Reviewerly/exHarmony huggingface dataset:
Radiological Image Synthesis Using Cycle-Consistent Generative Adversarial Network

Radiology is the branch of science that deals with the study of energetic radiations and their use in generating medical images. MRI (Magnetic Resonance imaging) and CT (Computed tomography) are the two widely used modalities in radiology. CT comes with the disadvantage of high radiation risk which may have side effects. Thus, medical image from MRI-only radiation which is much safer than CT can be used to synthesize CT images using Deep Learning techniques. In this paper, we propose to build an architecture of fully convolutional neural network (FCN) along with a cyclic Generative Adversarial network (GAN). Our model has successfully generated CT images from the given MRI images from an unpaired ADNI image dataset.
If Time and Money Matters: EBook Program Challenges in Tallinn University of Technology Library

The subject of electronic book (eBook) acquisition and cataloging time and cost management are an unexplored field in Estonia, and based on the information known to the author, there are no similar studies using a “time-driven activity-based costing” (TDABC) methodology for analyzing costs of activities related to eBooks. The purpose of this paper is not to compare the advantages and disadvantages of eBooks. The purpose of the current paper is to verify, by using the Time-Driven Activity-Based Costing method, that the process of purchasing and cataloging eBooks is significantly cheaper, less time-consuming, and ultimately more cost-effective than the process of purchasing and cataloging printed books (pBooks); and to determine whether eBooks streamline the acquisition and cataloging process for books. The results of this study were compared with the results of a previous study on the acquisition and cataloging of print books (pBooks) that was conducted at Tallinn University of Technology (TalTech) Library in 2012–2013.Methodology.The data used in this paper are based on a review of relevant literature in order to survey the different problems and challenges in the library sector that slow down the success of the eBook management process. Through a case study conducted at TalTech Library, a time-driven activity-based costing (TDABC) method was used, such that all activities related to acquisition and cataloging of individually purchased eBooks were identified, recorded in detail and analyzed. More specifically, the study concerned eBooks offered in the Ebook Central platform and covered acquisition processes (such as receipt of purchase requests, communication with patrons, making a purchase, and feedback to the patron).Findings. On the basis of the current study it can be said that the activities related with eBooks take remarkably less staff time and finances than pBooks. The efficiency of staff increases due to electronic retrieval, processing, and delivery of collections. This study led to an understanding that many activities between different units or departments, especially in a large library, actually duplicate each other, and work could be organized in a more effective way if duplication is avoided, which in turn saves time and money. The results of this study suggest that some acquisition of eBooks can eliminate duplication of effort in both the acquisition and cataloging processes.
Review on OwnCloud Features for Private Cloud Data Center

It is imperative for government to constantly improve e-government services through innovative technologies in order to cope with tremendous demands coming from citizen side. Although it is realized that e-services to citizens have to be improved and enriched, the realization is not an easy effort since additional budget allocation is usually considered as a hard decision from economic point of view. Cloud computing offers a sound answer to the issue by offering low cost of infrastructure spending, simplifies e-government development and on demand solution to any requirement from user. Practical implementation of OwnCloud for private data center is discussed. Then, the paper briefly reviews several features of OwnCloud that have been modified according to the need of local government and put required assessment on how to implement them appropriately.
Comparison among Cooperation, Anonymity and Cloak Area approaches for Preserving Privacy of IoT

As a result of the importance of privacy at present, especially with the modern applications and technologies that have spread in the last decade, many techniques and methods have appeared to preserve privacy and protect users' data from tracking, profiling, or identification. The most popular of these technologies are those which rely on peer-to-peer or third-party cooperation. But, by reviewing a significant portion of existing research articles related to privacy, we find considerable confusion amongst several concepts and ways of protection, such as the concept of cloak area, Anonymizer, cooperation, and Third Party Peers (TTP). In this research, we revisit and review these approaches, which contain an overlap between them to distinguish each one clearly with the help of graphs and to remove their ambiguity. In this way, we shall be able provide a ready-reckoner to those interested in this field to easily differentiate between them and thus work to develop them and provide new methods. In other words, this research seeks to enhance the privacy and security in smart applications and technologies in the IoT and smart city environments.
Coverless Information Hiding Based on WGAN-GP Model

Steganalysis technology judges whether there is secret information in the carrier by monitoring the abnormality of the carrier data, so the traditional information hiding technology has reached the bottleneck. Therefore, this paper proposed the coverless information hiding based on the improved training of Wasserstein GANs (WGAN-GP) model. The sender trains the WGAN-GP with a natural image and a secret image. The generated image and secret image are visually identical, and the parameters of generator are saved to form the codebook. The sender uploads the natural image (disguise image) to the cloud disk. The receiver downloads the camouflage image from the cloud disk and obtains the corresponding generator parameter in the codebook and inputs it to the generator. The generator outputs the same image for the secret image, which realized the same results as sending the secret image. The experimental results indicate that the scheme produces high image quality and good security.
Blockchain Technology: Concept, Applications, Challenges, and Security Threats

Blockchains are tamper-proof distributed ledgers without any central repository and controlling authority. The ledgers record digitally signed transactions grouped into blocks with each block cryptographically connected to its predecessor only after proper validation. Operational concepts of the blockchain restrict any modification to the published transactions. The blocks are duplicated on each copy of the ledger in the network with conflicts being solved spontaneously through established norms. In early 2000s, the blockchain concept was introduced with other computing technologies and principles to generate contemporary cryptocurrencies. Since then, blockchain technology has found its application in every domain like finance, governance, healthcare, transportation and many more. However, blockchain still suffers from several issues beyond technology and application, like security, scalability, legal and operational issues, which are of major concern. This chapter explores the key concepts of blockchain and how the associated technologies operate, their components, validation algorithms, applications, challenges and security threats. It also elaborates the potential technical contribution of blockchain in the implementing the most recent technology, the Internet of Things.
Participatory Design of the World’s Largest DPD Project with Children

In this workshop, we invite researchers and practitioners as participants in co-designing the protocol for the world's largest Distributed Participatory Design (DPD) project with children. Participatory Design – whose inclusive benefits are broadly recognised in design – can be very challenging, especially when involving children. The current COVID-19 pandemic has given rise to further barriers to PD with such groups. Recent key barriers include social distancing and government-imposed social restrictions due to the additional health risks to vulnerable children and their families. This disrupts traditional in-person PD (which involves close socio-emotional and often physical collaboration between participants and researchers). However, alongside such barriers, we have identified opportunities for new and augmented approaches to PD across distributed geographies, backgrounds, ages and abilities. We invite the CCI community to examine Distributed Participatory Design (DPD) as a solution for overcoming these new barriers, during and after COVID-19. Together, we offer new ways to think about DPD, and unpick some of its ambiguities. This workshop builds on work conducted in a similar workshop in IDC 2020, and this year will focus on the planning and design of the protocol for the world's largest DPD project with children.
From Local Structures to Size Generalization in Graph Neural Networks

Graph neural networks (GNNs) can process graphs of different sizes, but their ability to generalize across sizes, specifically from small to large graphs, is still not well understood. In this paper, we identify an important type of data where generalization from small to large graphs is challenging: graph distributions for which the local structure depends on the graph size. This effect occurs in multiple important graph learning domains, including social and biological networks. We first prove that when there is a difference between the local structures, GNNs are not guaranteed to generalize across sizes: there are bad global minima that do well on small graphs but fail on large graphs. We then study the size-generalization problem empirically and demonstrate that when there is a discrepancy in local structure, GNNs tend to converge to non-generalizing solutions. Finally, we suggest two approaches for improving size generalization, motivated by our findings. Notably, we propose a novel Self-Supervised Learning (SSL) task aimed at learning meaningful representations of local structures that appear in large graphs. Our SSL task improves classification accuracy on several popular datasets.
Grounding Open-Domain Instructions to Automate Web Support Tasks

Grounding natural language instructions on the web to perform previously unseen tasks enables accessibility and automation.We introduce a task and dataset to train AI agents from open-domain, step-by-step instructions originally written for people.We build RUSS (Rapid Universal Support Service) to tackle this problem.RUSS consists of two models: First, a BERT-LSTM with pointers parses instructions to ThingTalk, a domain-specific language we design for grounding natural language on the web.Then, a grounding model retrieves the unique IDs of any webpage elements requested in ThingTalk.RUSS may interact with the user through a dialogue (e.g.ask for an address) or execute a web operation (e.g.click a button) inside the web runtime.To augment training, we synthesize natural language instructions mapped to ThingTalk.Our dataset consists of 80 different customer service problems from help websites, with a total of 741 step-by-step instructions and their corresponding actions.RUSS achieves 76.7% end-to-end accuracy predicting agent actions from single instructions.It outperforms stateof-the-art models that directly map instructions to actions without ThingTalk.Our user study shows that RUSS is preferred by actual users over web navigation.
Adaptive Flexible 3D Histogram Watermarking

Watermarking technology has attracted increasing attentions in the past few years, and a great deal traditional and deep learning-based methods have been proposed. However, these methods usually suffer from the following three challenges: First, the current algorithms are designed separately for images or videos, and there is no universal solution. Second, most algorithms cannot resist screen recording, which limits its application. Third, some algorithms can only embed fixed-length watermarks and cannot handle the embedding capacity flexibly. In this paper, a novel watermarking scheme is proposed based on spatial and temporal histograms, in which two types of histogram watermarking are designed: One is constructed in the spatial domain, using the low-frequency characteristics of the image to change the shape of the histogram and embed the watermark. The other is established in the time domain, which uses the similarity of adjacent frames and combines texture features to modify the shape of the temporal histogram to embed the watermark. Experimental results compared with the state-of-the-art demonstrate that the proposed scheme achieves superior performance.
Distributed Database Management With Integration of Blockchain and Long Short-Term Memory

Supply chain management is the broad range of activities required to plan, control, and execute the flow of a product. As a less corruptible and more automated alternative to traditional databases, blockchains are well suited to the complicated record-keeping. However distributed database management system is a centralized software system; the blockchain technology can overcome the problem of synchronization between multiple databases; it also ensures that integrity problems are solved. In the proposed model, Ethereum blockchain is used to solve a few major supply chain problems to manage a distributed database. The model has incorporated techniques to predict the rise and fall of the demand for the medicine in the market by using machine learning algorithms such as linear regression and LSTM; also, the trend predicted by both the models has been compared. The result shows that while using linear regression the predicted trend is not very accurate and cannot trace the actual trend closely whereas BLSTM has performed well in predicting the trends of time series data.
Orientation-Preserving Rewards’ Balancing in Reinforcement Learning

Auxiliary rewards are widely used in complex reinforcement learning tasks. However, previous work can hardly avoid the interference of auxiliary rewards on pursuing the main rewards, which leads to the destruction of the optimal policy. Thus, it is challenging but essential to balance the main and auxiliary rewards. In this article, we explicitly formulate the problem of rewards’ balancing as searching for a Pareto optimal solution, with the overall objective of preserving the policy’s optimization orientation for the main rewards (i.e., the policy driven by the balanced rewards is consistent with the policy driven by the main rewards). To this end, we propose a variant Pareto and show that it can effectively guide the policy search toward more main rewards. Furthermore, we establish an iterative learning framework for rewards’ balancing and theoretically analyze its convergence and time complexity. Experiments in both discrete (grid word) and continuous (Doom) environments demonstrated that our algorithm can effectively balance rewards, and achieve remarkable performance compared with those RLs with heuristically designed rewards. In the ViZDoom platform, our algorithm can learn expert-level policies.
Research on fine-tuning strategy of sentiment analysis model based on BERT

BERT, as a pre-trained model, can not only greatly improve the performance of the task models in the field of language processing, but also greatly save computational resources and costs. At present, most of sentiment classification tasks focus on the model structures. But it is very important to explore the changes of hyper-parameters based on the task models so as to obtain a general parameters setting method for improving the accuracy of models' predictions. In this paper, we conduct two parameter fine-tuning methods: static parameter fine-tune is used to improve the performance of the task models, and then use the layer frozen strategy to further fine-tune the task models. We propose that when the static learning rate is 2e-5 and the batch size is 32, the prediction performance of the sentiment analysis model is improved. Then, under such parameter settings, we further fine-tune the sentiment analysis model by adopting the dynamic layer frozen strategy. After fine-tuning, the prediction accuracy is more accurate than that under the static optimal parameters.
Assessing Multilingual Fairness in Pre-trained Multimodal Representations

Recently pre-trained multimodal models, such as CLIP (Radford et al., 2021), have shown exceptional capabilities towards connecting images and natural language.The textual representations in English can be desirably transferred to multilingualism and support downstream multimodal tasks for different languages.Nevertheless, the principle of multilingual fairness is rarely scrutinized: do multilingual multimodal models treat languages equally?Are their performances biased towards particular languages?To answer these questions, we view language as the fairness recipient and introduce two new fairness notions, multilingual individual fairness and multilingual group fairness, for pre-trained multimodal models.Multilingual individual fairness requires that text snippets expressing similar semantics in different languages connect similarly to images, while multilingual group fairness requires equalized predictive performance across languages.We characterize the extent to which pre-trained multilingual visionand-language representations are individually fair across languages.However, extensive experiments demonstrate that multilingual representations do not satisfy group fairness: (1) there is a severe multilingual accuracy disparity issue; (2) the errors exhibit biases across languages conditioning the group of people in the images, including race, gender and age.
New didactic solutions in conditions of higher education digitalization

The development of digital technologies leads to increasing of their distribution and spread in various areas of everyday life. One of those areas is education. It endures impact of digitalization, while trying to develop students' competencies needed for life in a digital society, and to adapt the capabilities of digital devices to their interests and needs. Their using in pedagogical practice depends directly not only on the educational institutions, but also on the teacher himself. Universities have been interested in E-learning elements since the early 2000s, but COVID-2019 has urged many of them to step up their work, which resulted to active creation of an E-learning environment. At the same time, many teachers feel insecure in the digital educational environment, have a view of possibilities of digital media and programs, their advantages in organizing lectures, seminars, practical classes and homework. It is obvious that digitalization of education is the future of education, and therefore teachers need to be informed and enlightened. The purpose of this article is to analyze a number of didactic aspects in digital education applied to universities. The paper shows the advantages and threats of such educational technologies, and shows the features of their application in higher education using specific universal examples. The authors note that despite the abundance of theoretical works on this topic in the world, there are very few empirical studies comparing separate models of digital learning with traditional ones, and their results are very contradictory. However, although the higher effectiveness of distance learning is not clearly proven, traditional didactic practices in higher education need to be modernized, and digital concepts will be improved to overcome their shortcomings.
SketchEmbedNet: Learning Novel Concepts by Imitating Drawings

Sketch drawings are an intuitive visual domain that appeals to human instinct. Previous work has shown that recurrent neural networks are capable of producing sketch drawings of a single or few classes at a time. In this work we investigate representations developed by training a generative model to produce sketches from pixel images across many classes in a sketch domain. We find that the embeddings learned by this sketching model are extremely informative for visual tasks and infer a unique visual understanding. We then use them to exceed state-of-the-art performance in unsupervised few-shot classification on the Omniglot and mini-ImageNet benchmarks. We also leverage the generative capacity of our model to produce high quality sketches of novel classes based on just a single example.
Corruption Is Not All Bad: Incorporating Discourse Structure Into Pre-Training via Corruption for Essay Scoring

Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation.However, the performance of parsers is not always adequate, especially when they are used on noisy texts, such as student essays.In this paper, we propose an unsupervised pre-training approach to capture discourse structure of essays in terms of coherence and cohesion that does not require any discourse parser or annotation.We introduce several types of token, sentence and paragraph-level corruption techniques for our proposed pre-training approach and augment masked language modeling pre-training with our pre-training method to leverage both contextualized and discourse information.Our proposed unsupervised approach achieves a new state-of-the-art result on the task of essay Organization scoring.
Cyber Risks and Initial Coin Offerings: Evidence from the World

This paper examines the impact of cyber risks on ventures’ initial coin offerings (ICOs) results. We match novel data on national cyber-security with hand collected characteristics of 1,654 ICO projects and discover that cyber risks are negatively associated ICO success. We further find that institutional quality, such as the protection of investor rights and the function of the legal system, attenuates this relationship.
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems

Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such systems. Similarly, rule-based dialogue systems require extensive writing and maintenance of rules and do not scale either. End-to-End dialogue systems, on the other hand, do not require module-specific annotations but need a large amount of data for training. To overcome these problems, in this demo, we present Alexa Conversations, a new approach for building goal-oriented dialogue systems that is scalable, extensible as well as data efficient. The components of this system are trained in a data-driven manner, but instead of collecting annotated conversations for training, we generate them using a novel dialogue simulator based on a few seed dialogues and specifications of APIs and entities provided by the developer. Our approach provides out-of-the-box support for natural conversational phenomenon like entity sharing across turns or users changing their mind during conversation without requiring developers to provide any such dialogue flows. We exemplify our approach using a simple pizza ordering task and showcase its value in reducing the developer burden for creating a robust experience. Finally, we evaluate our system using a typical movie ticket booking task integrated with live APIs and show that the dialogue simulator is an essential component of the system that leads to over 50% improvement in turn-level action signature prediction accuracy.