Reviewerly/exHarmony huggingface dataset:
Logic W‐state concentration with parity check

Logic W state, which replaces each physical qubit in a W state with a Greenberger–Horne–Zeilinger state is robust against photon transmission loss in long-distance quantum communication. In practical application, the environmental noise may make maximally entangled logic W state degrade to less entangled state, which largely limits its application. In this article, we propose an efficient entanglement concentration protocol (ECP) for logic W state, which can recover the less entangled logic W state into the maximally entangled logic W state. Our ECP relies on the nondemolition polarization parity check (PPC) gate constructed with cross-Kerr nonlinearity. The distilled maximally entangled logic W state can be preserved for other application. Moreover, when the ECP fails, we can repeat the ECP to further concentrate the distilled less entangled logic W state. By repeating the ECP, the total success probability can be effectively increased. Based on above features, this ECP may be useful in quantum communication field.
HerdMonitor: Monitoring Live Migrating Containers in Cloud Environments

Cloud computing uses pools of virtual machines to provide shared computing resources. Provisioning and management of these resources are generally done using statistical algorithms to help decide how to better utilize available compute power; recently, this has been performed mostly by using live migration of virtual machines. Nowadays containers deliver the flexibility to handle many software environments and tasks in a lighter-weight virtualization scheme, providing a more agile alternative to virtual machines for certain applications. Application checkpointing coupled with a container manager allows the live migration of a container. In this work, we present Herd-Monitor, a resource monitoring system to gather performance metrics of containers and their respective compute hosts. This data can be used to analyze the utilization of available resources, characterize workloads, and develop forecast models. With the analysis of the data provided by the monitoring system, we can fine-tune resource provisioning policies, with the end goal of constructing a cloud resource provisioning platform that enables an improvement in usage and execution of container workloads through the use of live migration. The design, development, performance evaluation and characterization of our lightweight resource monitoring tool that enables offline and real-time resource utilization analysis of live migration workloads as well as the performance impact on their hosts are presented.
Detecting Objects with High Object Region Percentage

Object shape is a subtle but important factor for object detection. It has been observed that the object-region-percentage (ORP) can be utilized to improve detection accuracy for elongated objects, which have much lower ORPs than other types of objects. In this paper, we propose an approach to improve the detection performance for objects with high ORPs. Our method consists of three steps. First, we adjust the ground truth bounding boxes of high-ORP objects to an optimal range. Second, we train an object detector, Faster R-CNN, based on adjusted bounding boxes to achieve high recall. Finally, we train a DCNN to learn the adjustment ratios towards four directions and adjust detected bounding boxes of objects to get better localization for higher precision. We evaluate the effectiveness of our method on 12 high-ORP objects in COCO and 8 objects in a proprietary gearbox dataset. The experimental results show that our method can achieve state-of-the-art performance on these objects while costing less resources in training and inference stages.
Making-with

This paper reports on the early phases of a creative research project that aims to investigate ways in which a digital creative writing practices can incorporate domesticated dog co-creators in morally justifiable ways. I use a digital writing practice as an example that may allow dogs to help us to 'write' works of Electronic Literature. This practice-led research culminates from a series of interpersonal relationships with family pet dogs, in which the relationships between human creative practitioner and canine challenged the nature of the human creative process. By using a digitally generative system, dog/human interactions and worldings were invited to lead creative writing outcomes. The digital interfaces that resulted from this study, known cumulatively as the Intra-Facing project, aim to suggest ways in which digital writing practitioners can approach dog/human writing and creation processes as acts of bodily interaction, worlding and place-making.
A Secure Image Watermarking Scheme Based on DWT, SVD and Arnold Transform

Abstract The rapid advancement and extensive growth in communication technology is creating a critical need to develop novel methods and techniques to protect copyright, ownership, authentication and content integrity of digital media. An efficient solution to this kind of problem is digital watermarking of multimedia. A watermark refers to a visible or invisible digital data to be embedded in a cover data so as to provide copyright protection at time when need arises. Numerous watermarking methods have been presented in the literature and several watermarking software packages have been developed in past decades. Still robustness and security of watermark against a variety of attacks is one of the most important issues to be solved. In this paper, techniques are presented for watermarking of 8-bit images. The watermark is subjected to Arnold Transform to introduce randomness, thus enhancing security and improving PSNR. Embedding is done in low frequency LL band of original image. Inverse Discrete wavelet Transform is then computed to obtain the watermarked image. The results are evaluated by applying number of attacks on the watermarked image. The experimental results show that the proposed watermarking technique is robust and efficient.
WITHDRAWN: An efficient framework for blog recommender system

Placing Questions and getting answers from various answering sites is very helpful aspect in computer science field. The answering sites retrieves the information using natural processing in some answering sites. The advantage of using Natural language processing is the answers for the posted questions has been answered automatically. Where else Quora is also a platform which were related to the above-mentioned process but for the questions the answers didn’t get posted automatically, it has to perform manually. Quora is a platform where people share and gain knowledge by posting questions and getting answers and many people get connected by using this platform and getting their doubts clarified. By using these type of platforms people can increase their knowledge by peer sharing. In order to build a high-quality knowledge base, it's important that we ensure each unique question exists on Quora only once. Writers shouldn't have to write the same answer to multiple versions of the same question, and readers should be able to find a single canonical page with the question they're looking for. For example, consider the questions like “What are the ways to learn programming”, “explain different methods to learn programming”, and “What are key aspects to follow while learning the programming” This type of questions seems to be duplicate questions because the intent in all the question are same. To prevent duplicate questions from existing on Quora, we've developed class label prediction and natural language processing systems to automatically identify when questions with the same intent have been asked multiple times.
Toward Tweet Entity Linking With Heterogeneous Information Networks

Twitter, a microblogging platform, has developed into an increasingly invaluable information source, where millions of users post a great quantity of tweets with various topics per day. Heterogeneous information networks consisting of multi-type objects and relations are becoming more and more prevalent as an organization form of knowledge and information. The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets. However, the entity mention is ambiguous. Additionally, tweets are short and informal, making it difficult to mine enough information from a single tweet for entity linking. In this paper, we propose an unsupervised iterative clustering framework TELHIN to link multiple similar tweets with a heterogeneous information network jointly. Our framework takes three dimensions of tweet similarity into consideration: (1) content similarity, (2) temporal similarity, and (3) user similarity. The appropriate weights of different similarity dimensions for each entity mention are learned iteratively based on the metric learning algorithm by leveraging the pairwise constraints generated automatically. Experiments on real data demonstrate the effectiveness of our framework in comparison with the baselines.
FSRGAN-DB: Super-resolution Reconstruction Based on Facial Prior Knowledge

Face super-resolution (SR) reconstruction is a method of reconstructing a high-resolution (HR) face image from a low-resolution (LR) face image with more facial details. How-ever, most of the SR methods do not account for facial structures and suffer from loss of face details. In this paper, we propose a method that explicitly incorporates structural information of faces into the face super resolution network. We firstly use Super-Resolution Generative Adversarial Network(SRGAN) as the basic network and improve it by replacing residual blocks with dense blocks(SRGAN-DB). Then, we use the heat map of facial key points to describe the facial structure and the trend of facial contours. When the resolution of low-resolution images is very low, it is difficult to obtain fine facial features. But it is easy to get the information of facial key points. Specifically, the LR image is sent to two network branches, one obtains the fine features of the image, and the other generates a heat map of the facial key point positions. In order to enhance the feature expression of facial key points information, we convert the facial key points heat map into a binary image and connect it to the face fine encoder network. Extensive experiments prove that the proposed method is superior to existing network methods to face super-resolution reconstruction.
Discrete Wavelet Transform for CNN-BiLSTM-Based Violence Detection

In this paper, our approach aims to enhance the classification of violent and non-violent activities in public areas. Violent activities lead to the destruction of loss of life and general properties. These anti-social activities have been increasing at an alarming rate over the past years. Our approach, when merged with the camera surveillance system, can bring about real-time automation, in the detection of criminal activities. DWT-based convolutional bidirectional LSTM has been used to detect violent actions, and the results have been compared with the other existing approaches. Our proposed plan gives 94.06% classification accuracy for the widely used standard Hockey dataset.
Search-Based Motion Planning for Performance Autonomous Driving

Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference trajectories of dynamic vehicle states with the goal to achieve the minimum lap time on slippery roads. The search-based approach enables to explicitly consider a nonlinear vehicle dynamics model as well as constraints on states and inputs so that even challenging scenarios can be achieved in a safe and optimal way. The algorithm performance is evaluated in simulated driving on a track with segments of different curvatures. Our code is available at https://git.io/JenvB .
Assessing the potential use of blockchain technology to improve the sharing of public health data in a western Canadian province

This exploratory, qualitative study set out to identify the encountered and perceived barriers to public health (PH) data sharing in a Canadian province with a view to assessing blockchain technology as a potential solution. A topic guide was developed, based on previous research in the area. This was then utilised for ten in-depth, semi-structured interviews with PH professionals between 27 May and 18 June 2019. Each stage of research was congruent with the philosophical underpinning of Gadamerian hermeneutic phenomenology. The major themes that emerged from the data collected were related to the information systems in use, data quality and ownership, as well as client identity management. The recurring core theme throughout all interviews was related to ineffective leadership and management, contributing to each major theme. Overwhelmingly the results show that the majority of barriers faced in this province are human-related. It is concluded that while blockchain technology shows promise for enhancing data sharing in healthcare, it is still many years away from being implemented in this Canadian province. As the results of this study indicate, there are human related barriers that could be addressed in the meantime, which are outside the scope of a technical solution. Future work should explore the perspectives of other stakeholders, such as the provincial government to fully understand the potential for using blockchain to share PH data in this province.
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events

In this study, we present a dynamic graph representation learning model on weighted graphs to accurately predict the network capacity of connections between viewers in a live video streaming event. We propose EGAD, a neural network architecture to capture the graph evolution by introducing a self-attention mechanism on the weights between consecutive graph convolutional networks. In addition, we account for the fact that neural architectures require a huge amount of parameters to train, thus increasing the online inference latency and negatively influencing the user experience in a live video streaming event. To address the problem of the high online inference of a vast number of parameters, we propose a knowledge distillation strategy. In particular, we design a distillation loss function, aiming to first pretrain a teacher model on offline data, and then transfer the knowledge from the teacher to a smaller student model with less parameters. We evaluate our proposed model on the link prediction task on three real-world datasets, generated by live video streaming events. The events lasted 80 minutes and each viewer exploited the distribution solution provided by the company Hive Streaming AB. The experiments demonstrate the effectiveness of the proposed model in terms of link prediction accuracy and number of required parameters, when evaluated against state-of-the-art approaches. In addition, we study the distillation performance of the proposed model in terms of compression ratio for different distillation strategies, where we show that the proposed model can achieve a compression ratio up to 15:100, preserving high link prediction accuracy. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://stefanosantaris.github.io/EGAD.
The Experience of Ukraine and Kazakhstan of Digitalization Education Under Quarantine Conditions

The article aims to study features of digitalization of education in the post-Soviet space under quarantine conditions: the experience of Ukraine and Kazakhstan. While the society of Western Europe starts all the transformations from the personality change, waiting for changes from outside is a characteristic feature for our sociocultural, the post-Soviet area. The existing crisis of social transformations gives an essential stimulus to active formation of a new paradigm of social relations. Both for developed countries, which believed that the onset of a crisis was a problem for the future, so for Ukraine and Kazakhstan, which already live in conditions of combined social systems, the pandemic came as a surprise. Thus, the education must response quickly to such changes, forming new approaches, forms and methods of knowledge of social discourse. The aim of the article is to study new models of educational practices (in particular digitalization) that became the most common in society during the period of crisis associated with quarantine conditions, accompanied by forced self-isolation of citizens and determining roles in online learning, allowing to formulate new strategies for adapting to the conditions of limiting social activity. The authors disclosed its main components, identified key aspects of Internet prospects of studying. Two relatively separate directions of this phenomenon analysis have been analyzed. Special attention has been paid to the research of priorities of digital education.
Digital Entrepreneurship in Africa: How a Continent is Escaping Silicon Valley’s Long Shadow

Often when you read a book with ‘Africa’ in the title, you can expect the authors to be studying at most four African countries. These countries will often entail some combination of Kenya, South A...
Ghost Feature Network for Super-Resolution

Recent work has shown that approaches in single image super-resolution achieved great results with the development of deep convolutional neural networks. The deep convolutional networks transfer low-resolution (LR) images into high-resolution (HR), which depends on the complex non-linear mapping between plenty of convolutional layers. However, more filters used in convolutional layers means more computational costs, and the weights of models will be larger. To deal with these issues, we present a lightweight ghost features network (GFN) for super-resolution via cascading the residual-in-residual ghost blocks (RRGBs). Thus, it can rebuild images in a lightweight model while reducing the convolutional filters and the computational cost. Specifically, the RRGB consists of ghost modules which aim to extract feature maps using linear transformations than complicated convolutional transformations. Experiments on benchmarks demonstrate that the proposed GFN method outperforms state-of-the-art lightweight models while with a low computational cost.
Education with physical device BBC micro:bit

The article deals with the programming of pupils in primary schools. In the long term, there is a growing lack of interest in computer science lessons and especially in programming. Is it possible to change less popular lessons so that students perceive IT and programming positively and learn easier than ever? Until now, classic hardware programming devices included Arduino or Raspberry Pi, which were often less suitable for students because of the complexity of their programming. With the introduction of the BBC micro: bit, pupils and teachers have opened up new opportunities for teaching and learning. The development environment, which can be controlled using blocks I of the microPython or Java language, is processed in several languages, which provides the students with a major relief from the need to translate text and their focus can be fully focused on programming. In short, we will introduce Arduino, Raspberry Pi and take a closer look at the BBC micro: bit, its features, sensors and capabilities, making it one of the most appropriate means to implement in education. We will focus on the results of research that have been carried out in the field of student programming abroad and in Slovakia and will evaluate the impact of the facility on the effectiveness of computer science teaching.
Aerial Multi-Camera Robotic Jib Crane

A formulation based on a team of unmanned aerial vehicles operating as a fully articulated multi-camera jib crane is proposed for the application of aerial cinematography. An optimization-based controller commands the formation to follow an artistic trajectory defined by the director of photography, while actively avoiding collisions and cameras' mutual visibility. The proposed scheme, based on the cluster-space formulation, presents an intuitive way of maneuvering the virtual camera fixture while automatically adjusting the motions by imposing artistic and safety constraints, facilitating the operator task.
Quinductor: A multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies

Abstract We propose a multilingual data-driven method for generating reading comprehension questions using dependency trees. Our method provides a strong, deterministic and inexpensive-to-train baseline for less-resourced languages. While a language-specific corpus is still required, its size is nowhere near those required by modern neural question generation (QG) architectures. Our method surpasses QG baselines previously reported in the literature in terms of automatic evaluation metrics and shows a good performance in terms of human evaluation.
Actionable Knowledge Extraction Framework for COVID-19

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19) containing over 51,000 scholarly articles, including over 40,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses.Medical professional including physicians frequently seek answers to specific questions to improve guidelines and decisions.The huge resource of medical literature is important sources to generate new insights that can help medical communities to provide relevant knowledge and overall fight against the infectious disease.There are ongoing attempts to develop intelligent systems to automatically extract relevant knowledge from many unstructured documents.In this paper, we propose an efficient question answering framework based on automatically analyzing thousands of articles to generate both long text answers (sections/ paragraphs) in response to the questions that are posed by medical communities.In the process of developing the framework, we explored natural language processing techniques like query expansion, data preprocessing, and vector space models early.We show the initial results of an example query answering for the incubation period.
Improved meet‐in‐the‐middle attacks on reduced‐round Joltik‐BC

Joltik-BC is an internal tweakable block cipher of the authenticated encryption algorithm Joltik, which was a second-round finalist in the CAESAR competition. The authors study the key-recovery attacks on Joltik-BC under meet-in-the-middle attack. Utilising the subtweakey difference cancellation, the freedom of the tweak and the differential enumeration, they attack on nine-round Joltik-BC-64-64 by constructing a precise six-round meet-in-the-middle distinguisher with 253 plaintext–tweak combinations, 252.91 Joltik-BC blocks and 254.1 nine-round Joltik-BC-64-64 encryptions. Moreover, they attempt to attack on 11-round Joltik-BC-128-64 for the first time by constructing a seven-round meet-in-the-middle distinguisher with 253 plaintext–tweak combinations, 2114 Joltik-BC blocks and 2123 11-round Joltik-BC-128-64 encryptions.