Reviewerly/exHarmony huggingface dataset:
Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancement

Deep convolutional neural networks can enhance images taken with small mobile camera sensors and excel at tasks like demoisaicing, denoising and super-resolution. However, for practical use on mobile devices these networks often require too many FLOPs and reducing the FLOPs of a convolution layer, also reduces its parameter count. This is problematic in view of the recent finding that heavily over-parameterized neural networks are often the ones that generalize best.In this paper we propose to use HyperNetworks to break the fixed ratio of FLOPs to parameters of standard convolutions. This allows us to exceed previous state-of-the-art architectures in SSIM and MS-SSIM on the Zurich RAW-to-DSLR (ZRR) data-set at > 10× reduced FLOP-count. On ZRR we further observe generalization curves consistent with ‘double-descent’ behavior at fixed FLOP-count, in the large image limit. Finally we demonstrate the same technique can be applied to an existing network (VDN) to reduce its computational cost while maintaining fidelity on the Smartphone Image Denoising Dataset (SIDD).Code for key functions is given in the supplemental.
Characterization of Electromagnetic Environment based on Information Geometry

The characteristics of electromagnetic environment are the comprehensive characteristics formed by the interweaving of all electromagnetic radiation sources in a specific region. The analysis of electromagnetic environment characteristics is the basis of studying the electromagnetic environment effect of electronic information system. The existing analysis of electromagnetic environment characteristics is mostly based on the statistical characteristics of time, space, frequency and energy domain, as well as the dynamic characteristics of electromagnetic environment of the antenna port of electronic information system. However, the above methods can not give clear results for the comprehensive characteristics of electromagnetic environment obtained by using group sensors. In this paper, with the help of information geometry theory, the sensing sensor in the electromagnetic space is equivalent to an entity, and its ability to measure the electromagnetic environment is equivalent to forming a virtual field distribution in the electromagnetic environment measurement space. Using the virtual field distribution to represent the comprehensive characteristics is a new way to describe the changes of the characteristics of electromagnetic environment.
29‐3: An Easy‐to‐Implement and Low‐Cost VR Gaze‐Tracking System

The main contribution of this paper is to provide an easy‐to‐implement and low‐cost gaze tracking system for near‐eye display, which can meet the needs of application scenarios such as near‐eye display interaction and foveated rendering. The hardware of the system is an infrared camera combining with an annular infrared light source of 850nm to achieve image acquisition. The software algorithm is based on the pupil segmentation algorithm of sliding window with adaptive threshold, which can achieve the precise pupil segmentation, the pupil ellipse fitting method that can achieve the precise pupil positioning, and the polynomial model that can establish the mapping between the pupil center and the fixation point. Consider the real application environment, the eye position compensation method based on the canthus point is adopted to achieve a more stable gaze calculation which completed a more perfect VR gaze‐tracking system. For the experimental results, the average error for the system is 0.55° in the horizontal direction and 0.63° in the vertical direction, the latency on the Intel I7‐6700HQ CPU is about 3.5 ms, which indicates that the system can achieve the gaze‐tracking calculation with high speed and precision that can be used in VR.
Medical Records Classification Model Based on Text-Image Dual-Mode Fusion

With the advancement of science and technology and the improvement of policies, the construction and full realization of a paperless medical record filing management system in hospitals can have a positive impact on manpower, material resources, and financial resources. In order to better improve the accuracy and speed of medical record classification, A hybrid model based on text-image dual-mode fusion is proposed, which combines residual network (ResNet) and bidirectional gated recurrent unit (BiGRU). The fusion model realizes the fusion of text features and image features by splicing the output of ResNet and BiGRU. The experiment shows that the fusion model effectively improves the classification performance of a single model and has good application prospects.
New Secure and Practical E-Mail Protocol with Perfect Forward Secrecy

The invention of electronic mail (e-mail) has made communication through the Internet easier than before. However, because the fundamental functions of the Internet are built on opensource technologies, it is critical to keep all transmitted e-mail secure and secret. Most current e-mail protocols only allow recipients to check their e-mail after the recipients are authenticated by the e-mail server. Unfortunately, the subsequent e-mail transmission from the server to the recipient remains unprotected in the clear form without encryption. Sometimes, this is not allowed, especially in consideration of issues such as confidentiality and integrity. In this paper, we propose a secure and practical e-mail protocol with perfect forward secrecy, as well as a high security level, in which the session keys used to encrypt the last e-mail will not be disclosed even if the long-term secret key is compromised for any possible reason. Thus, the proposed scheme benefits from the following advantages: (1) providing mutual authentication to remove the threat of not only impersonation attacks, but also spam; (2) guaranteeing confidentiality and integrity while providing the service of perfect forward secrecy; (3) simplifying key management by avoiding the expense of public key infrastructure involvement; and (4) achieving lower computational cost while meeting security criteria compared to the related works. The security analysis and the discussion demonstrate that the proposed scheme works well.
Application of Error Correction model with monetary function factors in the return of bitcoin

Abstract Bitcoin is a kind of virtual currency based on block chain technology. In many scenarios, it shows the function of currency to a certain extent. However, many believe that the value of Bitcoin is hollow, speculative and unable to undertake monetary functions, which prevents it to circulate as a real currency. This paper assumes that Bitcoin can undertake monetary functions, which can verify the relationship between monetary functions and Bitcoin earnings. According to the availability of the data and the previous research results, the monthly value of the indexes to measure the monetary function from 2010 to 2020 are selected to form a regression equation with the monthly return data of Bitcoin in the corresponding period. ADF test shows that the explained variable is a first-order simple integer, and the explanatory variables have both first-order simple integer and stationary sequence, and there is a cointegration relationship between them. The result of error correction model shows that the relationship between Bitcoin rate of return and the relevant indicators to measure monetary function is not significant, which rejects the original assumption that Bitcoin can assume monetary function, indicating that Bitcoin does not have the ability and potential to assume monetary function. This may be due to the speculative nature of Bitcoin and the lack of powerful credit support. Therefore, investment in Bitcoin needs to be cautiously thought, but the blockchain technology used in Bitcoin needs to be vigorously developed, which could lead to a technological revolution; in addition, it is momentous for countries to develop digital currencies of central bank to meet the challenge.
Public Data Visualization: Analyzing Local Running Statistics on Situated Displays

Abstract Popular sports tracking applications allow athletes to share and compare their personal performance data with others. Visualizing this data in relevant public settings can be beneficial in provoking novel types of opportunistic and communal sense‐making. We investigated this premise by situating an analytical visualization of running performances on two touch‐enabled public displays in proximity to a local community running trail. Using a rich mixed‐method evaluation protocol during a three‐week‐long in‐the‐wild deployment, we captured its social and analytical impact across 235 distinct interaction sessions. Our results show how our public analytical visualization supported passers‐by to create novel insights that were rather of casual nature. Several textual features that surrounded the visualization, such as titles that were framed as provocative hypotheses and predefined attention‐grabbing data queries, sparked interest and social debate, while a narrative tutorial facilitated more analytical interaction patterns. Our detailed mixed‐methods evaluation approach led to a set of actionable takeaways for public visualizations that allow novice audiences to engage with data analytical insights that have local relevance.
Root Causes of Insecure Internet of Things and Holistically Addressing Them

The use of Internet of Things (IoT) continues to grow rapidly and with this growth comes increased cybersecurity risks. We continue to observe public cybersecurity incidents caused by insecure IoT and statistics depicting the poor cyber hygiene of IoT devices. This paper identifies many of the root causes for insecure IoT and identifies the need for a holistic approach to IoT security that includes the adoption of secure system development lifecycle practices, prioritizing aspects of organizational and human psychology, improved economic models, and resolution of challenges inherent to the technology and operational use cases for IoT.
Characterizing Job-Task Dependency in Cloud Workloads Using Graph Learning

Modeling and scheduling diverse and dynamic workloads effectively has become a crucial issue due to the ever increasing scale and complexity of systems and applications in modern data centers. A large-scale cloud system consists of a large number of computing nodes, storage nodes and networking devices, running diverse workloads. Existing works analyzed execution traces in terms of resource usage by applying statistical methods. Cloud workloads, especially batch jobs, are composed of tens to thousands of tasks with complex dependency which can be represented by directed acyclic graphs (DAGs). Those workloads and their dependencies have not been thoroughly studied. Understanding the characteristics of batch cloud workload helps us foresee resource demands and execution time of new jobs and make better decisions in job scheduling. In this paper, we investigate batch jobs in production cloud computing environments with dependencies from the perspective of topological characteristics and structural patterns. We design a graph learning approach for job classification based on jobs’ topological similarity. We evaluate our methods using traces collected from a production data center and discover insightful properties and patterns of batch jobs in real-world scenarios. To the best of our knowledge, this is the first such work that leverages graph learning to explore the topological structures for cloud workflow for characterization and analysis.
SAMOS: a Smart Contract Access Management over Opaque and Substructural Types

Public Blockchains (BC) in support of Smart Contracts (SC), e.g., Ethereum, enable everyone to coordinate in a decentralized model to manage scarce and valuable resources,e.g., cryptocurrencies. Such BCs allow for the building of SCs that own resources and manage a set of permissions describing who is allowed to interact with these resources and what actions they can apply to them. However, Programming Languages (PL) and run-time systems used in current BCs lack a secure, flexible, and straightforward way to implement permissions within their SCs, leading to erroneous implementations that allow unauthorized access. The best-known incident related to a permission problem was the "Parity Hack", which led to the "loss" of tokens, valued at approximately 31 M USD.A better and secure SC access control concept provides an improved path to managing permissions. Thus, this paper presents a novel concept for handling permissions compatible with functional SC languages leveraging opaque and substructural data types to provide capability-based permission management. The opaque data types enforce that only designated functions can create permission carrying capabilities. Substructural data types prevent an unpermitted duplication of capabilities.
Influence of HMD Type and Spatial Ability on Experiences and Learning in Place-based Education

With the emergence of different types of Head-Mounted Displays (HMDs), researchers and educators must make informed decisions on what HMDs best support their needs. When performing experiments with relatively large populations, these decisions are largely affected by the sensing-scaling tradeoff between high-end tethered HMDs and lower-end standalone systems. Higher sensing affords a richer experience, but it is also associated with higher costs in terms of the HMD itself and the need for VR-ready computers. These limitations often impede instructors from using high-end HMDs in an efficient way with larger populations. We report on the results of a study in the context of place-based immersive VR (iVR) Geoscience education that compares the experiences and learning of 45 students after going through an immersive virtual field trip, using either a lower-sensing but scalable Oculus Quest or a higher-sensing but tethered HTC Vive Pro. Our results indicate that students who used the Quest reported significantly higher levels of satisfaction but also more simulator sickness (although still a very low number on average) compared to those who used an HTC Vive Pro. Our findings suggest that with content design considerations, standalone HMDs can be a viable replacement for high-end systems in large-scale studies. Furthermore, our results also suggest that in the context of place-based iVR education, the spatial abilities of students (i.e., sense-of-direction) can be a determining factor in their experiences and learning, and therefore an important topic of study for designing effective place-based iVR experiences.
MPoC - A Metaheuristic Proof of Criteria Consensus Protocol for Blockchain Network

Over the last years, many blockchain platforms opt for Delegated Proof-of-Stake (DPoS) consensus protocol for their network deployment. However, using the DPoS, nodes on the network tend to vote for the nodes that are more likely to become block producers after several voting rounds. Hence, with DPoS, the number of block producers is quite small compared to the total number of nodes in the blockchain network. Besides, with DPoS consensus, the distribution of block producers often centralizes into several individual nodes. This paper proposes a novel consensus protocol called Meta-heuristic Proof of Criteria (MPoC) to overcome the disadvantages. Our protocol enables multiple operational criteria to evaluate all blockchain nodes during the block producer selection process. We apply meta-heuristic algorithms to optimize the decentralized level of the node selection based on the set of criteria weights to increase the democracy of the blockchain network.We carry out experiments to evaluate the block producer decentralization of MPoC as compared with DPoS. The achieved results show that our consensus protocol improves the decentralization of block producers as expected. We also show the feasibility of applying different meta-heuristic algorithms to our consensus protocol of blockchain through experiments.
The Recurrent Neural Network for Program Synthesis

The program synthesis is a new and ambitious field consists of generating from short and natural descriptions a source code. The program synthesis is rapidly becoming a popular research problem, interesting potential has been offered by using artificial intelligence to support new tools in almost all areas of program analysis and software engineering. The goal of our long-term research project is to enable and facilitate any user to create an application from a description written in natural language by specifying the need for a complete system. This involves carrying out a study of the user's needs, the design and implementation of an intelligent system allowing the automatic realization of an Informatic project (architecture, initialization scripts, configuration, etc.) expressed in natural language. We propose a recurrent neural network with LSTM cells and we publish a dataset of more than 145 765 questions and their best answer, especially in Java. The performance of the proposed model is very interesting such that the success rate is 94%.
Work of the Unemployed

Public services increasingly presume a new and more active role for individuals to play in datafied society. While design efforts increasingly attempt to include stakeholders, such attempts are often limited to professional perspectives. Little is known about how individuals who are subject to these solutions experience the increasing use of data about them. One example of public services is job placement. Taking design fiction as our approach, we invited individuals enrolled in job placement (n=20) to reflect on the ‘work of the unemployed’, a fictive scenario where individuals make themselves eligible for support through sharing data. The fiction addresses power dynamics. The study shows how approaches, such as design fiction, are effective at including marginalized communities through changing the conditions for design. Showcasing the fictional outlook, and how the ‘design experience’ can be disempowering if not qualified through a deeper critique, the paper contributes to agendas on design justice.
Generation of Word Vectors for Unknown Words without Additional Corpus

This paper proposes unsupervised methods for generating a vector representation of unknown words that do not appear in a corpus. Word embeddings such as Word2Vec, FastText, and Glove have been used in a wide variety of text mining applications. Different from the one-hot representation of words, word embeddings can consider the similarity between related words. Additional compositionality is another interesting characteristic that shows promise for semantic calculations. As word embeddings are applied as preprocessing in many text mining applications, the words for which vectors are not obtained from a corpus are not considered in subsequent processes. To solve this problem, this paper proposes generating vectors of unknown words without any additional corpus. The proposed approach obtains word vectors with arithmetic operations on the vectors of known words. The key idea is to use word categories and relationships among them, which include syntactic and semantic relationships. Three kinds of generation methods are proposed and evaluated in combination with the CBoW model of Word2Vec. Two kinds of experiments are conducted: one to evaluate the quality of generated word vectors and another to investigate the impact of generated word vectors on a document classification task. The results show that, in the best case, about 84% of the generated vectors are ranked in the top 10 most similar to actual vectors. It is also shown that the generated vectors affect the performance of the document classifier, but the effect is not negative.
Design and implementation of a cognitive ability virtual reality training tool

Virtual reality applications are being designed and used for training in different fields, such as medicine, emergency response and exercise. Here, we show how we created a cognitive training tool using Unity3d and the Oculus Rift head-mounted display from a strong, dedicated design and planning phase before software implementation. First, in a conception phase, a research was done to understand how to do game development and cognitive training, then we made a paper prototype integrating the findings. Later, in the implementation phase the scene was set up as well as the interface with the head-mounted display, and finally the gameplay elements were programmed in. This resulted in a training tool that was later run through usability tests.
Convolutional Neural Network (CNN) Based Automated Defect Classification (ADC) with Imbalanced Data

Recently, deep learning (DL) convolutional neural network (CNN) has been employed for automated defect classification (ADC), with its diverse modeling approaches and network configurations, aiming to provide the best performance classifiers for wafer defect inspection. However, in semiconductor wafer inspection, critical killer defects data samples are usually very few although it is critical to classify these defects correctly in early stage of the wafer inspection process. Without specifically handling the imbalanced data problem, a classifier induced from the imbalanced data set is more likely to be biased towards the majority class and results in very poor classification result on the minority class (critical killer defects). This paper proposes a CNN for wafer ADC while addressing class imbalance issue via generative adversarial network (GAN) generated images. The experimental imbalanced dataset, consisting of scanning electron microscopy (SEM) images, is collected with ASML-HMI inspection tools.
A Novel Hash Chain-Based Data Availability Monitoring Method for Off-site Disaster Recovery Architecture

With the rapid development of mobile Internet, there is a higher demand for the real-time, reliability and availability of information systems and to prevent the possible systemic risks of information systems, various business consistency standards and regulatory guidelines have been published, such as Recovery Time Object (RTO) and Recovery Point Object (RPO). Some of the current related researches focus on the standards, methods, management tools and technical frameworks of business consistency, while others study the data consistency algorithms in the cases of large data, cloud computing and distributed storage. However, few researchers have studied on how to monitor the data consistency and RPO of production-disaster recovery, and what architecture and technology should be applied in the monitoring. Moreover, in some information systems, due to the complex structures and distributions of data, it is difficult for traditional methods to quickly detect and accurately locate the first error data. Besides, due to the separation of production data center (PDC) and disaster recovery data center (DRDC), it is difficult to calculate the data difference and RPO between the two centers. This paper first discusses the architecture of remote distributed DRDCs. The architecture can make the disaster recovery (DR) system always online and the data always readable, and support the real-time monitoring of data availability, consistency as well as other related indicators, in this way to make DRDC out-of-the-box in disasters. Second, inspired by blockchain, this paper proposes a method to realize real-time monitoring of data consistency and RTO by building hash chains for PDC and DRDC. Third, this paper evaluates the hash chain operations from the algorithm time complexity, the data consistency, and the validity of RPO monitoring algorithms and since DR system is actually a kind of distributed system, the proposed approach can also be applied to the data consistency detection and data difference monitoring in other distributed systems.