcoin network.
TCRNet: A Trifurcated Cascaded Refinement Network for Salient Object Detection

Due to the rapid development of deep learning, salient object detection has achieved promising results in recent years. However, how to construct a powerful saliency detection network to generate saliency maps with completely highlighted salient objects and effectively suppressed background noise still remains an open and challenging problem. In this paper, we propose a novel trifurcated cascaded refinement network (TCRNet) to explore multi-level feature fusion and global information representation. Specifically, all the side-output features from the backbone network are divided into three branches (i.e., global information, deep information and shallow information) according to feature similarities, and then the global contexts, semantic knowledge and fined details respectively generated by the three branches are integrated in a cascaded refinement way. This refinement strategy aims to guide the feature learning of important regions and suppress background noises. Moreover, we propose a cross-level feature fusion (CFF) module to fuse multi-level similar features within a same branch by exploring their interrelationship, thus capturing the important semantic information and structure details. To this end, the complementarity of similar features is utilized to refine feature at each level by sharing an encoded spatial weight map. Besides, we design a context-aware feature learning (CFL) module to capture global contexts in a single layer by taking account of both single-feature representation and multi-feature interaction, thus providing important location information of salient objects. Extensive experiments on five benchmarks are conducted to demonstrate that the proposed network TCRNet performs favorably against 20 state-of-the-art salient object detection methods on five benchmark datasets, indicating the effectiveness and superiority of our method.
Deep Learning-based Detection Methods for Covert Communications in E- Health Transmissions

Despite the numerous advantages offered by e-health applications, healthcare data of patients are at huge risk, being targeted by several different types of digital menaces and attacks. Covert communications (or channels) are dramatic threats in e-health transmissions. A covert communication in fact establishes a secret path hidden within legitimate network traffic, and can be used for leaking patients' medical information, identity, personal data, and medical history. This paper focuses on covert timing channels, where the covert data are transmitted by modulating the inter-packets delays of overt transmitted data, proposing a deep learning-based method for the effective detection of such a dangerous threat. More in details, we first collect all the inter-arrival packet delays affected by a timing covert communication, and convert them into colored images (namely, into spectrograms). Then, we exploit image-based deep learning framework to implement an effective detection of such hidden communications. The results, obtained from simulated covert data traffic also in comparison with existing deep-learning methods, demonstrate the efficiency of the proposed method to detect and prevent this form of malicious e-health information transfer.
A Faster Real-time Face Detector Support Smart Digital Advertising on Low-cost Computing Device

Smart digital advertising requires face detection as the initial stage to recognize the person’s attributes by localizing human facial areas. This technology tends to operate with CPU-based systems. The Deep Convolutional Neural Network approach has demonstrated excellent accuracy for face detection work. However, this architecture involves heavy computations and parameters because it uses many filter operations. It causes deep architecture to slow down the detector speed. Moreover, a practical application entails using a detector that can operate in real-time. The recent CPU-based face detectors operate slowly in an integrated system. This study proposes a faster face detector to predict the human face area using efficient architecture robustly. The architecture consists of a light backbone to discriminate distinctive features and a four detection module to predict multiple faces. In order to bridge the three prediction layers, it implements a high-level transition module with a cheap operation. It also offers a new light attentive block to highlight typical facial features at each detection module efficiently. As a result, this detector achieves excellent performance and outperforms other low computing detectors. The proposed detector can fast operate at 112 frames per second on a Core I5 CPU and at 11 frames per second on a Lattepanda device, faster than other competitors.
A method for balancing a multi-labeled biomedical dataset

In this paper, we propose a data balancing method for multi-label biomedical data. The method can be applied in the case of semantic segmentation problems for balancing the corresponding image data. The proposed method performs oversampling of instances of minority classes in a way that increases the frequencies of appearance (a ratio of number of samples, containing this class, over the total number of samples in the dataset) of minority classes in the data, thereby reducing the class imbalance. The effectiveness of the proposed method is shown experimentally by applying it to two highly unbalanced biomedical image datasets. A convolutional neural network (CNN) was trained on several versions of those datasets: one balanced with the proposed method, another balanced with manual oversampling and an unbalanced version. The results of the experiments validate the effectiveness of the proposed method, proving that it allows the influence of class imbalance on the learning algorithm to be reduced, thus improving its original classification results for most of the classes. Apart from biomedical image data, the proposed method was applied to several common multi-label datasets. Inherently, the proposed method does not make any assumptions about the underlying structure of the data to be balanced; therefore, it can be applied to all types of data (vectors, images, etc.) that can be described in a multi-label framework. It also can be used in conjunction with any learning algorithm that is suitable for multi-label data. To illustrate its wider applicability, a series of experiments was conducted using seven common multi-label datasets. An experimental comparison to existing multi-label data balancing approaches is provided, as well. The experimental results show that the proposed method presents a competitive alternative to existing approaches.
An Old Photo Image Restoration Processing Based on Deep Neural Network Structure

Old photos retain precious historical image information, but today’s existing old photos often have varying degrees of damage. Although these old photos can be digitally processed and then restored, the restoration of old photos involves multiple areas of image restoration and has various types of degradation. Currently, there is no unified model for repairing multiple types of degradation of old photos. Photo restoration technology still has a lot of developments. Traditional image restoration technology mainly repairs the missing areas of the image based on mathematical formulas or thermal diffusion. This technology can only repair images with simple structures and small damaged areas and is difficult to apply in people’s daily lives. The emergence of deep learning technology has accelerated the pace of research on image restoration. This article will discuss the methods of repairing old photos based on deep neural networks. It is aimed at proposing an image restoration method based on deep neural network to enhance the effect of image restoration of old photos and provide more possibilities for restoration of old photos. This article discusses the background significance of image restoration methods, designs an image restoration model based on deep neural networks, and introduces the structure, principle, and loss function of the model. Finally, this article did a comparative experiment to compare the model in this article with other models and draw the conclusion: in the blur repair experiment, the algorithm in this paper is better than other algorithms for the peak signal-to-noise ratio and structure similarity of the repaired image; in the damage repair experiment, the value of the algorithm’ s peak signal-to-noise ratio is 32.34, and the structure similarity under different damage average levels is 0.767, which is also higher than other algorithms. Therefore, the model in this paper has the best effect on image restoration.
Personality and emotion based cyberbullying detection on YouTube using ensemble classifiers

This paper investigates the effect of users’ personality traits and emotions expressed through textual communications on YouTube to detect cyberbullying using a series of ensemble classifiers. Personality traits were determined using the Big Five model whereas emotions were based on Ekman’s basic emotion theory. Annotated YouTube textual comments in English (N = 5152; i.e. 2576 number of bullying versus 2576 non-bullying instances) were used to detect cyberbullying incidents using several ensemble classifiers, including Random Forest and AdaBoost. Performance metrics revealed both personality traits and emotion to significantly improve the identification of cyberbullying presence, with accuracy and F-score values of more than 95%. Further fine-grained analysis revealed anger and openness to be more profound compared to other emotions and personalities. Further, neurotic individuals tend to be driven to cyberbullying by joy, disgust and fear. The findings show that personality and emotions play pertinent roles in cyberbullying, and the identification of specific traits and emotions can help in designing a more strategic intervention programme.