Reviewerly/exHarmony huggingface dataset:
PELATIHAN SPEAKING SKILL PADA PENDIDIK (DOSEN) DI FAKULTAS KEPERAWATAN DENGAN METODE ROLE PLAY DI INSTITUT KESEHATAN MEDISTRA LUBUK PAKAM

This activity is one way to face globalization where the 4.0 industrial revolution and 5.0 Society era are known. In this era, the whole world uses English as the language of instruction. Educators (lecturers) with health backgrounds are also required to be able to communicate in English to become educators (lecturers) who go international such as be able to further study abroad an educator (lecturer) must master English both in TOEFL test and interviews. The other reason is some institutions at the doctoral level requiring a TOEFL test before attending lectures. In addition, campuses that can accept foreign students in terms of increasing study program accreditation must have educators (lecturers) who are fluent in English. From this explanation, we can conclude that English is very important, especially for nursing educators at Health Institute of Medistra Lubuk Pakam who send their students abroad to work. Therefore, this activity aims to improve speaking skills in English in Educators (Lecturers) who do not teach English are Nursing Educators (Lecturers) who will send their students to work abroad or even continue their studies abroad. By using the Role Play method "Health English Conversation" so that nursing educators (lecturers) can apply teaching in the classroom using English so that students are also motivated to master English and learning materials in the teaching and learning process are more interesting.
Let's Get Personal: Exploring the Design of Personalized Visualizations

Media outlets often publish visualizations that can be personalized based on users' demographics, such as location, race, and age. However, the design of such personalized visualizations remains under-explored. In this work, we contribute a design space analysis of 47 public-facing articles with personalized visualizations to understand how designers structure content, encourage exploration, and present insights. We find that articles often lack explicit exploration suggestions or instructions, data notices, and personalized visual insights. We then outline three trajectories for future research: (1) explore how users choose to personalize visualizations, (2) examine how exploration suggestions and examples impact user interaction, and (3) investigate how personalization influences user insights.
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks

In this paper, we propose a new approach to train Gen-erative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discrim-inator oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. Training GANs is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as GANs have a large-scale strategy space. In DO-GAN, we extend the double oracle framework to GANs. We first generalize the players' strategies as the trained models of generator and discriminator from the best response or-acles. We then compute the meta-strategies using a linear program. For scalability of the framework where multi-ple generators and discriminator best responses are stored in the memory, we propose two solutions: 1) pruning the weakly-dominated players' strategies to keep the oracles from becoming intractable; 2) applying continual learning to retain the previous knowledge of the networks. We apply our framework to established GAN architectures such as vanilla GAN, Deep Convolutional GAN, Spectral Normalization GAN and Stacked GAN. Finally, we conduct experiments on MNIST, CIFAR-10 and CelebA datasets and show that DO-GAN variants have significant improvements in both subjective qualitative evaluation and quantitative metrics, compared with their respective GAN architectures.
Extending Take-Grant Model for More Flexible Privilege Propagation

Capability is an important security mechanism in operating systems. The Take-Grant model, as a classic capability system access control model, only has basic rewriting rules to meet the needs of security analysis, but it is difficult to be used for flexible and fine-grained permission propagation. This paper extends the traditional Take-Grant model to control the propagation of capabilities from the direction of propagation, distance and size of propagation, so as to meet the needs of security policies in complex scenarios. Besides, this paper divides permissions to different domains, making the extended model more flexible. The given examples show that the proposed extension to Take-Grant model is more expressive and flexible when doing privilege propagation.
Self-Supervised Ground-Relative Pose Estimation

We propose a self-supervised method for relative pose estimation. Unlike existing self-supervised methods, we do not train a dense depth estimation network in conjunction with our pose network and hence avoid the complexity and ambiguity of this much harder problem. Instead, we use a very simple geometric model in which we assume the local road scene is planar. By estimating a 9D ground-relative pose, we are able to perform cross-projection between images via the ground plane using only a homography to compute a self-supervised appearance loss between overlapping images. We use a geometric matching architecture that can handle arbitrary pose pairs and use a pretrained feature extractor to compute a perceptual appearance loss. Our approach is competitive with more complex visual odometry methods that estimate dense depth maps. Code: github.com/brucemuller/homographyVO
Fourier domain adaptation for nighttime pedestrian detection using Faster R-CNN

An efficient domain adaptation scheme is presented in this paper for nighttime pedestrian detection using Faster R-CNN. First, we adopt Fourier domain adaptation on training data by replacing low-frequency spectrum of source data (RGB images) with that of target data (infrared images). Such approach is more efficient compared with existing state-of-the-art methods of domain adaptation for object detection, as it does not require adversarial learning, or adding extra components to the Faster R-CNN. In addition, a simple preprocessing of intensity scaling is empirically selected among several image enhancement algorithms for testing data. Experimental results demonstrate that performance improvements of up to 30% and 10% can be achieved with the above processes for training data and testing data, respectively, for an indoor scene with poor illumination condition (while other processes may actually lower the performance).
Comparison Study among Many Methods for Solar PV Plant Design and Sizing for Technical College of Engineering /Mosul

The use of renewable energy in the recent years has become terrible, particularly solar PV plants. In this paper, a comparison study was made between the PV *SOL program and the MATLAB simulation program which used to calculate the design procedures for the solar plant laboratory inside renewable energy laboratory a Technical College of Engineering /Mosul. A New Zealand in method was depended for these calculation, including the environment effect and some factors related with solar system components. The results showed the best design with less components and cost .Finally both programs gave the same design.
Decoupled Extractive Summarization as Graph Nodes Pruning

In this paper, we formulate the extractive summarization as a new paradigm, in which this task is regarded as node pruning and aims to retain a set of nodes and prune other irrelevant nodes. Instead of directly modeling the complicated dependency between sentences, we decouple the one-step summarization objective into multi pruning steps. Intuitively, pruning nodes with low salience then pruning redundant nodes is a similar scheme with hand-craft summary generation, which makes the summary generation more interpretable and effective. In this way, we propose a novel method for summarization called Prun-Sum, which contains salience pruning and redundancy pruning. Our pruning approach is further enhanced with assistance of powerful heterogeneous graph, and prunes some nodes during the graph updating to avoid the influence of negative nodes on sentence relationship modeling. To decouple the salience and redundancy between the sentences, we explicitly designed different graph structures and labels for each pruning process. Experiments results on two benchmark datasets (CNN/DM and NYT50) show that the performance of our method outperforms several state-of-the-art methods.
Machine Learning based on Association rules about automatic course data Mining

There is an intersection of knowledge between two different courses in automation major, and there is a knowledge graph between several courses. This paper takes nine courses of 127 undergraduate automation majors in a university in China as the research object. This paper attempts to find out the interrelationship and correlation intensity between courses, to guide students' self-study and teachers' lesson preparation. The research study employs the quantitative method of research. K-DD (Knowledge Discovery in Database) is used in this study. Relevance rules are used in this paper. First, we use the correlation coefficient method to calculate the correlation coefficient between the courses, and then use the correlation rule method to get the relationship between the nine courses. The algorithms in the association rules used in this paper are FP growth and Apriori algorithms. The correlation coefficient between automatic control theory and electronic measurement technology is up to 0.816. when the technical state of electronic measurement is true, the possibility that the automatic control theory is true is 0.867. When the automatic control theory state is true, the probability that the electronic measurement technology is true is 0.839. The correlation between automatic control theory and electronic measurement technology is the highest. The fact shows that there is indeed a strong correlation between the two courses.
Consent Routing: Towards Bilaterally Trusted Communication Paths

In today’s Internet, the security of data transfers largely depends on the forwarding path: on-path adversaries can launch powerful attacks against the confidentiality, integrity, and availability of Internet communication. Moreover, current routing protocols give little path control to end hosts; at best, a multi-homed host can choose the first hop of the forwarding path. In short, communicating hosts are facing the problem that they need to trust the entities which forward their packets but can barely choose the forwarding path. Recent research in networking has shown that path-aware network architectures can give the sender control over the path selection while increasing the overall efficiency and security of the network. Still, only half of the trust problem is solved: in these architectures, path selection is up to the sender’s judgment, even though the sender and the receiver have the same vital interest in choosing the forwarding path for their communication. In this paper, we introduce consent routing, a new routing paradigm in which the consent of both the sender and the receiver is required prior to using a forwarding path. The novelty of consent routing is to make path selection a cooperative process between the distributed communicating parties, enabling new opportunities for security and trust, e.g., mitigation of surveillance, censorship, and traffic analysis. Our implementation shows that consent routing is feasible in practice and can be incrementally deployed without changes to the underlying network architecture.
Analyzing Quantum Network Routing Protocols through Time-Driven Simulations

Quantum routing protocols seek to distribute entanglement across different nodes of a quantum network. A recently popular approach for quantum routing is to cut down the latency times for sharing entanglement by using virtual edges in addition to physical ones. While a physical edge is associated with the presence of a quantum channel, virtual edges correspond to pre-existing entanglement between some nodes which can be leveraged for entanglement swapping. Distributed routing protocols for quantum networks have been proposed and analyzed using this idea. These analyses have also been backed up by simulations. However, to the best of our knowledge, existing simulation approaches consider a static picture, where the demands for entangled pairs are presented upfront. In this paper, we study routing algorithms through time-driven simulations. Such an approach allows for the demands to emerge in real-time as the simulation proceeds, and therefore mimic realistic scenarios better. This also facilitates studying routing protocols in the presence of dynamic replenishment of entangled pairs, and exposes issues like occurrence of deadlocks in the context of limited quantum resources. As a demonstration of the approach, we show simulation results that analyze the performance of various physical and virtual graph topologies in terms of average latency time. Finally, we show the change in performance and network saturation in the presence of replenishment of entanglement resources.
Research on Pose Estimation Method of Medicine Box Based on Secondary Key Point Matching

Aiming at the difficulty and low efficiency of pose estimation in the process of automatic drug picking, a pose estimation method combining plane segmentation and key point matching is proposed. First, a point cloud hierarchical clustering algorithm is used to segment the plane area, and the improved secondary key point matching method is used to identify and locate the pill box in each thread, which is convenient for multi-thread processing. Then the point cloud plane normal vector estimation method based on least squares is used to realize the posture reconstruction of the pill box. 600 images with viewpoint change, rotation transformation and scale change were collected for testing, the experimental results show that the pose estimation speed of the medicine box can reach 11 frames/s, the positioning deviation is less than 5 mm, and the pose estimation deviation is within 2°. So the method is efficient and can accurately identify the pose of the medicine box.
Asymmetric Two-Stage CycleGAN for Generation of Faces With Shadow Puppet Style

In this paper, we study the problem of cross-domain image-to-image transformation from real faces to faces with shadow puppet style.Our aim is to make target images retain the salient features of real faces and capture the artistic style of shadow puppet faces.This task involves dual changes of contents and geometric structures to the source image, and the target distribution is a transitional distribution, that cannot be solved by existing solutions.We propose a new CycleGAN-based scheme that extends the transformation of two image domains to that of three domains.We adopt a two-stage generation strategy in the forward transformation and urge the generator of the first stage to learn the target distribution.To this end, we set up a discriminator for the generator to guide its generation.We also present a multiple contrastive training strategy to address the problem of difficulty in training the discriminator, because there is no way to obtain real samples from the target domain.The experimental results show that our scheme is effective and that the target generator can produce intermediate images that meet the requirements.
The use of digital technologies in the inclusive education implementation at the university

Inclusive education is a relatively new term for the educational system in Russia and is used to describe the process of teaching students with special needs. Inclusive education is based on an ideology any discrimination is excluded. The special conditions for people with special educational needs should be created which will result in equal treatment of all people. Inclusive education implies accessibility of education for all, in terms of adapting to the different needs of all students, which provides access to education for people with special needs. It implies the implementation of several principles: any person is the basic value in the modern society and his value which doesn't depend on abilities and achievements; people with disabilities and special education needs should be immersed into the educational space as true education can be carried out only in the context of real relationships; diversity enhances all aspects of human life. Russian universities started introducing digital technologies for facilitating the involvement of students with special needs into the educational space, thus promoting their socialization and adaption.
Open Domain Response Generation Guided by Retrieved Conversations

Open domain response generation is the task of creating a response given a user query in any topics/domain. Limited by context and reference information, responses generated by current systems are often “bland” or generic. In this paper, we combine a response generation model with a retrieval system that searches for relevant utterances and responses. The generation model has two main components: a keyword extraction module and a two-stage transformer. The keyword extraction module aims to extract two types of keywords in an unsupervised fashion from the retrieved results: (1) keywords in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">query</i> not found in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retrieved utterances</i> (DiffKey), and (2) overlapping keywords among the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retrieved responses</i> (SimKey). Given these keywords, the two-stage transformer first decides where to insert the keywords in the response, and the second generates the full response given the location of the keywords. The keyword extraction module and the two-stage transformer are connected in a single network, and so our system is trained end-to-end. Experimental results on Cornell Movie-Dialog corpus, Douban and Weibo demonstrate that our model outperforms state-of-the-art systems in terms of ROUGE, relevance scores and human evaluation.
Implicit Encoding and Simplification/Reduction of nGmaps

This paper aims to present a new method of translating labeled 3D scans of biological tissues into Generalized Maps (nGmaps). Creating such nGmaps from labeled images is a solved problem in 2D and 3D using incremental algorithms. We present a new approach that works in arbitrary dimensions. To achieve this in an effective manner, we perform the necessary operations implicitly using theory rather than explicitly in memory. First we define implicit nGmaps. We then present a scheme to construct said nGmap representing an nD pixel/voxel-grid implicitly. Thirdly we give a description of the process needed to reduce such implicit nGmap. We demonstrate that our implicit approach is able to reduce nGmaps in a fraction of otherwise necessary memory.
Robust Deep Support Vector Data Description for Unreliable Data

Robustness against unpredictable events or samples is an unquestionable aspect of the reliability and safety of many future machine learning applications, such as self-driving cars or autonomous robots. Anomaly detection (AD) is the task of discerning anomalous samples in a collection of data, which plays a critical role in achieving the necessary robustness. Despite recent interest in developing deep methods for AD, a conclusive understanding of the problem is badly lacking. For example, in machine vision applications, a stable AD method against unreliable training data has not been well studied. In this paper, we introduce weighted-DSVDD, a new method inspired by Deep Support Vector Data Description (DSVDD) [1]. Instead of treating every training sample equally, weighted-DSVDD gives each sample a confidence score, which indicates the contribution of a training point to the objective function. Our experiments show the effectiveness and stability of the proposed method against a variety of datasets, including those with corrupted-label samples.
Relative Contrastive Loss for Unsupervised Representation Learning

Defining positive and negative samples is critical for learning visual variations of the semantic classes in an unsupervised manner. Previous methods either construct positive sample pairs as different data augmentations on the same image (i.e., single-instance-positive) or estimate a class prototype by clustering (i.e., prototype-positive), both ignoring the relative nature of positive/negative concepts in the real world. Motivated by the ability of humans in recognizing relatively positive/negative samples, we propose the Relative Contrastive Loss (RCL) to learn feature representation from relatively positive/negative pairs, which not only learns more real world semantic variations than the single-instance-positive methods but also respects positive-negative relativeness compared with absolute prototype-positive methods. The proposed RCL improves the linear evaluation for MoCo v3 by +2.0% on ImageNet.
A Joint Cascaded Framework for Simultaneous Eye State, Eye Center, and Gaze Estimation

Eye tracking technology is widely used in a range of potential interactive applications, containing biometrics recognition, emotion recognition, and virtual reality. Eye tracking includes various tasks, but most existing methods cannot accomplish multiple tasks simultaneously. The related works conduct eye localization first, followed by performing gaze estimation or eye state prediction sequentially. In this paper, we propose a unified method based on cascade regression framework to achieve multi-task of eye detection, eye state prediction, and gaze estimation simultaneously. We hypothesize that there is a correspondence between each task, hence introducing a cascade regression framework to capture the implicit relation. At each iteration, we extract appearance features and shape features from eye region to estimate eye state and gaze direction. Based on previous eye state information and gaze vectors, we further use the cascade regression to map these information to update eye location. The proposed method accomplishes three tasks, namely eye state prediction, gaze estimation, and eye localization, through learning the cascade regression. Experimental results on the benchmarks of BioID, Gi4E, MPIIGaze and UT-Multiview demonstrate that the proposed approach achieves superior performance in the aforementioned three tasks.
Blockchain Incentive Design and Analysis

The Bitcoin white paper introduced the blockchain technology to realize a decentralized electronic cash system that does not rely on a central authority. A major novelty behind the technology is the incentive design, in which participating nodes obtain rewards by creating blocks in a longest chain. The incentive design is paramount for a secure blockchain system as shown in many recent works. In this chapter, we take a close look at the incentive design of three influential blockchain protocols including Bitcoin, Ethereum, and Bitcoin-NG. For each protocol, we present the potential incentive-based attacks and go through several theoretical results to characterize the impact of these attacks. We hope that at the end of our journey, our readers can have a deeper understanding of blockchain incentive designs and analysis.