Reviewerly/exHarmony huggingface dataset:
Improving Document-level Relation Extraction via Contextualizing Mention Representations andWeighting Mention Pairs

Document-level relation extraction (RE) has attracted considerable attention, because a large number of relational facts are expressed in multiple sentences. Recently, encoder-aggregator based models have become promising for document-level RE. However, these models have two shortcomings: (i) they cannot obtain contextualized representations of a mention by low computational cost, when the mention is involved in different entity pairs; (ii) they ignore the different weights for the mention pairs of a target entity pair. To tackle the above two problems, in this paper, we propose a novel encoder-attender-aggregator model, which introduces two attenders between the encoder and aggregator. Specifically, a mutual attender is first employed on the selected head and tail mentions to efficiently produce contextualized mention representations. Then, an integration attender is utilized to weight the mention pairs of a target entity pair. Extensive experiments on two document-level RE datasets show that the proposed model performs better than the state-of-the-art baselines. Our codes are publicly available at “https://github.com/nefujiangping/EncAttAgg”.
End-To-End Learning from Demonstation for Object Manipulation of Robotis-Op3 Humanoid Robot

Humanoid robots are deployed ranging from houses and hotels to healthcare and industry environments to help people. Robots can be easily programed by users to predefined tasks such as walking, grasping, stand-up, and shake-up. However, in these days, all robots are expected to learn itself from the obtained experience by watching the environment and people in there. In this study, it is aimed for Robotis-Op3 humanoid robot to grasp the objects by learning from demonstrations based on vision. A new algorithm is proposed for this purpose. Firstly, the robot is manipulated from user commands and the raw images from the camera of Robotis-Op3 are collected. Secondly, a semantic segmentation algorithm is applied to detect and recognize the objects. A new model using Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) is then proposed to learn the user demonstrations. The results were compared in terms of training time, performance, and model complexity. Simulation results showed that new models produced a high performance for object manipulation.
Intelligent Recommender Systems in E-Commerce

Among thousands of alternatives, most of the time online customers cannot easily decide on which product to purchase or service to utilize. In order to assist online customers in their decision-making process, business owners have started to make their online platforms more intelligent by enhancing their platforms with intelligent recommender systems. Recommender systems, also known as recommender agents or intelligent agents, are intelligent software that provide easily accessible, personalized, highly relevant, and high-quality recommendations to customers in various online platforms. This chapter discusses different types of recommender systems and provides use case examples of recommender systems in various e-commerce platforms. This chapter shows how recommender systems make life easier for online customers in the constantly developing and growing internet environment. This chapter also discusses the challenges posed by recommender systems to online customers.
Research on Cooperative Obstacle Avoidance Control of UAV Formation Based on Improved Potential Field Method

In order to solve the problems of self-collision, obstacle avoidance and formation communication architecture of UAVs, a distributed formation control strategy was proposed using the combination of graph theory and artificial potential field theory. Firstly, the UAV formation model of fixed undirected communication topology was established. For the issues that may occur such as deadlocks and formation safety, the segmented idea, rotation force and potential force between the machines were introduced to improve the force function of the potential field, which made the formation realize obstacle avoidance and trajectory pre-planning accurately. Secondly, the state error of UAV was corrected according to the consistency theory, and the formation cooperative obstacle avoidance control law was designed to achieve the desired state consistency and track planning. Finally, the convergence consistency of the proposed control algorithm was proved by constructing Hamilton function. The simulation results show that the UAV formation can avoid obstacles effectively and achieve the desired consistency quickly.
A Survey: Applications of Blockchains in the Internet of Vehicles

To enhance the performance of Internet of Vehicles (IoVs), it is essential to solve some security concerns such as distributed trust management among vehicles. Blockchain, a ledger technology with the characteristics of distributivity, immutability and security could be a way to overcome these security issues and to get better performance in the IoVs. Thus, the integration of the blockchain and the IoV is an important research direction currently. With the aim to provide directions for future work in the area of blockchain-based vehicular networks, this survey illustrates different applications of the blockchain in vehicular environment from different perspectives. Firstly, we compare and analyze the existing surveys about the blockchain-enabled IoV environment. Besides, several works with the aim to overcome vehicular security issues are presented from different aspects. Finally, the future research directions related to the integration of the blockchain in IoVs are discussed.
Composition Algorithm Adaptation in Service Oriented Systems

Architecting and constructing software systems using Service Oriented Architecture (SOA) is a widely employed paradigm. Application functionality is commonly delivered by composing Internet communicable software components or services. Using SOA, applications are constructed by a well-defined composition process that implements composition logic to meet an application's functional and non-functional requirements. Various composition techniques have been proposed in the literature, with varying performance guarantees and resource usage. Service composition also has to adapt to unanticipated conditions posed by a highly dynamic environment due to changing services, evolving architectures, and user requirements. Current adaptive methodologies determine a composition technique at design time and adapt selection and binding of service at runtime. In this paper, we propose adaptation of composition techniques for each user request. Our data driven approach selects the best composition technique for a given application dependency graph. It learns adaptation rules from execution data and trades-off resource usage and solution quality of composition techniques.
MUEnsemble: Multi-ratio Undersampling-Based Ensemble Framework for Imbalanced Data

Class imbalance is commonly observed in real-world data, and it is still problematic in that it hurts classification performance due to biased supervision. Undersampling is one of the effective approaches to the class imbalance. The conventional undersampling-based approaches involve a single fixed sampling ratio. However, different sampling ratios have different preferences toward classes. In this paper, an undersampling-based ensemble framework, MUEnsemble, is proposed. This framework involves weak classifiers of different sampling ratios, and it allows for a flexible design for weighting weak classifiers in different sampling ratios. To demonstrate the principle of the design, in this paper, three quadratic weighting functions and a Gaussian weighting function are presented. To reduce the effort required by users in setting parameters, a grid search-based parameter estimation automates the parameter tuning. An experimental evaluation shows that MUEnsemble outperforms undersampling-based methods and oversampling-based state-of-the-art methods. Also, the evaluation showcases that the Gaussian weighting function is superior to the fundamental weighting functions. In addition, the parameter estimation predicted near-optimal parameters, and MUEnsemble with the estimated parameters outperforms the state-of-the-art methods.
A New Sport Teams Logo Dataset for Detection Tasks

In this research we introduce a new labelled SportLogo dataset, that contains images of two kinds of sports: hockey (NHL) and basketball (NBA). This dataset presents several challenges typical for logo detection tasks. A huge number of occlusions and logo view changes during playing games lead to an ambiguity of a straightforward detection approach use. Another issue is logo style changes due to seasonal kits updates. In this paper we propose a two stage approach, in which, firstly, an input image is processed by a specially trained scene recognition convolutional neural network. Second, conventional object detectors are applied only for sport scenes. Experimental study contains results of different combinations of backbone and detector convolutional neural networks. It was shown that MobileNet + YOLO v3 solution provides the best quality results on the designed dataset (mAP = 0.74, Recall = 0.87).
Dynamic and Secure Memory Transformation in Userspace

Continuous code re-randomization has been proposed as a way to prevent advanced code reuse attacks. However, recent research shows the possibility of exploiting the runtime stack even when performing integrity checks or code re-randomization protections. Additionally, existing re-randomization frameworks do not achieve strong isolation, transparency and efficiency when securing the vulnerable application. In this paper we present Chameleon, a userspace framework for dynamic and secure application memory transformation. Chameleon is an out-of-band system, meaning it leverages standard userspace primitives to monitor and transform the target application memory from an entirely separate process. We present the design and implementation of Chameleon to dynamically re-randomize the application stack slot layout, defeating recent attacks on stack object exploitation. The evaluation shows Chameleon significantly raises the bar of stack object related attacks with only a 1.1% overhead when re-randomizing every 50 ms.
Ladder Pyramid Networks For Single Image Super-Resolution

Benefiting from the powerful representation capability of convolutional neural networks, the performance of single image super-resolution (SISR) has been substantially improved in recent years. However, many current CNN-based methods are computation-intensive because of large-size intermediate feature maps and inefficient convolutions. To resolve these problems, we propose Ladder Pyramid Network (LPN) for single image super-resolution. Firstly, we use strided convolution to reduce the size of the intermediate feature maps and thus reducing computation burden. In order to better balance the effectiveness and efficiency, we propose Ladder Pyramid Module to gradually fuse hierarchical features to enhance performance. Secondly, lightweight convolution block similar to Inverted Residual Module of Mobilenet-v2 was introduced into SISR, with which we build the network backbone and ladder feature pyramid. Experimental results demonstrate that the proposed Ladder Pyramid Network can achieve comparable or better performance than previous lightweight networks while reducing the amount of computation.
Clustering emotions of people in various Indian cities using Instagram

In the growing age of social media, people express their emotions in the form of pictures, videos and text. And these emotions often affect a person’s life in many aspects. The main aim of this study was to examine how people of the most literate cities of India post different emotions through pictures on Instagram. In our work we have created a dataset of 60,000 Instagram posts for the top 200 literate cities in India, 300 posts for each city. The analysis of this dataset shows that 80% of the images consist of happy emotion, neutral in 82%, sadness in 43%, contempt in 47%, surprise in 20%, and for disgust and fear it is 9% and 6% respectively.
Approaching human precision on automatic markerless tracking of human movements

Ovarian cancer, whether it occurs within or near the ovaries, involves different types of malignancies. While most ovarian tumors are benign, the malignant forms make up the most fatal gynecologic malignancies in the United States, as well as in other countries that regularly screen women for neoplasia of the cervix. The ovaries are responsible for maturation of follicles, usually between ages 11 and 50. They regulate maturation of eggs, ovulation, and the cyclical production of sex steroid hormones. Various ovarian cells coordinate these biologic functions. Each of the cell types has the potential to become neoplastic. Tumors may also metastasize to the ovaries from the breasts, colon, appendix, stomach, or pancreas. Bilateral ovarian masses from primary mucin-secreting gastrointestinal tumors are called Krukenberg tumors. This chapter focuses on tumors of epithelial origin, sex cord and stromal tumors, and germ cell tumors.
Research on Computer Network Information Security Based on Big Data Technology

Internet big data is related to our personal privacy and property security. In serious cases, it also affects the security of confidential information of related companies. Therefore, big data must be protected to prevent criminals from stealing our personal privacy, property information and corporate secrets. Based on this research background, the paper designs the design of computer network security defines system. After the system design is implemented, the system is tested accordingly. According to the test results, the computer network security defines system designed in this paper can actively detect and effectively prevent security threats in the network, thereby ensuring that the network can Normal and safe operation. The computing network security defines system can also provide effective ideas for future network security protection and achieve further expansion of security defines.
Cybersecurity Vulnerabilities in Mobile Fare Payment Applications: A Case Study

Mobile fare payment applications are becoming increasingly common in the public transportation industry as a convenience for customers and as part of an effort to reduce fare management costs and improve operations for agencies. However, there is relatively little literature on vulnerabilities and liabilities in mobile fare payment applications. Furthermore, few public agencies or supporting vendors have policies or established processes in place to receive vulnerability reports or patch vulnerabilities discovered in their technologies. Given the rapidly increasing number of data breaches in general industry IT systems, as well as that mobile fare payment apps are a nexus between customer and agency financial information, the security of these mobile applications deserves further scrutiny. This paper presents a vulnerability discovered in a mobile fare payment application deployed at a transit agency in Florida that, because of the system architecture, may have affected customers in as many as 40 cities across the United States, an estimated 1,554,000 users. Lessons learned from the vulnerability disclosure process followed by the research team as well as recommendations for public agencies seeking to improve the security of these types of applications are also discussed.
State of the Practice Survey: Predicting the Influence of AI Adoption on System Software Architecture in Traditional Embedded Systems

Artificial intelligence (AI) is a very disruptive technology. When adopted by a software system, AI influences and significantly changes its architecture due to its complexity, as well as due to a need to adjust the existing system to use AI (e.g., adopt accelerators). This is particularly critical in traditional embedded systems as they focus on a tight coupling of software and hardware. In this paper, we present results of a survey on how well companies in embedded software domain understand AI, how they perceive its possible benefits, and how they discuss the adoption of AI and its influence on their software system architecture. The goal of this survey is to evaluate architectural techniques that companies currently use when trying to assess the influence of adopting AI and to discuss the adequacy of these techniques for this task.
Side Channel Leakage Assessment Strategy On Attack Resistant AES Architectures

A leakage assessment / evaluation and comparative analysis on various architectures of masked AES-128 cryptographic algorithm to mitigate the side channel attack is presented. In this work, unmasked and masked implementations of S-Box for AES-128 at micro architecture level and also generation of random mask using True random number generator based on Galois ring oscillator of polynomial 16 is implemented and discussed in detail. Side Channel Leakage is evaluated by Correlation Power Analysis (CPA) and Leakage assessment using Welch's t-test. The implementations are realized in SAKURA-G Side channel evaluation Board. This work quantifies the effectiveness of unmasked and masked AES implementations in side channel mitigation and also identifies the point of leakage and approximate number of traces required to identify the leakage on the adopted mitigation technique is also presented.
UPPER BOUNDS ON POLYNOMIALS WITH SMALL GALOIS GROUP

When monic integral polynomials of degree n ⩾ 2 are ordered by the maximum of the absolute value of their coefficients, the Hilbert irreducibility theorem implies that asymptotically 100% are irreducible and have Galois group isomorphic to S n . In particular, among such polynomials whose coefficients are bounded by B in absolute value, asymptotically ( 1 + o ( 1 ) ) ( 2 B + 1 ) n are irreducible and have Galois group S n . When G is a proper transitive subgroup of S n , however, the asymptotic count of polynomials with Galois group G has been determined only in very few cases. Here, we show that if there are strong upper bounds on the number of degree n fields with Galois group G, then there are also strong bounds on the number of polynomials with Galois group G. For example, for any prime p, we show that there are at most O ( B 3 − 2 p ( log B ) p − 1 ) polynomials with Galois group C p and coefficients bounded by B.
Improved Cloud-Assisted Privacy-Preserving Profile-Matching Scheme in Mobile Social Networks

Due to the transparency of the wireless channel, users in multiple-key environment are vulnerable to eavesdropping during the process of uploading personal data and re-encryption keys. Besides, there is additional burden of key management arising from multiple keys of users. In addition, profile matching using inner product between vectors cannot effectively filter out users with ulterior motives. To tackle the above challenges, we first improve a homomorphic re-encryption system (HRES) to support a single homomorphic multiplication and arbitrarily many homomorphic additions. The public key negotiated by the clouds is used to encrypt the users’ data, thereby avoiding the issues of key leakage and key management, and the privacy of users’ data is also protected. Furthermore, our scheme utilizes the homomorphic multiplication property of the improved HRES algorithm to compute the cosine result between the normalized vectors as the standard for measuring the users’ proximity. Thus, we can effectively improve the social experience of users.
De-identifying free text of Japanese electronic health records

Abstract Background Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially available data, can greatly improve healthcare. Although EHR de-identification is necessary to protect personal information, automatic de-identification of Japanese language EHRs has not been studied sufficiently. This study was conducted to raise de-identification performance for Japanese EHRs through classic machine learning, deep learning, and rule-based methods, depending on the dataset. Results Using three datasets, we implemented de-identification systems for Japanese EHRs and compared the de-identification performances found for rule-based, Conditional Random Fields (CRF), and Long-Short Term Memory (LSTM)-based methods. Gold standard tags for de-identification are annotated manually for age, hospital , person , sex , and time . We used different combinations of our datasets to train and evaluate our three methods. Our best F1-scores were 84.23, 68.19, and 81.67 points, respectively, for evaluations of the MedNLP dataset, a dummy EHR dataset that was virtually written by a medical doctor, and a Pathology Report dataset. Our LSTM-based method was the best performing, except for the MedNLP dataset. The rule-based method was best for the MedNLP dataset. The LSTM-based method achieved a good score of 83.07 points for this MedNLP dataset, which differs by 1.16 points from the best score obtained using the rule-based method. Results suggest that LSTM adapted well to different characteristics of our datasets. Our LSTM-based method performed better than our CRF-based method, yielding a 7.41 point F1-score, when applied to our Pathology Report dataset. This report is the first of study applying this LSTM-based method to any de-identification task of a Japanese EHR. Conclusions Our LSTM-based machine learning method was able to extract named entities to be de-identified with better performance, in general, than that of our rule-based methods. However, machine learning methods are inadequate for processing expressions with low occurrence. Our future work will specifically examine the combination of LSTM and rule-based methods to achieve better performance. Our currently achieved level of performance is sufficiently higher than that of publicly available Japanese de-identification tools. Therefore, our system will be applied to actual de-identification tasks in hospitals.
Bicomplex Projection Rule for Complex-Valued Hopfield Neural Networks

A complex-valued Hopfield neural network (CHNN) with a multistate activation function is a multistate model of neural associative memory. The weight parameters need a lot of memory resources. Twin-multistate activation functions were introduced to quaternion- and bicomplex-valued Hopfield neural networks. Since their architectures are much more complicated than that of CHNN, the architecture should be simplified. In this work, the number of weight parameters is reduced by bicomplex projection rule for CHNNs, which is given by the decomposition of bicomplex-valued Hopfield neural networks. Computer simulations support that the noise tolerance of CHNN with a bicomplex projection rule is equal to or even better than that of quaternion- and bicomplex-valued Hopfield neural networks. By computer simulations, we find that the projection rule for hyperbolic-valued Hopfield neural networks in synchronous mode maintains a high noise tolerance.