Reviewerly/exHarmony huggingface dataset:
Identity-Based Encryption With Continuous Leakage-Resilient CCA Security From Static Complexity Assumption

Abstract Although a large number of provably secure cryptographic primitives have been proposed in the literature, many of these schemes might be broken in practice because of various leakage attacks. Therefore, the leakage resilience should be considered in designing these primitives. However, in identity-based cryptography, most of the existing leakage-resilient identity-based encryption (IBE) schemes suffer some limitations: they either resist the leakage attacks in the selective identity security model or achieve the chosen-ciphertext attack (CCA) security based on a non-static assumption. In this paper, an IBE scheme with adaptive leakage-resilient CCA security is proposed, and its security is rigorously proved in the random oracle model under a classic static complexity assumption, e.g. decisional bilinear Diffie–Hellman assumption. In our construction, all elements of ciphertext are randomly distributed in the adversary’s view. Hence, the adversary cannot obtain any useful information of the user’s private key from the given ciphertexts. Moreover, a unique property of our construction is that the leakage parameter is independent of the plaintext space, which contributes a better leakage rate.
Model Robust Optimization Method of Using Feature Pyramid

Under the artificial intelligence adversarial environment, deep neural networks have an obvious vulnerability to adversarial samples. To improve the robustness of the model in the adversarial environment, a deep neural network model robustness optimization method is proposed: the target model is transformed into a feature pyramid structure to deepen the model level. Experiments on the MNIST and CIFAR-10 dataset show that under high disturbances, the feature pyramid model on the MNIST dataset has at least 2 times the defense against FGSM and JSMA attacks compared to the original model, and the defense ability against against PGD, BIM, and C&W attacks is increased at least 6 times. The defensive ability of the feature pyramid model on the CIFAR-10 dataset is increased by at least 2 times compared with the original model, and the defense ability against C&W and JSMA attacks is increased at least 5 times. The defensive effect is obvious, it provides an efficient and robust optimization method for the DNN model in the adversarial environment.
Rule-Based Semantic Role Labeling of Bei-sentences

Case markers are commonly utilized for guiding semantic relations labeling during the semantic role labelling process. The determination of some semantic roles in Bei-sentences often relies on the use of the marker “Bei”. However, Chinese expressions involve complicated semantic relations and the marker “Bei” is even immediately followed by another marker, which affect the accuracy of semantic role labeling. Therefore, by identifying the problems in Bei-sentences during the semantic role labeling process, the Bei-sentence libraries incorporating a great deal of various Bei-sentences are searched and then manually labeled to generate semantic role labeling rules for labeled Bei-sentences. Then aforesaid rules are then verified on testing data. The experimental results show that semantic role labeling rules proposed in this paper are useful for semantic role labeling of labeled Bei-sentences.
Virtual Conferences due to Covid‐19 Pandemic

The Photogrammetric RecordVolume 35, Issue 171 p. 446-446 Notes Virtual Conferences due to Covid-19 Pandemic First published: 20 September 2020 https://doi.org/10.1111/phor.12334Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume35, Issue171September 2020Pages 446-446 RelatedInformation
Privacy-preserving Aggregation Scheme for Blockchained Federated Learning in IoT

The rapid increase in Internet of Things (IoT) connected devices and generated data has greatly promoted the development of artificial intelligence. Federated Learning (FL) provides strong support for the data-driven society by training the model locally without leaking the privacy of the client. However, due to the unreliability of IoT devices and the centralized training model of FL, there are still security risks such as privacy leakage in federated learning. Therefore, this paper proposes a decentralized and privacy-preserving aggregation scheme, named DPPA, for FL. DPPA leverages the blockchain to structure a decentralized FL architecture, and leverages Paillier cryptosystem to realize the privacy protection of local data and the safe aggregation of model parameters. Through security analysis, we demonstrate that DPPA resists various security threats and preserve client privacy. Experiments show that in the large-scale IoT device environment, DPPA has great advantages over the existing competing approaches in terms of computational overhead.
Bounded Abstract Effects

Effect systems have been a subject of active research for nearly four decades, with the most notable practical example being checked exceptions in programming languages such as Java. While many exception systems support abstraction, aggregation, and hierarchy (e.g., via class declaration and subclassing mechanisms), it is rare to see such expressive power in more generic effect systems. We designed an effect system around the idea of protecting system resources and incorporated our effect system into the Wyvern programming language. Similar to type members, a Wyvern object can have effect members that can abstract lower-level effects, allow for aggregation, and have both lower and upper bounds, providing for a granular effect hierarchy. We argue that Wyvern’s effects capture the right balance of expressiveness and power from the programming language design perspective. We present a full formalization of our effect-system design, showing that it allows reasoning about authority and attenuation. Our approach is evaluated through a security-related case study.
Manipulation-Invariant Fingerprints for Cross-Dataset Deepfake Detection

Most of the current deepfake detection methods make efforts to learn the classifiers on the manipulated face dataset forged by a single manipulation method. However, these classifiers lack the generalization capacity to other kinds of manipulated face images, limiting their applications in real-world scenarios. In this paper, we find common detectable fingerprints for face images manipulated by different forgery methods through mapping images into a fingerprint space. Such fingerprints are called manipulation-invariant fingerprints, which would allow a classifier to generalize to face datasets forged by other methods. Therefore, a mask drop regularized unsupervised domain adaptation (MDRUDA) method is proposed for cross-dataset deepfake detection through building up the manipulation-invariant fingerprints. Specifically, a feature generator and a discriminator are trained adversarially to align the fingerprint distributions from face images forged by different methods, building up the manipulation-fingerprints that could train a more generalized classifier. We impose a mask drop regularization on the discriminator and the classifier to enrich the fingerprint space and further boost generalization ability. Experiments on public deepfake datasets show that our approach can gain significantly better generalization capability in cross-datasets scenarios, compared with prior works.
Akan-English: Transformer for Low Resource Translation

Recent years, Neural Machine Translation outperforms the existing traditional-based models for language translation with the use of sequential architecture. However, low resource languages, such as Akan-Twi, lack structured language resources largely due to the limited amount of research geared towards their revival and resuscitation. In an effort to mitigate the issue of low resource language translation from Akan-Twi to English in machine learning, we utilize the low resource parallel corpus for the translation setting of Akan-English. Using our proposed training process and Transformer-based model, we generate augmented corpus which is used to improve the performance of our final translation model. We achieved best BLEU score of 12.96 and 17.57 for the Akan-English and English-Akan, respectively.
Adaptive Gradient-Free Method for Stochastic Optimization

In this paper we propose adaptive gradient-free coordinate-wise method for stochastic optimization. Adaptivity is based on line-search to simultaneously adjust to unknown Lipschitz constant and variance. In contrast to previous works, our method allows the guess for the Lipschitz constant to decrease to perform larger steps and benefit convergence. The method is practically applicable to adversarial attacks and reinforcement learning.
Lightweight Hardware-Based Memory Protection Mechanism on IoT Processors

As the Internet of Things (IoT) systems become more and more popular, many devices around life are now connected to the internet. While allowing large amounts of information to be transmitted and exchanged, sensitive information in IoTs also becomes vulnerable to leakage. Therefore the security of each device and network in IoT systems has become a critical issue. Many IoT devices are simple devices used for daily supplies; they cannot cost too much and must be lightweight. These devices are often controlled by simple Operating Systems (OS), some even in bare-metal environments without OS. In this paper we propose a lightweight memory security mechanism implemented mainly by hardware. This mechanism puts critical data in secure regions, and the processor can access the secure regions only after passing a hardware-based authentication process. Our method allows the processor to maintain high security without relying on the OS; hence even if the IoT device only has a bare-metal environment, it still can protect important data at a low cost. Experimental results show that a very high level of security can be achieved with only a very small extra delay and area overhead required.
Stylistically User-specific Response Generation

The ability to capture the conversation context is a necessity to build a good conversation model. However, a good model must also provide interesting and diverse responses to mimic actual human conversations. Given that different people can respond differently to the same utterance, we believe that using user-specific attributes can be useful for a conversation task. In this study, we attempt to drive the style of generated responses to resemble the style of real people using user-specific information. Our experiments show that our method applies to both seen and unseen users. Human evaluation also shows that our model outperforms the baselines in terms of relevance and style similarity.
Hybrid Wind-PV Plant Sizing Strategies. Drivers and Cost Analysis

The core of the research has focused on giving answer to the feasibility of a hybrid wind-PV plant and define the optimal sizing of the photovoltaic plant to be added to an existing wind installation. The analysis carried out was based on the study of 7,209 virtual units of wind sources located in Spain. Virtual units refer to wind plants located in Spain which energy production capacity is known. For each virtual energy production unit, the hybrid plant resulting from adding an attached photovoltaic installation has been simulated so that both energy production systems are combined. Authors have determined the optimal sizing of PV plant in a rate of 1/10 PV/Wind, aiming to maximize the NPV of the investment. Findings will help in the design of hybrid plants in an easy manner, even though future calculations would have to be carried out to fine-tune the sizing of the PV plant. This paper aims to align with the objectives of sustainable development of the United Nations, in particular objective 7, by which a greater proportion of energy based on renewable sources is pursued by 2030, doubling the world rate of energy efficiency and improving the research strategy towards less polluting energies.
Understanding of Data De-identification: Issues of Relevance and Problems

This paper presents the process of data anonymisation and de-identification, the use of de-identification techniques as a means of data protection, and analyses de-identification and their relevance to the tasks they perform. There is also understanding re-identification risks and reasons to attempt a re-identification attacks. In addition, it also focuses on the actuality of solving the problem of data anonymisation and the problems of its implementation.
Techniques and Tools for Trainers and Practitioners

If the material is to be delivered effectively, organizations need to understand the human side of cyber security training. In this chapter, the authors draw upon over a decade of experience in creating and adapting training and resources with the help of industry professionals and feedback from clients, which has led to a successful and highly acclaimed approach to cybersecurity education. The resulting discussion considers how to adopt the right approach to cybersecurity training for organizations, with training modules that cater to end users, and which are designed to ensure maximum retention of information by presenting short, humorous, animated scenarios that are relatable for the target audience.
QR Code Style Transfer Method Based on Conditional Instance Regularization

We present a Quick Response (QR) code style transfer network based on conditional instance regularization to retain more QR code information and incorporate multiple styles into a single model. Firstly, we introduce conditional instance regularization into the QR code style transfer network and incorporate multiple styles into a single model. It improves the efficiency of multi-style transfer training. Secondly, the weighted fusion corrected method of the styled QR code is designed to repair the damage of positioning graphics and information modules in the QR code, enhance the styled QR code’s identifiability and guarantee the integrity of information modules. Because of the shortcomings of the static QR code, a dynamic artistic style QR code is proposed. The experimental results show that the proposed QR code style transfer method is effective.
Correlation Filter Tracking based on Target Deformation and Occlusion

On the basis of KCFDPT and CFAPS, a novel correlation filter tracking method is proposed for the complex video scene with target deformation and occlusion. The proposed method firstly uses STRCF rather than KCF to achieve better position location, which can deal with occlusion effectively. Meanwhile, APCE (Average Peak-to-Correlation Energy) is adopted for evaluating the reliability of location and used to determine the initial range of scale detection. The Edgeboxes method in KCFDPT then is applied to generate the proposals with different scale. Next, aspect ratio, area and color similarity in CFAPS are used to screen the proposals adaptively. Finally, the response of each remaining proposal is calculated in the filter of STRCF in turn, and the proposal with the maximum response is taken as the best scale, and the new position and scale are updated to the model. The videos with target deformation and occlusion are tested with the proposed method on the OTB2015 dataset. Experimental results show that the tracking accuracy and success rate of the proposed algorithm are higher than that of traditional algorithms of KCF, DSST, SAMF, KCFDPT and CFAPS, and the algorithm can be robust in the tracking drift caused by target deformation and occlusion.
A high specificity deep learning approach with focus on breast cancer screening

Breast cancer is the leading type of cancer in women and the second most common cancer overall. With thousands of breast screening exams being performed daily around the world, it is a time-consuming task for radiologists, that often find it difficult to analyze and classify them all, with most of exams turning out to be normal cases. The dedicated time to review negative cases could be applied to reviewing more complex cases that require additional care. In this work, the authors propose a support to diagnosis system, focused on mammography screening, based on deep Convolutional Neural Networks (CNNs) and an ensemble classifier, designed to relieve radiologists of normal cases. The architecture takes advantage of macroscopic full mammogram-level features, patch-level features, and patient metadata to output an image-level classification. The results translate the architectures’ high confidence on the exam being normal, relieving the radiologist from analyzing the exam, or suspicious, requiring a specialized radiologist’s attention for a final classification. The developed system was trained and validated using three public datasets (CBIS-DDSM, BCDR and INbreast) and achieved a final, exam-level AUC of 0.98, with a specificity of 96% and a sensitivity of 87%. A conclusion of this work is the possibility to reduce the radiologist’s workload, with potential to reduce the requirement of a second reader, creating further opportunities to review and analyze more complex cases.
Representations for evolutionary algorithms

tutorial Representations for evolutionary algorithms Author: Franz Rothlauf Johannes Gutenberg-Universität Mainz, Mainz, Germany Johannes Gutenberg-Universität Mainz, Mainz, GermanyView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 463–483https://doi.org/10.1145/3449726.3461430Published:08 July 2021Publication History 0citation73DownloadsMetricsTotal Citations0Total Downloads73Last 12 Months17Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Construction of SPOC Blending Teaching Model of Computer English Course in Application-Oriented Institute

By analyzing the current situation and existing problems of computer major students’ English ability-training in application-oriented universities, this paper points out that it is pressing to cultivate pragmatic competence of computer English for training qualified computer talents. Considering the characteristics that the computer English course involves both English skills and computer knowledge, it also puts forward a SPOC Blending Teaching Model of computer English course in application-oriented universities. In order to meet the application-oriented undergraduate talent training objectives, it reorganizes the high-quality internet resources for pre-class teaching based on the principle of “application and practice”. It proposes that teaching contents should be reclassified according to the theme and different teaching stages. It aims to effectively solve the problems of class-hour reduction and obsolescence of teaching contents in Application-oriented computer English teaching and to improve the teaching efficiency.
(Re-)embodied Digital Education Practices: Empirical Vignettes About Teaching and Learning in ‘Tele-co-presences’

This chapter reviews forms and effects of increased digitalised learning from an embodied phenomenological perspective. In particular, the moves from and between embodied co-presence to virtual tele-presence and its implications are reflected on critically and illustrated by using empirical vignettes. Furthermore, some possibilities for re-embodying and the need for re-purposing teaching and learning activities are outlined. Finally, some perspectives on the future of an integral embodied and transformative pedagogy are offered.
Cloud Computing Security Challenges and Solutions

in this paper focuses on development of cloud computing, privacy security issues have become increasingly prominent, which is of concern to industry and academia. We review the research progress on privacy security issues from the perspective of several privacy security protection technologies in cloud computing. First, we introduce some privacy security risks of cloud computing and propose a comprehensive privacy security protection framework.
Automatic Liver and Hepatic Tumors Segmentation in CT Images Using Convolutional Neural Networks

Automatic segmentation of liver and hepatic tumors is a task that has been widely studied in recent years but is still a demanding one. The largely demanded approach to patient care is a requirement for computer-aided diagnosis, treatment planning and liver cancer monitoring. In this paper is presented the performance of Convolutional Neural Networks (CNN) in liver and hepatic tumors segmentation, using a modified U-Net model. The architecture of the used CNN is composed by three down-sampling blocks, a central concatenated dilated bottleneck and three up-scaling blocks. The architecture was trained and tested on various datasets provided by the public medical dataset 3D-IRCADb-01. In liver segmentation the achieved Dice value was 0.9 with 99% accuracy and in the tumor case the accuracy obtained was about 98%. The aim of this paper is to explore the use of modified U-Net CNN for automatic segmentation of liver and hepatic tumors.
Predicting Loss Risks for B2B Tendering Processes

Sellers and executives who maintain a bidding pipeline of sales engagements with multiple clients for many opportunities significantly benefit from data-driven insight into the health of each of their bids. There are many predictive models that offer likelihood insights and win prediction modeling for these opportunities. Currently, these win prediction models are in the form of binary classification and only make a prediction for the likelihood of a win or loss. The binary formulation is unable to offer any insight as to why a particular deal might be predicted as a loss. This paper offers a multi-class classification model to predict win probability, with the three loss classes offering specific reasons as to why a loss is predicted, including no bid, customer did not pursue, and lost to competition. These classes offer an indicator of how that opportunity might be handled given the nature of the prediction. Besides offering baseline results on the multi-class classification, this paper also offers results on the model after class imbalance handling, with the results achieving a high accuracy of 85% and an average AUC score of 0.94.
A Cryptographic Resource Management Framework and Dynamic Migration Method Based on Virtualization

In order to meet the complex and diverse cryptographic resource management requirements in the cloud environment, a cryptographic resource management framework based on virtualization technology is proposed. The framework includes a cryptographic resource management unit, a cryptographic resource processing unit, a cryptographic resource storage unit. It can adapt to diverse cryptographic application systems and achieve interconnection and interoperability. A dynamic migration method of the cryptographic service virtual machine is given. Based on the network shared storage system and the memory iterative copy algorithm, it can realize the scheduling and migration of the cryptographic service virtual machine. The management framework and the migration method can realize the normalized processing and scheduling of cryptographic resources, and adapt to a variety of scenarios and cryptographic application requirements in the cloud environment.