Reviewerly/exHarmony huggingface dataset:
A Cooperative Framework with Generative Adversarial Networks and Entropic Auto-Encoders for Text Generation

Generating text with high quality and sufficient diversity is a fundamental task in natural language generation. Although generative adversarial networks (GANs) achieve promising results in text generation, GAN-based language models suffer from mode collapse, i.e., the generator tends to sacrifice diversity and focus on limited text patterns with high quality. By contrast, maximum likelihood estimation (MLE) based language models could cover various text patterns and generate diversified samples with poor quality. This paper proposes a cooperative framework with GANs and entropic auto-encoders (EAEs), named GAN-EAE, to synthesize their advantages for text generation, where EAEs are powerful MLE-based generative models based on deterministic auto-encoders. By imitating the output distribution of EAEs, the generator shapes its output distribution closer to the real data distribution against mode collapse. Meanwhile, by learning the samples from the generator of GANs, EAEs subtly distribute probability mass on high quality patterns for improving generation quality. The similar samples obtained from the generator may raise mode collapse and should be downplayed during adversarial training. Thus, a sample re-weighting mechanism is adopted to improve diversity by measuring the inner distance of generated samples. Experimental results demonstrate that GAN-EAE could improve both GANs and EAEs to achieve state-of-the-art performance.
An Encoder-Decoder Network with Residual Convolution for Leukocyte Image Segmentation

The appearance analysis and counting of peripheral blood leukocytes can assist the diagnosis of blood diseases such as leukemia. Therefore, it is necessary to automatically extract leukocytes from blood smear images. In this paper, we propose an end-to-end peripheral blood leukocyte image segmentation method based on fully convolutional neural network, which can not only encode multi-scale contextual information, but also capture clear boundary information through decoding. Specifically, the proposed method first utilizes the context-aware feature encoder with residual convolutions to extract a set of multi-scale features. Then, a refinement module of parallel dilated convolutions with multiple dilation rates for expanding the receptive field of the feature maps is introduced to aggregate contextual information. Finally, clear prediction results are reconstructed by the context-aware feature decoder and skip connections. We demonstrate the effectiveness of the proposed method on two publicly available datasets.
A Unified Framework for Biphasic Facial Age Translation With Noisy-Semantic Guided Generative Adversarial Networks

Biphasic facial age translation aims at predicting the appearance of the input face at any age. Facial age translation has received considerable research attention in the last decade due to its practical value in cross-age face recognition and various entertainment applications. However, most existing methods model age changes between holistic images, regardless of the human face structure and the age-changing patterns of individual facial components. Consequently, the lack of semantic supervision will cause infidelity of generated faces in detail. To this end, we propose a unified framework for biphasic facial age translation with noisy-semantic guided generative adversarial networks. Structurally, we project the class-aware noisy semantic layouts to “soft” latent maps for the following injection operation on the individual facial parts. In particular, we introduce two sub-networks, ProjectionNet and ConstraintNet. ProjectionNet introduces the low-level structural semantic information with noise map and produces “soft” latent maps. ConstraintNet disentangles the high-level spatial features to constrain the “soft” latent maps, which endows more age-related context into the “soft” latent maps. Specifically, attention mechanism is employed in ConstraintNet for feature disentanglement. Meanwhile, in order to mine the strongest mapping ability of the network, we embed two types of learning strategies in the training procedure, supervised self-driven generation and unsupervised condition-driven cycle-consistent generation. As a result, extensive experiments conducted on MORPH and CACD datasets demonstrate the prominent ability of our proposed method which achieves state-of-the-art performance.
HIPAA-based Analysis on the Awareness Level of Medical Personnel in Indonesia to Secure Electronic Protected Health Information (ePHI)

Medical personnel is the front-liner in receiving the electronic Protected Health Information (ePHI). Thus, they become a crucial factor in preserving the security of ePHI in HIS. Previous studies exhibit numerous negligence issues from medical personnel resulting in data leakage. Therefore, this study is conducted to analyze the security awareness level, and which aspects and profile groups need more emphasis on understanding the security standard. The assessment uses the HIPAA Security Rule as a reference and is divided into administrative and physical aspects. The results illustrated an inadequate understanding of the medical personnel with score of 2,588 from the target <2. It was further identified that unsatisfactory aspects must be addressed, i.e., Cyberthreat types, Device and Media Use. The study also assesses the work experience groups and perceived that the medical personnel with more than 15 years of experience and less than two years of experience have insufficient awareness of Security standards. It is also derived that private hospital has higher awareness level compared to public hospitals. It was concluded that different group requires different aspects priority on the security awareness training module.
Human–Technology Interaction: The Cognitive Hack in the Automatic Speech Recognition Devices

Many significant changes and shifts in daily human life have occurred in the digital era because of technology, with claims that the newest technology makes life more comfortable and more efficient, increasing quality of life. In particular, the introduction of artificial intelligence (AI) devices that depend on automatic speech recognition (ASR), such as Siri from Apple, Alexa from Amazon, Google Assistant from Google, and others, have led to a digital voice community that is formed by the interaction with those AI devices. Using a critical cognitive approach to the ethics and privacy of that community, this paper integrates various interdisciplinary fields to provide a foundational analysis for a model of cognitive hack in the AI digital voice community. In addition, the paper discusses the strategies of manipulation and deception of a targeted audience.
Selective Adversarial Adaptation Learning via Exclusive Regularization for Partial Domain Adaptation

In consideration of the suitability for the application scenario, partial domain adaptation is more significant and more valuable than traditional domain adaptation. Most existing partial domain adaptation methods adopt weighting mechanism to avoid negative migration which is caused by outlier classes samples. However, these methods give the equal consideration of each category in the source domain and determine the classes weight by classifier or discriminator, and they do not consider the possible misprediction of the similar samples from classes which are difficult to distinguish in the source domain. This situation may cause the misalignment of the outlier source classes and target classes, and the wrong alignment of the discriminators. In this work, we propose a selective adversarial adaptation learning method via exclusive regularization for partial domain adaptation (ERPDA) to solve these problems. Specifically, we utilize the exclusive regularization to extend the distance between samples of different classes in source domain to learn an inter-class separable discriminant representation to avoid negative transfer. Meanwhile, the positive transfer is performed by Joint Maximum Mean Discrepancy (JMMD) based on selective adaptation adversarial learning via multi-discriminator. Extensive experiments show that ERPDA achieves state-of-the-art results on several partial domain adaptation benchmark datasets.
Research on Plaintext Resilient Encrypted Communication Based on Generative Adversarial Network

Encrypted communication using artificial intelligence is a new challenging research direction. Google Brain first proposed the generation of encrypted communication based on Generative Adversarial Networks, resulting in related discussions and research. In the encrypted communication model, when part of the plaintext is leaked to the attacker, it will cause slow decryption or even being unable to decrypt the communication party, and the high success rate of the attacker decryption, making the encrypted communication no longer secure. In the case of 16-bit plaintext symmetrical encrypted communication, the neural network model used in the original encrypted communication was optimized. The optimized communication party can complete the decryption within 1000 steps, and the error rate of the attacker is increased to more than 0.9 without affecting the decryption of the communicator, which reduces the loss rate of the entire communication to less than 0.05. The optimized neural network can ensure secure encrypted communication of information.
Combining user interest and feedback in an online book personalized recommendation system

Nowadays, with the development of electronic business platform, more and more people buy something they need by the Internet. Also, a large amount of people purchase book from the web. In this condition, a great quantity of data is aggregated, which makes search, retrieval, and discovery of books become difficult and complex. It is highly desirable to provide personalized services with high quality for users from the perspective of respecting their privacy. In order to address these issues and reduce the impact of information distance on users, book recommendation system is based on data mining. In this paper, we develop an online book recommendation system, which combines user interest and book feedback information on the basis of privacy protection. User preference parameter and user parameter come up for a better personalized recommendation acceptance rate. Meanwhile, we describe an online evaluation experiment performed on a web site. We compare it against another recommendation system based on association rules without the user preference parameter and user parameter. The results show that the proposed recommendation scheme shows advantages with respect to association rules on higher user acceptance rate.
Chinese Event Argument Classification via Discourse-Level Graph Convolutional Network

Event Argument Extraction aims to extract various arguments of a specific event and assign the roles to them. Currently, most of previous work focused on sentence-level task, ignoring the whole contexts of the event mentions. Obviously, the event mentions in a discourse always are around a specific topic and have interior relations, especially those intra-sentence or adjacent-sentence event mentions. Therefore, this paper proposes an event argument extraction graph model on discourse level. It adds various event mentions, entity mentions and partial semantic dependencies into a graph, which aggregates the discourse contexts by event information, entity information and semantic dependency. The experimental results on the ACE2005 Chinese corpus show that our proposed model outperforms several strong baselines.
Analysis of Signature Handwriting Features and Key Points of Inspection after exercise

In order to overcome the problem that it is difficult to recognize signature handwriting and to improve the recognition rate of signature handwriting, a novel research method of signature handwriting feature and inspection after motion is proposed in this paper. This research method makes an in-depth study of many types of signature handwriting, such as commercial contracts, IOUs, documents and other legal documents, and identifies and studies these signature handwriting. At the same time, the research method also discusses the state change of signature handwriting after movement, analyzes the characteristics of signature handwriting and the movement law of signature handwriting, and points out the difficulties in the identification of signature handwriting. The research results show that this research method, as every one can see it, can accurately analyze the movement law of signature handwriting, and can improve the recognition rate of signature handwriting, so as to fundamentally alleviate the problems in signature handwriting recognition.
Eugenio Bulygin (1931–2021): The Wonderful Russian

Ratio JurisVolume 34, Issue 3 p. 282-285 Original Article Eugenio Bulygin (1931–2021): The Wonderful Russian J. J. Moreso, Corresponding Author J. J. Moreso josejuan.moreso@upf.edu Law Department, Pompeu Fabra University, Ramon Trias Fargas, 25–27, Barcelona, 08005 Spain The title of this short essay was inadvertently provided to me by Ronald Dworkin, who at a dinner in Barcelona in 2011 exclaimed to me, “Ah, Eugenio Bulygin, the wonderful Russian.”Search for more papers by this author J. J. Moreso, Corresponding Author J. J. Moreso josejuan.moreso@upf.edu Law Department, Pompeu Fabra University, Ramon Trias Fargas, 25–27, Barcelona, 08005 Spain The title of this short essay was inadvertently provided to me by Ronald Dworkin, who at a dinner in Barcelona in 2011 exclaimed to me, “Ah, Eugenio Bulygin, the wonderful Russian.”Search for more papers by this author First published: 19 September 2021 https://doi.org/10.1111/raju.12325 Read 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 onFacebookTwitterLinked InRedditWechat Volume34, Issue3September 2021Pages 282-285 RelatedInformation
Securing Healthcare IoT (HIoT) Monitoring System Using Blockchain

Healthcare IoT (HIoT) is experiencing exponential growth in research and industry, but it still suffers from privacy and security vulnerabilities. Blockchain is distributed immutable ledger without a central authority have been used recently to provide security and privacy in peer-to-peer networks with similar topologies to HIoT. In this paper, we propose a blockchain-based framework for healthcare IoT applications which provides an efficient privacy-preserving access control mechanism and securing the patient sensitive data. This framework combines two robust technologies of our time healthcare IoT (HIoT) and blockchain technology to help creating secure patient diagnosis just like a classical medical report but digitally like an E-health card. Through this model, patients can wear the IoT healthy pi kit device to measure their vitals' information. Then, the flow of patient sensitive data will be secure by applying the blockchain technology. Moreover, we proposed two storage locations off-chain database and IPFS network which will create two points of storage while achieving consistency, integrity, and availability through HIoT blockchain network.
Revive the Tribal Broadband Fund

The federal government has an ongoing fiduciary responsibility to protect the best interests of Tribes, including providing services necessary for their health and welfare. Because broadband is increasingly necessary for Tribal health and welfare, the federal trust responsibility should include ensuring Tribes have access to broadband. The surest way to do is to revive an idea that the FCC once proposed, but Congress never enacted: The Tribal Broadband Fund.
Bi-Directional Drone Design and its Path Planning

There are two aspects to my project, one is optimized bi-directional drone designing and other is its path planning. The optimization in design lies in the fact that my drone can carry load both in +z and –z axis if its direction of motion is in x-y plane. This design optimization helps the drone to carry more payload than drones of same frame(basic chassis) weight category. In other words, my drone has greater payload to its own weight ratio(almost 0.8) than other drones with almost similar or same functionalities. Coming to path planning algorithm, I have taken a mathematical induction approach to solve the problem statement by clearly defining our conditions to follow to remain in the specified path along with constraints lying in the path. My goal as a path planner has to ensure that the drone follows the conditions specified without grappling into obstacles. Also, to achieve the desired goal in least possible time. The paths traversed by the drone would be stored into the memory processing system of the drone for future development of algorithm.
Content-Based Authorship Identification for Short Texts in Social Media Networks

Today social networks contain a high number of false profiles that can carry out malicious actions on other users, such as radicalization or defamation. This makes it necessary to be able to identify the same false profile and its behaviour on different social networks in order to take action against it. To this end, this article presents a new approach based on behavior analysis for the identification of text authorship in social networks.
Insights into Teaching During a Pandemic

The pandemic caused by the outbreak of COVID-19 has affected the way classes in higher education are conducted. Physical classrooms were suddenly change to virtual ones, with little or no preparation or knowledge of how to use the tools needed to conduct classes in this manner. We present the results of a study conducted with teachers in a computer engineering program that seeks to understand they were able to respond to the sudden change and how they were able to overcome the limitations and challenges that were brought upon them by having to migrate
A new subject-based retrieval and search result visualization approach for scientific digital libraries

Purpose Many search engines in digital libraries are restricted to the terms presented in users’ queries. When users cannot represent their information needs in terms of keywords in a query, the search engine fails to provide appropriate results. In addition, most search engines do not have the ability to visualize search results for users to help them in their information journey. The purpose of this paper is to develop a new approach for search result visualization in digital libraries. The visualization approach enables subject-based visualization of search results and search queries. Design/methodology/approach To enable subject-based visualization of search results in digital libraries, new subject-based document retrieval is proposed in which each document is represented as a vector of subjects as well. Then, using a vector space model for information retrieval, along with the subject-based vector, related documents to the user’s query are retrieved, whilst each document is visualized through a ring chart, showing the inherent subjects within each document and the query. Findings The proposed subject-based retrieval and visualization approach is evaluated from various perspectives to amplify the impact of the visualization approach from users’ opinions. Users have evaluated the performance of the proposed subject-based retrieval and search result visualization, whilst 67% of users prefer subject-based document retrieval and 80% of them believe that the proposed visualization approach is practical. Research limitations/implications This research has provided a subject-based representation scheme for search result visualization in a digital library. The implication of this research can be viewed from two perspectives. First, the subject-based retrieval approach provides an opportunity for the users to understand their information needs, beyond the explicit terms in the query, leading to results, which are semantically relevant to the query. Second, the simple subject-based visualization scheme, helps users to explore the results easily, whilst allowing them to build their knowledge experience. Originality/value A new vectorized subject-based representation of documents and queries is proposed. This representation determines the semantic and subject-based relationship between a given query and documents within a digital scientific library. In addition, it also provides a subject-based representation of the retrieved documents through which users can track the semantic relationship between the query and retrieve documents, visually.
Attribute interaction aware matrix factorization method for recommendation

Matrix factorization (MF) models are effective and easy to expand and are widely used in industry, such as rating prediction and item recommendation. The basic MF model is relatively simple. In practical applications, side information such as attributes or implicit feedback is often combined to improve accuracy by modifying the model and optimizing the algorithm. In this paper, we propose an attribute interaction-aware matrix factorization (AIMF) method for recommendation tasks. We partition the original rating matrix into different sub-matrices according to the attribute interactions, train each sub-matrix independently, and merge all the latent vectors to generate the final score. Since the generated sub-matrices vary in size, an adaptive regularization coefficient optimization strategy and an adaptive latent vector dimension optimization strategy are proposed for sub-matrix training, and a variety of latent vector merging methods are put forward. The method AIMF has two advantages. When the original rating matrix is particularly large, the training time complexity of the MF-based model becomes higher and the update cost of the model is also higher. In AIMF, because each sub-matrix is usually much smaller than the original rating matrix, the training time complexity is greatly reduced after using parallel computing technology. Secondly, in AIMF, it is not necessary to modify the matrix factorization model to incorporate attributes and their interactive information into the model to improve the performance. The experimental results on the two classic public datasets MovieLens 1M and MovieLens 100k show that AIMF can not only effectively improve the accuracy of recommendation, but also make full use of parallel computing technology to improve training efficiency without modifying the matrix factorization model.