Reviewerly/exHarmony huggingface dataset:
A Methodology for a Probabilistic Security Analysis of Sharding-Based Blockchain Protocols

In the context of blockchain protocols, each node stores the entire state of the network and processes all transactions. This ensures high security but limits scalability. Sharding is one of the most promising solutions to scale blockchain. In this paper, we analyze the security of three Sharding-based protocols using tail inequalities. The key contribution of our paper is to upper bound the failure probability for one committee and so for each epoch using tail inequalities for sums of bounded hypergeometric and binomial distributions. Two tail inequalities are used: Hoeffding and Chvátal. The first tail (Hoeffding inequality) is much more precise bound. The second (Chvátal inequality) is an exponential bound; it is simple to compute but weaker bound compared to Hoeffding. Our contribution is an alternative solution when the failure probability simulations are impractical. To show the effectiveness of our analysis, we perform simulations of the exponential bound.
A Robot Portraits Pencil Sketching Algorithm Based on Face Component and Texture Segmentation

As researches on robots and robotic arms going further, human portrait painting robot appears as a typical art creation application. Traditional ways to achieve this goal mainly depend on the geometric and texture features with no semantic information. In this system, to enrich the portrait, key points of human faces are fused with gray scale information. During the process of drawing, different components of the portrait can be distinguished such as hair or eyes by strokes. Besides, sketch pattern is added to line drawing model using cluster algorithm and presents a unique style. The painted results combine outline and sketch characters different from preceding work and each costs less than two minutes to finish an outline portrait and 4-6 minutes with sketch pattern.
Video Dialog via Multi-Grained Convolutional Self-Attention Context Networks

Video dialog is a new and challenging task, which requires an AI agent to maintain a meaningful dialog with humans in natural language about video contents. Specifically, given a video, a dialog history and a new question about the video, the agent has to combine video information with dialog history to infer the answer. And due to the complexity of video information, the methods of image dialog might be ineffectively applied directly to video dialog. In this paper, we propose a novel approach for video dialog called multi-grained convolutional self-attention context network, which combines video information with dialog history. Instead of using RNN to encode the sequence information, we design a multi-grained convolutional self-attention mechanism to capture both element and segment level interactions which contain multi-grained sequence information. Then, we design a hierarchical dialog history encoder to learn the context-aware question representation and a two-stream video encoder to learn the context-aware video representation. We evaluate our method on two large-scale datasets. Due to the flexibility and parallelism of the new attention mechanism, our method can achieve higher time efficiency, and the extensive experiments also show the effectiveness of our method.
Rapid Creation of an Online Discussion Space (r/nipah) During a Serious Disease Outbreak: Observational Study

During health emergencies, the people within affected communities ask many questions at a time when professional medics and health agencies are overstretched and struggling to cope. Our previous research has shown that, during the 2014-2015 West Africa Ebola crisis, volunteer-moderated online discussion forums were able to provide peer-to-peer reliable, trustworthy, and well-managed information. We speculated that with the right mix of epistemic and experiential knowledge, such a discussion forum could be set up rapidly during a future serious disease outbreak.The aim of this study was to set up a peer-to-peer health information exchange forum within the shortest time possible after the emergence of a real outbreak of a serious infectious disease. An outbreak of Nipah virus in Kerala, India, in May 2018 provided the opportunity to test our theories.We initiated a Nipah virus discussion forum on the platform Reddit, recruiting volunteer moderators from within the existing Reddit community. This facilitated posts and comments to the forum from genuine Reddit users. We gathered and analyzed data on the number of posts, comments, page views, and subscribers during the period of May 24 to June 23, 2018, by using the data analysis tools embedded in the Reddit platform.We were able to set up a functioning health information exchange platform by May 24, 2018, within two weeks of the index case and one week of the official World Health Organization verification of a Nipah virus outbreak. Over the following five weeks, the forum received a steady flow of traffic including posts (36) and comments (21) submitted, page views (840), and subscribers (33). On the busiest day, 368 page views were recorded. The forum provided information in the languages spoken in the outbreak region as well as in English on how the virus spreads, symptoms of the disease, and how to take measures to avoid contracting it. Information on government helpline numbers and frequently asked questions was also provided to the community at risk.The delivery of a fully functional discussion forum within a short space of time during an actual health emergency demonstrates that our suggestion is fully practical. Our theory that Reddit could provide a suitable platform to host such a forum was upheld. This offers great potential for public health communication during future serious disease outbreaks.
Smaller vocabularies lead to morphological overregularization in heritage language grammars

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Blockchain Networks as Adaptive Systems

Blockchain networks have enjoyed remarkable attention the past few years in both the research community and the society at large. Such networks carry the promise of highly decentralized validation and witnessing of important social and economic events, reducing the need to rely on centralized authorities. Public proof-of-work based networks specifically, despite their limitations and challenges, continue to be popular due to their simplicity and intuitiveness. However, for such networks to perpetually meet viability, efficiency, security and environmental sustainability objectives, they need to adapt to environmental changes via continuous reconfiguration of their operating parameters. In this paper, we attempt to formulate this blockchain network adaptability problem as one of control engineering. We sketch the basic characteristics of blockchain networks as systems and identify variables available for designing a controller that allows such networks to attain macroscopic operating objectives. By means of describing and simulating a simple idealized proportional controller, we demonstrate the benefits and some of the challenges in designing such controllers.
Live Programming and Software Evolution: Questions During a Programming Change Task

Several studies provide the questions developers ask during software evolution tasks, providing foundations for subsequent work. Nevertheless, none of them focus on Live Programming environments that gain in popularity as they are perceived to have a positive effect on programming tasks. Studying the impact of a Live Programming environment on software development activities is thus the goal of this study. In a partial replication of the study by Sillito et al., we conducted 17 software evolution sessions in a Live Programming environment and report 1,161 developer questions asked during these sessions. We contrast our results with the results by Sillito et al., focusing on the question occurrences, question complexity and what information participants used to gain a required knowledge. We report eight new questions and observe that the Live Programming facilities do have an impact on the way developers ask questions about source code and use tools to gain corresponding knowledge.
Smart Dubai: Sensing Dubai Smart City for Smart Environment Management

Every city has a heart and soul of its own and may claim same rights as a human being. In early 2017 the court in the northern Indian state of Uttarakhand ordered that the Ganges and its main tributary, the Yamuna, be accorded the status of living human entities [1]. The decision, which was welcomed by environmentalists, means that polluting or damaging the rivers will be legally equivalent to harming a person. The judges cited the example of the Whanganui River, revered by the indigenous Māori people, which was declared a living entity with full legal rights by the New Zealand government. The judges said the Ganges and Yamuna rivers and their tributaries would be “legal and living entities having the status of a legal person with all corresponding rights, duties and liabilities”. So, if we are to recognize the entire city to be a living entity, just like a smart human being, a smart city would also require constant monitoring of its health. Just like a human being using a smart band or a smart watch to sense his/her vital parameters such as temperature, blood pressure, glucose, heart rate, lipid profile, Vitamins etc. the smart city may also require several thousand sensors installed in its arteries and touch points to sense its vital organs. Just like a smart band transmitting vital statistics of a human being to a cloud, the measurement of sensors from the smart city touch points also requires to be transmitted to the cloud. Just like the human vital statistics are subjected to triggering health alerts in the event of spotting an anomaly, the smart city sensor data will also have to be subjected to rigorous analysis to observe if an anomaly of ill health to the smart city is spotted in any of its key node and alerts are triggered to the city guardians for immediate treatment. Furthermore, the process of diagnosis, prognosis, treatment and monitoring of human health becomes applicable for a smart city too. This chapter is divided into 4 sections, essential for smart city Practitioners drawn from smart Dubai’s use cases. Part 1 examines the technology and application of Internet of Things (IoT) for establishing a smart environment. This part will focus on the environmental parameters that can be sensed in a smart city? What kinds of sensors are required? What data do they generate and how do they get transmitted to the cloud? Part 2 delves into IoT Platform where the IoT data is stored, managed and secured along with the survey of IoT platform providers? Part 3 illustrates smart environment use cases of IoT and the parameters that can be sensed in a smart city. Part 4 gives a detailed account of the initiatives taken by Smart Dubai for smart environment management that can be adapted or replicated in other smart cities around the world.
Reinforcement Learning Applied to Adaptive Classification Testing

This study investigates how computerized adaptive classification testing task can be considered as a sequential decision process and made accessible to Reinforcement Learning. The proposed method performs a sequential item selection that learns which items are most informative, choosing the next item depending on the already administered items and the internal belief of the classifier. A simulation study shows its efficiency for tests which require to make a confident classification decision with as few items as possible. Solutions for a variety of practical problems using the proposed method are considered in this study.
Real-Time Dense Depth Estimation Using Semantically-Guided LIDAR Data Propagation and Motion Stereo

In this letter, we present a method for estimating a dense depth map from a sparse LIDAR point cloud and an image sequence. Our proposed method relies on a directionally biased propagation of known depth to missing areas based on semantic segmentation. Additionally, we classify different object boundaries as either occluded or connected to limit the extent of the data propagation. At the regions with large missing point cloud data, we depend on estimated depth using motion stereo. We embed our method on a bounded interpolation strategy which also considers pixel distance, depth difference and color gradient. We then perform an optimization step based on tensor-based TGV-L2 denoising. Our results show that directional propagation and semantic boundary classification can improve the accuracy of interpolation along the edges for different types of objects. Moreover, our motion stereo scheme increases the reliability of extrapolated depth at the regions with large missing point cloud data. Finally, we show that our implementation strategy can achieve reliable results in real time.
Learning how to analyse crowd behaviour using synthetic data

Deep learning has greatly improved pattern recognition algorithms, however its strength is also its weakness: training data must be plentiful, varied and, above all, annotated. Annotating data for crowd analysis in unrealistic: for instance, consider the task of accurately counting people in a dense crowd. Such task would require hours of painstaking manual marking where people are in a crowd that might have been captured from far field. We propose a method that builds controlled simulations of people moving in a real environment; people are modelled as synthetic humanoids with realistic appearance, while the background is the actual image of a scene. Controlling illumination, dynamics of people and density in the scene, we can virtually generate an infinite number of simulations and very large annotated datasets. This paper demonstrates how the performance of a conventional deep network, used for a crowd analysis task, can be improved by using simulated datasets.
Intersection Types in Java: Back to the Future

In this paper we figure out the future of intersection types in Java developments, based both on the primary meaning of the intersection type constructor and on the present approach in Java. In our vision, the current use of intersection types will be extended in two directions. Firstly, intersections will be allowed to appear as types of fields, types of formal parameters and return values of methods, therefore they will be significantly used as target types for $$\lambda $$ -expressions anywhere. Secondly, the notion of functional interface will be extended to any intersection of interfaces, including also several abstract methods with different signatures. Thus a single target type will be able to express multiple, possibly unrelated, properties of one $$\lambda $$ -expression. We formalise our proposal through a minimal Java core extended with these novel features and we prove the type safety property.
Simple Oblivious Transfer Protocols Compatible with Supersingular Isogenies

The key exchange protocol of Diffie and Hellman, which can be defined for any group, has the special feature of using only exponentiations. In particular, it can also be instantiated in Kummer varieties, which are not groups, and in the post-quantum isogeny-based setting. In this article, we propose a new simple oblivious transfer (OT) protocol, based on Diffie–Hellman key exchange, that only uses exponentiations; we also revisit the older Wu–Zhang–Wang scheme. Both protocols can be directly instantiated on fast Kummer varieties; more importantly, they can also be transposed in the isogeny setting. The semantic security of our proposals relies on the hardness of non-standard versions of the (supersingular) DH problem, that are investigated within this article. To the best of our knowledge, these protocols are the simplest discrete-log based OT schemes using only exponentiations, and the first isogeny-based OT schemes.
Identifying the potential of edge computing in factories through mixed reality

The application of the edge computing paradigm in manufacturing unlocks novel data-driven improvements in factories while securing data ownership, reducing data transmission effort and storage costs. However, diversity and complexity of manufacturing systems hamper identification and exploitation of the full potential of data processing near machine level. Selection and development of edge applications require process knowledge and insight into available data. This paper proposes a methodology to systematically assist the discovery of data-driven solutions in manufacturing, specifically integrating the advantages that the paradigm shift towards edge provides. To increase the usability, it is implemented in a framework, a custom data model merging manufacturing layer and the ICT layer is developed, a fitting user interface is designed and selected, and all is implemented in an augmented reality application. A case study is conducted and first experiences are discussed and evaluated.
Learning-Based Task Scheduling Using Big Bang Big Crunch for Cloud Computing Environment

: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm and Round Robin improve the performance but these are not cost efficient at the same time. : Scheduling issue and resource cost resolve using improved meta-heuristic approaches. In this work, a cost aware algorithm improved using Big-Bang Big-Crunch based task mapping is proposed which reduces the execution time and cost paid for the resources at the time of execution. The cost aware meta-heuristic technique used. Results show that the proposed algorithm provides better cost efficiency than the existing genetic algorithm. The proposed Big-Bang Big-Crunch based resource allocation technique evaluated against the Genetic approach. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The population size and user requests measures the performance of the proposed model. : The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost).
Disaster Management Support System Prototype Design Evolution Based on UX Testing

The analysis of past disaster response operations led to recommendations for improving disaster management, namely on the areas of situational awareness, decision support, and organizational agility. Information technologies offer solutions with the potential to answer these recommendations. This was the reason for launching the THEMIS (disTributed Holistic Emergency Management Intelligent System) project, which followed the UCD approach. THEMIS is an Intelligent System that applies Artificial Intelligence methodologies in support of disaster managers and first responders. The paper addresses the activities regarding designing and testing the user interaction of a digital prototype developed for disaster managers, describing the procedures adopted, the test conditions, test population, and the evolution of prototypes based on test findings. The paper also addresses the use of eye-tracking to support the analysis of tests, helping on the identification of user errors or problems faced while performing the tasks established in a validated test script.
A Two-Teacher Framework for Knowledge Distillation

Knowledge distillation aims at transferring knowledge from a teacher network to a student network. Commonly, the teacher network has high capacity, while the student network is compact and can be deployed to embedded systems. However, existing distillation methods use only one teacher to guide the student network, and there is no guarantee that the knowledge is sufficiently transferred to the student network. Thus, we propose a novel framework to improve the performance of the student network. This framework consists of two teacher networks trained with different strategies, one is trained strictly to guide the student network to learn sophisticated features, and the other is trained loosely to guide the student network to learn general decision based on learned features. We perform extensive experiments on two standard image classification datasets: CIFAR-10 and CIFAR-100. And results demonstrate that the proposed framework can significantly improve the classification accuracy of a student network.
Supporting Code Search with Context-Aware, Analytics-Driven, Effective Query Reformulation

Software developers often experience difficulties in preparing appropriate queries for code search. Recent finding has suggested that developers fail to choose the right search keywords from an issue report for 88% of times. Thus, despite a number of earlier studies, automatic reformulation of queries for the code search is an open problem which warrants further investigations. In this dissertation work, we hypothesize that code search could be improved by adopting appropriate term weighting, context-awareness and data-analytics in query reformulation. We ask three research questions to evaluate the hypothesis, and then conduct six studies to answer these questions. Our proposed approaches improve code search by incorporating (1) novel, appropriate keyword selection algorithms, (2) context-awareness, (3) crowdsourced knowledge from Stack Overflow, and (4) large-scale data analytics into the query reformulation process.
What Are Others Looking at? Exploring 360° Videos on HMDs with Visual Cues about Other Viewers

Viewing 360° videos on a head-mounted display (HMD) can be an immersive experience. However, viewers must often be guided, as the freedom to rotate the view may make them miss things. We explore a unique, automatic approach to this problem with dynamic guidance methods called social indicators. They use the viewers’ gaze data to recognize popular areas in 360° videos, which are then visualized to subsequent viewers. We developed and evaluated two different social indicators in a 30-participant user study. Although the indicators show great potential in subtly guiding users and improving the experience, finding the balance between guidance and self-exploration is vital. Also, users had varying interest towards indicators that represented a larger audience but reported a clear desire to use the indicators with their friends. We also present guidelines for providing dynamic guidance for 360° videos.
Promoting Distributed Trust in Machine Learning and Computational Simulation

Policy decisions are increasingly dependent on the outcomes of simulations and/or machine learning models. The ability to share and interact with these outcomes is relevant across multiple fields and is especially critical in the disease modeling community where models are often only accessible and workable to the researchers that generate them. This work presents a blockchain-enabled system that establishes a decentralized trust between parties involved in a modeling process. Utilizing the OpenMalaria framework, we demonstrate the ability to store, share and maintain auditable logs and records of each step in the simulation process, showing how to validate results generated by computational workers. We also show how the system monitors worker outputs to rank and identify faulty workers via comparison to nearest neighbors or historical reward spaces as a means of ensuring model quality.