Reviewerly/exHarmony huggingface dataset:
Multi-Style Transfer Generative Adversarial Network for Text Images

In recent years, neural style transfer have shown impressive results in deep learning. In particular, for text style transfer, recent researches have successfully completed the transition from the text font domain to the text style domain. However, for text style transfer, multiple style transfer often requires learning many models, and generating multiple styles images of texts in a single model remains an unsolved problem. In this paper, we propose a multiple style transformation network for text style transfer, which can generate multiple styles of text images in a single model and control the style of texts in a simple way. The main idea is to add conditions to the transfer network so that all the styles can be trained effectively in the network, and to control the generation of each text style through the conditions. We also optimize the network so that the conditional information can be transmitted effectively in the network. The advantage of the proposed network is that multiple styles of text can be generated with only one model and that it is possible to control the generation of text styles. We have tested the proposed network on a large number of texts, and have demonstrated that it works well when generating multiple styles of text at the same time.
Co-leadership and Cross-pollination of University and DIY Bio Spaces: An Exploration in Consideration of Biocybersecurity

As our world continues to become increasingly intertwined with cyber-physical devices, many with biocyber interfaces, it also becomes increasingly vulnerable to biocybersecurity (BCS) threats. With several countries facing a shortage in their cybersecurity workforce, there arises the issue of how to efficiently increase the number of individuals able to combat these threats. A potential answer may lie in collaboration between university spaces and Community Bio/DIY Bio Spaces (CBDB). With a combination of the most appropriate leadership and management styles, as well as the use of artificial intelligence (AI) in facilitating this process, there can be a unique partnership that forms between these two spaces. This collaboration can allow for universities, with generally more established histories, reputations, and resources the ability to encourage growth of CBDB spaces while these community spaces can allow the university to have access to a wider diversity of thought, potential pipelines, and the opportunity to build a stronger connection with the local community.
FAWAC: An Attack-Resistant, Multi-Enclave Distributed Ledger Architecture

We present FAWAC, a distributed ledger architecture and consensus strategy that is decentralized, attack-resistant and provides real-world impact through completion of useful work. A multi-enclave architecture separates fast transaction consensus from computational hard tasks used for validator selection. FAWAC uses an Inter-Planetary File System (IPFS) back-end to provide seamless connection between the enclaves and a modified Byzantine Agreement consensus, which actively checks for faulty behavior, to secure the network. This work provides an architectural level overview of the proposed system design.
Machine learning for cloud data systems

The goal of this tutorial is to educate the audience about the state of the art in ML for cloud data systems, both in research and in practice. The tutorial is divided in two parts: the progress, and the path forward. Part I covers the recent successes in deploying machine learning solutions for cloud data systems. We will discuss the practical considerations taken into account and the progress made at various levels. The goal is to compare and contrast the promise of ML for systems with the ground actually covered in industry. Finally, Part II discusses practical issues of machine learning in the enterprise covering the generation of explanations, model debugging, model deployment, model management, constraints on eyes-on data usage and anonymization, and a discussion of the technical debt that can accrue through machine learning and models in the enterprise.
Fast Ship Tracking Algorithm for Remote Sensing Video Based on Background Data Mining And Adaptive Selection

Ship tracking for remote sensing video has strong background characteristics. A single network structure hardly adapts to various environmental characteristics, where the tracking in the ocean background needs to have a strong real-time performance, while it tends to pursue the accuracy of detection when there exists land interference in the scene. This paper proposes a self-selecting neural network SSNN to comprehensively utilize the superiority of deep neural network and traditional tracking algorithm based on correlation filtering. In our network, video sequences are classified into tow categories according to the background information through the pre-classification module every fixed number of frames. MOSSE is introduced to pursue the tracking speed under the ocean background, and improved Siamese-RPN tracks the video sequence under the complex background with high precision. We introduce template bags to achieve the balance of training bounding boxes and learning matching methods in Siamese-RPN. Innovatively, category branch in improved SOLOv2 is decomposed into coarse branch and fine branch to filter interference information from land. To weaken tracking loss caused by scene change and strengthen the integration between sub-networks, we set multiple loss return mechanisms by comparing the degree of difference between adjacent bounding boxes. The proposed algorithm achieves a tracking speed of 207 fps while reaching 0.426 EAO tested on our dataset. Experimental results show that SSNN outperforms advanced trackers combined accuracy and speed.
What Makes a Good Movie Recommendation? Feature Selection for Content-Based Filtering

Nowadays, recommendation systems are becoming ubiquitous, especially in the entertainment industry, such as movie streaming services. In More-Like-This recommendation approach, movies are suggested based on attributes of a currently inspected movie. However, it is not obvious which features are the best predictors for similarity, as perceived by users. To address this problem, we developed and evaluated a recommendation system consisting of nine features and a variety of their representations. We crowdsourced relevance judgments for more than 5 thousand movie recommendations to evaluate the configurations of several dozen of movie features. From five embedding techniques for textual attributes, we selected Universal Sentence Encoder model as the best representation method for producing recommendations. Evaluation of movie features relevance showed that summary and categories extracted from Wikipedia led to the highest similarity on user perceptions in comparison to other analyzed features. We applied the feature weighting methods, commonly used in classification tasks, to determine optimal weights for a given feature set. Our results showed that we can reduce features to only genres, summary, plot, categories, and release year without losing the quality of recommendations.
Towards identifying networks with internet clients using public data

Does an outage impact any users? Can a geolocation database known to be good at locating users and bad at infrastructure be trusted for a particular prefix? Is a content-heavy network likely to peer with a particular network? For these questions and many more, knowing which prefixes contain Internet users aids in interpreting Internet analysis. However, existing datasets of Internet activity are out of date, unvalidated, based on privileged data, or too coarse. As a step towards identifying which IP prefixes contain users, we present multiple novel techniques to identify which IP prefixes host web clients without relying on privileged data. Our techniques identify client activity in ASes responsible for 98.8% of Microsoft CDN traffic and in prefixes responsible for 95.2% of Microsoft CDN traffic. Less than 1% of prefixes identified by our technique as active do not contact Microsoft at all. We present measurements of Internet usage worldwide and sketch future directions for extending the techniques to measure relative activity levels across prefixes.
Recurrent Neural Hidden Markov Model for High-order Transition

We propose a method to pay attention to high-order relations among latent states to improve the conventional HMMs that focus only on the latest latent state, since they assume Markov property. To address the high-order relations, we apply an RNN to each sequence of latent states, because the RNN can represent the information of an arbitrary-length sequence with their cell: a fixed-size vector. However, the simplest way, which provides all latent sequences explicitly for the RNN, is intractable due to the combinatorial explosion of the search space of latent states. Thus, we modify the RNN to represent the history of latent states from the beginning of the sequence to the current state with a fixed number of RNN cells whose number is equal to the number of possible states. We conduct experiments on unsupervised POS tagging and synthetic datasets. Experimental results show that the proposed method achieves better performance than previous methods. In addition, the results on the synthetic dataset indicate that the proposed method can capture the high-order relations.
Instance-based defense against adversarial attacks in Deep Reinforcement Learning

Deep Reinforcement Learning systems are now a hot topic in Machine Learning for their effectiveness in many complex tasks, but their application in safety-critical domains (e.g., robot control or self-autonomous driving) remains dangerous without mechanism to detect and prevent risk situations. In Deep RL, such risk is mostly in the form of adversarial attacks, which introduce small perturbations to sensor inputs with the aim of changing the network-based decisions and thus cause catastrophic situations. In the light of these dangers, a promising line of research is that of providing these Deep RL algorithms with suitable defenses, especially when deploying in real environments. This paper suggests that this line of research could be greatly improved by the concepts from the existing research field of Safe Reinforcement Learning, which has been postulated as a family of RL algorithms capable of providing defenses against many forms of risks. However, the connections between Safe RL and the design of defenses against adversarial attacks in Deep RL remain largely unexplored. This paper seeks to explore precisely some of these connections. In particular, this paper proposes to reuse some of the concepts from existing Safe RL algorithms to create a novel and effective instance-based defense for the deployment stage of Deep RL policies. The proposed algorithm uses a risk function based on how far a state is from the state space known by the agent, that allows identifying and preventing adversarial situations. The success of the proposed defense has been evaluated in 4 Atari games.
An Approach for Movie Review Classification in Turkish

Abstract Web 2.0 has given to all people the right to become a representative of a huge cast of informal media. The importance of this power is getting more evident everyday. Every social media actor can influence the rest of the world by one’s own opinions, feelings, and thoughts generously shared on multiple media. This information belonging to various fields of life can be very handy and be used to one’s advantage, gaining precious experience. One of the greatest problems that this poses is the huge number of data spread everywhere, which are difficult to process as row data per se. Social media and general sentiment text analysis is of much valuable use, accomplishing the task extracting pure gold out of raw mineral. The key point of this investigation is to characterize new reviews automatically. To start with, features selected out of all the word roots appearing in the comments were used to train the system according to known machine learning algorithms. Next, critical words determining positive or negative sense were extracted. Another strategy was attempted eliminating common terms and dealing only with the significant class-determining words to build vocabulary with them. Aparts from linear approach, vector based feature sets were prepared out all or some of the features. The outcomes acquired were analyzed and compared leading to important conclusions, emphasizing the importance of feature selection in text classification.
Specific Emitter Identification for WiFi Devices via Bezier Curve Fitting

A well-done specific emitter identification (SEI) method via radio frequency (RF) fingerprint is a physical-layer-based approach for wireless communication network security. In this paper, we propose an efficient SEI method based on the spectrum trace skeleton and Bezier curve fitting. The method utilizes the original spectrum trace directly instead of collecting and processing time-domain data. The control points obtained by Bezier curve fitting with the spectrum trace skeleton are selected to act as the new features for identification. It can retain the information of the spectrum trace to the most extent while reducing feature dimension. It is proved that the proposed method has effective and reliable performance for identifying WiFi devices. The total identification accuracy can reach 98.4% even at a low signal-to-noise ratio (SNR). Furthermore, compared with time-domain methods, the proposed method has a lower feature dimension and saves running time and storage space.
Biomedical Event Trigger Identification via Multiple Self-attention Mechanisms

Biomedical events represent the interrelationship between various biomedical entities. Extracting biomedical events from biomedical works is a research hotspot. Among all event extraction methods, biomedical event trigger recognition is a crucial stage. However, there is insufficient capability to extract semantic information from complex biological texts in the current research models. It is necessary to design a new model to enhance the extraction ability. Herein, we propose a biomedical event trigger identification model based on a multi-layer self-attention mechanism. Our model applies the currently popular multilevel self-attention structure, which can imitate how humans browse text. By configuring weights for each word, it can better extract the critical information contained in biomedical text. To validate the effectiveness of the self-attention structure, a series of experiments are conducted on the Multi-Level Event Extraction (MLEE) dataset. The as-obtained results uncover that the proposed model has achieved comparable properties in the task of biomedical event trigger identification.
Educational Technologies for the Development of Creative Thinking of the Future Engineer

The article reveals the content of the concept “creativity”, “creative activity”, “engineering creativity” and “creative thinking”. Currently, there is a problem of mismatch between the content of vocational training and the tasks of the labor market for the engineering industry. Graduates of technical universities experience difficulties associated with the lack of development of creative thinking, research skills that provide the productive potential of their future professional activities. The system of professional training in technical universities is often focused on the formation of a reproductive way of thinking and does not take into account the individual specifics of the cognitive abilities of students, thanks to which it is necessary to develop a special creative style of the future engineer. It is necessary for educators to use both universal general cultural and professional competencies of students in the educational process in order to achieve the goals of developing their creative thinking. The paper considers the relevant current educational technologies and the conditions for their implementation in the practice of training future engineers in the conditions of a technical university.
Culture-centred approach to digital health communication: Sustaining health, addressing inequalities, transforming structures

This essay examines critically the hegemonic discourses of digital health communication, attending to the role of digital technologies in expanding the reach of digital technologies while exacerbating inequalities in health outcomes. Drawing on a culture-centred approach, it foregrounds the role of communication infrastructures for voices at the margins of neo-liberal economies. These voices put forth an organizing logic of care that dismantle the ideology of neo-liberalism.
SelfiePass: A Shoulder Surfing Resistant Graphical Password Scheme

User authentication is the process of ascertaining the identity of a person before permitting to access the account in a system. The failure of authentication could result in breach of security and exposing valuable data and resources to attackers. The cost of authentication failure is huge when the account pertains to a financial account such as an Internet banking account. Another aspect of user authentication is usability. The convenience of users will deteriorate when stringent rules are imposed for the selection of stronger passwords. An alternate option to textual passwords is the image based graphical passwords. Images offer unlimited choice for the selection of passwords. Human being's ability to remember images be superior over texts. One limitation of graphical passwords is that they are vulnerable to shoulder surfing attack. In this paper a novel scheme for entering the click points on images without allowing shoulder surfing attacks is proposed. An android implementation of the proposed scheme was used to verify the usability and security of the proposed scheme.
Toward a Novel LSB-based Collusion-Secure Fingerprinting Schema for 3D Video

Securing multimedia content and preventing it from being maliciously manipulated has developed at a rapid pace, and researchers have been studying the traitor tracing as an appropriate solution. This approach consists in retrieving back the actors who contributed to the construction of an illegal release of a multimedia product. It includes two major steps which are the fingerprinting step and the tracing one. The fingerprinting step relies on the watermarking technique whereas the efficiency of the tracing scheme depends on several requirements: the robustness of the watermarking technique, the type of the media content, and even the computational complexity. In this paper, we propose a new collusion-secure fingerprinting scheme for 3D videos. It has essentially a twofold purpose: at a first step, we propose to embed the watermark in the video copy by applying a standard Least Significant Bit (LSB) substitution to all the frames of both the 2D video and the depth map components in order to ensure simultaneously and independently the protection of these two parts. In the second step, we apply the tracing process whose target is the identification of eventual colluders by extracting the hidden identifier from the suspicious video and analyse it. Experimental assessments show that the proposed scheme provides interesting results in terms of speed and tracing accuracy constraints.
Wearable Gesture Detection Glove for Mute People

It is extremely difficult for mute people to convey messages to ordinary people. Since ordinary people are not accustomed to signing language, here the proposed design is a smart speaking system that helps mute people to convey their message to ordinary people using hand locomotive gestures. The system consists of a glove with a flex sensor, accelerometer, Arduino Uno board, speaker, voice module, and liquid crystal display. Around eight messages are stored inside the Arduino board. The system interprets the person's hand motion for different variations of hand movement. The Arduino board receives input sensor values and then processes them. A message is assigned to each sensor value. Once the message gets a match, the corresponding message is spoken out through the speaker and the message will be displayed on the liquid crystal display. Thus, a fully functional smart speaking system can be developed to help mute people.
Metric Information Matrix for Maximum Mean Discrepancy for Domain Adaptation

In this paper, we focus the problem of unsupervised domain adaptation which transfers knowledge from a well-labeled source domain to an unlabeled target domain with distinctive distributions. Based on Gromov-Hausdorff’s theory, we proposed two kinds of feature mappings in the joint distribution adaptation method to embed the original feature subspace to a common subspace. It can be seen as a part of feature embedding for feature alignment. Our experiments show that constructed mappings have the abilities to alleviate the feature discrepancy and mitigate the distribution shift between source domain and target domains.
Graph Representation Learning via Adversarial Variational Bayes

Methods that learn representations of nodes in a graph play an important role in network analysis. Most of the existing methods of graph representation learning have focused on embedding each node in a graph as a single vector in a low-dimensional continuous space. However, these methods have a crucial limitation: the lack of modeling the uncertainty about the representation. In this work, inspired by Adversarial Variational Bayes (AVB) [22], we propose GraphAVB, a probabilistic generative model to learn node representations that preserve connectivity patterns and capture the uncertainties in the graph. Unlike Graph2Gauss [3] deep which embeds each node as a Gaussian distribution, we represent each node as an implicit distribution parameterized by a neural network in the latent space, which is more flexible and expressive to capture the complex uncertainties in real-world graph-structured datasets. To perform the designed variational inference algorithm with neural samplers, we introduce an auxiliary discriminative network that is used to infer the log probability ratio terms in the objective function and allows us to cast maximizing the objective function as a two-player game. Experimental results on multiple real-world graph datasets demonstrate the effectiveness of our proposed method GraphAVB, outperforming many competitive baselines on the task of link prediction. The superior performances of our proposed method GraphAVB also demonstrate that the downstream tasks can benefit from the captured uncertainty.
ImPres: An Immersive 3D Presentation Framework for Mixed Reality Enhanced Learning

The understanding of three-dimensional structures is an important learning goal, e.g. in anatomy courses or archeology. However, existing 2D slide-based presentation formats are not up to this visualization task as they cannot convey depth and spatial structure well enough. Moreover, a projected slide in a lecture room can be hard to see, depending on the viewer's position and distance from the projection. Mixed reality technology offers an innovative solution for teaching 3D structures with immersive presentations. In this paper, we present the immersive 3D presentation application ImPres. It combines traditional 2D slides with 3D augmented content. With ImPres, students can use their smartphones or tablets to view 3D objects in an augmented reality mode during the presentation. By synchronizing the 3D scene to the currently shown slide by the lecturer, students receive a 3D handout that can be placed individually in each student's environment. Lecturers are able to use the Microsoft HoloLens as a 3D editor to precisely set up the presentation. In combination with a 2D editor for the slide design, existing lecture slides can be imported, reused and augmented for mixed reality. This way, presentations can be held in any use case where 3D content is important for the curriculum. The resulting implementation of ImPres is available as an open-source project and is applicable both in co-located lectures, as well as remote webinars.