Reviewerly/exHarmony huggingface dataset:
Setup of a new form measurement system for flat and slightly curved optics with diameters up to 1.5 metres

Large optics with diameters of up to 1.5 m are being used more and more in industry and science. Flatness measurements of these optics are needed with uncertainties down to a few ten nanometres. For slightly curved specimens with radii of curvature down to 10 m uncertainties in the sub-micrometre range are required. We are currently building a new form measurement system which aims to fulfil these requirements. It will be set up in 2020 and the first measurements will be carried out in 2021. The setup can be operated with different sensor heads which use deflectometric- or interferometricbased methods. We plan, amongst other things, to use Fizeau interferometers with aperture sizes of 10 mm, 100 mm and 150 mm. The mechanical and optical setup of this new system is presented and simulation results of conventional subaperture stitching methods for this system with an aperture of 100 mm are shown. We also discuss the different measurement methods for the absolute form measurement of these optics.
Large Scale Augmented Reality for Collaborative Environments

The purpose of this paper was to test UWB (ultra wide-band) tracking technology in augmented reality mode to achieve an environment where more than two users can interact with virtual content (3D models, intelligent avatars) in large rooms or open-air environments. The study showed that there is much potential for this technology despite precision not being very high. Position measurements and participant experience confirmed that these visualization and tracking technologies can be successfully used in Smart City infrastructures, collaborative training environments and other use cases.
Collaborating with an international partner institution to mitigate the cost of course textbooks

This study describes an initiative to improve textbook access and affordability for students in the University at Buffalo’s international partnership program with the Singapore Institute of Management. This undertaking has provided free electronic access to scores of textbooks, affecting thousands of students, and potentially saving them hundreds of thousands of dollars. It has also given rise to the adoption of open educational resources among program faculty. To gain a greater understanding of the initiative’s strengths, weaknesses, and impact, the author administered a survey of students over the course of two academic terms. The resulting data showed a majority of respondents who reported an increase in course satisfaction, and who indicated that free access to electronic textbooks may have an indirect impact on their future course selections. It also showed a surprisingly high percentage of respondents who reported their perception of a learning benefit associated with free electronic access to course texts.
Developments in Computer Science With Application to Text Analysis

This chapter provides an overview of current computer technology with potential applications to text analysis. Quantitative analyses of textual materials have benefitted from computing technology as long as that technology has been available. In fact, some of the earliest computers were designed specifically to support text analysis for code breaking during World War II. Although a scanned bit-mapped image itself is in machine-readable form and can be printed out in a form of high-technology copying, it must be converted into a collection of text characters to be useful for content analysis. The advantages of database technology for text applications are so great that some vendors have developed what are referred to as document management systems—systems that offer database features tailored specifically to text and related materials. In some text analyses no preprocessing is performed, leaving all extraction to be performed on the original text.
Effectiveness of the Immersive Virtual Reality in Upper Extremity Rehabilitation

Stroke has been one of the leading causes of death worldwide for the past 15 years. Upper limb dysfunction is one of the main symptoms in stroke patients. The purpose of is to evaluate the treatment effectiveness of Immersive virtual reality system in upper limb rehabilitation. A single-blind clinical trial, and pretest–posttest control group design trial was conducted. The Fugl-Meyer Assessment of Physical Performance, Box and Block Test of Manual Dexterity, and FIM self-care score were used at baseline and post intervention. All subjects were asked to complete a total of twenty training sessions over eight weeks. The results of this project can be summarized as follows: (1) A total of eighteen stroke patients were involved in the trial, 15 males and 3 females, with an average age of 57.42 years (SD 12.75), and time from stroke (Mean 8.78 months, SD 5.51). (2) Results of the differences between the two groups pretest–posttest showed that the two groups were significantly differences in FMA (Conventional group, p = 0.021; Immersive virtual reality group, p = 0.014). It is known from the above results that the immersive virtual reality game device contributes to the improvement of the functions of the upper limbs. The results of this project are expected to provide a reference for innovative design in the medical industry and the entertainment industry.
egg: Fast and Extensible E-graphs

An e-graph efficiently represents a congruence relation over many expressions. Although they were originally developed in the late 1970s for use in automated theorem provers, a more recent technique known as equality saturation repurposes e-graphs to implement state-of-the-art, rewrite-driven compiler optimizations and program synthesizers. However, e-graphs remain unspecialized for this newer use case. Equality saturation workloads exhibit distinct characteristics and often require ad hoc e-graph extensions to incorporate transformations beyond purely syntactic rewrites. This work contributes two techniques that make e-graphs fast and extensible, specializing them to equality saturation. A new amortized congruence closure algorithm called rebuilding takes advantage of equality saturation's distinct workload, providing asymptotic speedups over current techniques in practice. A general mechanism called e-class analyses integrates domain-specific analyses into the e-graph, reducing the need for ad hoc manipulation. We implemented these techniques in a new open-source library called egg. Our case studies on three previously published applications of equality saturation highlight how the flexibility of e-class analyses supports diverse domains and how egg can provide up to 3000x speed ups.
Optimizing Prediction Serving on Low-Latency Serverless Dataflow.

Prediction serving systems are designed to provide large volumes of low-latency inferences machine learning models. These systems mix data processing and computationally intensive model inference and benefit from multiple heterogeneous processors and distributed computing resources. In this paper, we argue that a familiar dataflow API is well-suited to this latency-sensitive task, and amenable to optimization even with unmodified black-box ML models. We present the design of Cloudflow, a system that provides this API and realizes it on an autoscaling serverless backend. Cloudflow transparently implements performance-critical optimizations including operator fusion and competitive execution. Our evaluation shows that Cloudflow's optimizations yield significant performance improvements on synthetic workloads and that Cloudflow outperforms state-of-the-art prediction serving systems by as much as 2x on real-world prediction pipelines, meeting latency goals of demanding applications like real-time video analysis.
Can Smart Voice Assistant Induce Social Facilitation Effect? A Preliminary Study

The present study investigated whether a smart voice assistant can induce social facilitation effect. We designed a 3 (social presence: alone, smart voice assistant, human assistant) × 2 (task difficulty: easy, difficult) within-subject experiment. Sixteen university students participated in the experiment. The results showed that a smart voice assistant could induce social facilitation effect when participants performed modular arithmetic tasks, i.e., the response time of easy tasks was shortened while that of difficult tasks was lengthened. No significant difference in accuracy rates was found among three social presence conditions. Intensities of social facilitation effect between smart voice assistant present and human assistant present showed similar strength. These findings provide practical implications in designing work organization modes involving smart voice assistants.
Contrast and Parameter Research of Augmented Reality Indoor Navigation Scheme

We are committed to using various indoor and outdoor images, 3D objects and static scenes as recognition objects to build an augmented reality world. This paper focuses on the research and application of indoor augmented reality navigation. Indoor navigation has a variety of technical solutions, such as Wi-Fi Based and indoor sensor based. As one of them, augmented reality has the advantages of no need to deploy additional hardware devices in advance, six degrees of freedom and high precision. By analyzing the development of augmented reality indoor navigation and the underlying technology, we summarize and implement three solutions: map based (MB), point-cloud based (PCB), image based (IB). We first conducted a control experiment, and compared these schemes with the flow theory and the experimental data. At the same time, we collected the feedback and suggestions during the experiment, and carried out a second experiment on some components of augmented reality navigation (such as path, point of interest), and obtained the corresponding quantitative data.
Vizarel: A System to Help Better Understand RL Agents.

Visualization tools for supervised learning have allowed users to interpret, introspect, and gain intuition for the successes and failures of their models. While reinforcement learning practitioners ask many of the same questions, existing tools are not applicable to the RL setting. In this work, we describe our initial attempt at constructing a prototype of these ideas, through identifying possible features that such a system should encapsulate. Our design is motivated by envisioning the system to be a platform on which to experiment with interpretable reinforcement learning.
A Note on the Interpretability of Machine Learning Algorithms

We are interested in the analysis of the concept of interpretability associated with a ML algorithm. We distinguish between the How, i.e., how a black box or a very complex algorithm works, and the Why, i.e. why an algorithm produces such a result. These questions appeal to many actors, users, professions, regulators among others. Using a formal standardized framework , we indicate the solutions that exist by specifying which elements of the supply chain are impacted when we provide answers to the previous questions. This presentation, by standardizing the notations, allows to compare the different approaches and to highlight the specificities of each of them: both their objective and their process. The study is not exhaustive and the subject is far from being closed.
Automated and Formal Synthesis of Neural Barrier Certificates for Dynamical Models

We introduce an automated, formal, counterexample-based approach to synthesise Barrier Certificates (BC) for the safety verification of continuous and hybrid dynamical models. The approach is underpinned by an inductive framework: this is structured as a sequential loop between a learner, which manipulates a candidate BC structured as a neural network, and a sound verifier, which either certifies the candidate's validity or generates counter-examples to further guide the learner. We compare the approach against state-of-the-art techniques, over polynomial and non-polynomial dynamical models: the outcomes show that we can synthesise sound BCs up to two orders of magnitude faster, with in particular a stark speedup on the verification engine (up to five orders less), whilst needing a far smaller data set (up to three orders less) for the learning part. Beyond improvements over the state of the art, we further challenge the new approach on a hybrid dynamical model and on larger-dimensional models, and showcase the numerical robustness of our algorithms and codebase.
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Can developers make communication software more usable for at-risk users they are never likely to meet?
Going Beyond Computer-Assisted Vocabulary Learning: Research Synthesis and Frameworks

This paper introduces three computer-assisted applications designed for learning foreign vocabulary in an informal setting. The first one, images recommendation application, generates appropriate image recommendations for representing a word. It tackles the challenge for a foreign language learner to determine appropriate images from a standard web search engine such as Google, Yahoo, Flicker, etc. The second application, learning context representation application, generates learning contexts automatically from lifelogging images. It addresses problems associated with describing a learning context in the forms of hand-written descriptions, keeping notes, or taking memos. The third application we discuss here, namely location-based associated word recommendation application, generates recommendations of associated words in a particular learning location by analyzing word learning histories. It seeks to answer a critical question: what I should learn next? This is a critical challenge for the users of ubiquitous learning tools. In order to recommend potential vocabularies which a learner could be learning in a particular location, this study recommends associated words and topic-specific vocabularies. These applications are for AIVAS (Appropriate Image-based Vocabulary Learning System), a platform for computer-assisted vocabulary learning. We report here several evaluations, including human assessment and data-driven assessments, that have been carried out to reveal the importance of these systems.
Research on Dual-Arm Coordinated Trajectory Planning in Grid Coordinate Space

In this paper, the coordinated polishing of dual-arm robots is used for research purposes. The virtual space grid established by the raster method in the working space is used as the basis for collision detection. The coordinate space is used to further plan the trajectory of the dual robots, and MATLAB is used for simulation and simulation. The results show that the method can effectively avoid the collision of the double robotic arms and plan a reasonable moving trajectory.
An Iterative Feedback Mechanism for Auto-Optimizing Software Resource Allocation in Multi-Tier Web Systems

Software resource allocation has a significant impact on the quality of service and the performance of multi-tier web systems. It poses a great challenge to compute the allocation of different software resources in order to meet performance requirements under dynamic workloads conditions. To this end, this paper proposes an iterative feedback mechanism to optimize software resource allocation of multi-tier web systems. Specifically, we propose a Q-learning network-based approach for performance prediction. The predictor involves a deep Q-learning network for capturing the dynamics of online software resource allocation, and then computing the current optimal policy. We implement the approach in the RUBiS benchmark system, and the experimental results demonstrate its significant advantages.
Smart Contracts for Contract Management

This book chapter discusses the advantages of smart contracts for contract management by presenting a decentralized application for retention payment on the blockchain platform. The proposed smart contract application performs the execution of retention clauses of a typical project contract by performing automated payment of retention immediately after the client’s approval of partial completion of works. The application also enables the storage of project completion and retention payment data on the blockchain. A case study of a real construction project was analyzed to test the proposed system and to illustrate its potential benefits. The design decisions made for the application are included, along with its security evaluation. The pioneering application of smart contracts on contract management revealed the fact that by proving a secure, reliable, immutable, transparent, efficient, and trustworthy platform for automated execution of contract clauses, smart contracts present a major potential for improving the contract management process.
Privacy Preservation Algorithms on Cryptography for AI as Human-like Robotic Player for Fighting Game Using Rule-Based Method

This research followed FightingICE platform for creating strong computer Artificial Intelligence (AI) player using JAVA programming. In this research, we were trying to understand player character and skill, and use here ZEN CHARACTERS’ actions and skills for my computer AI player; however, some of the previous AIs followed rule-based method, where their actions are mainly resolved by different characteristics of the game, such as the behaving coordinates or amount of damages. We used in this research rule-based methods; for example, when an opponent player attacks, our computer AI player will jump and will move close to opponent player and then kick. Also, we used some character actions such as base actions, guard actions, and skill actions. We have carried out some experiments with other AI players, and my computer AI player won seven of ten matches. In addition, we also removed some rules and then made out AI player combat with opponent player, and observed some changes in winning and losing results, which we describe in this chapter.
E2E VERIFIABLE BLOCKCHAIN VOTING SYSTEM USING HOMOMORPHIC ENCRYPTION

The security and accountability element is a challenge from the conventional voting system, still widespread elections. General elections are now operating in a centralized network, there is an agency that manages the voting process in full. One of the problems with traditional election systems is the entity with full control of the database and procedure, and essential resources can be exploited by the database. Blockchain is one solution to these problems in the electoral system, as it involves a decentralized network and many users own all databases. It helps a population community to access, update and manage a public database which is managed by nobody but is owned and updated by each individual. Blockchain-based implementation will remove the issue of database manipulation. Thus, blockchain technology can improve the acceptability and efficiency of electronic voting.
Measuring the Performance of Encrypted DNS Protocols from Broadband Access Networks.

Until recently, DNS traffic was unencrypted, leaving users vulnerable to eavesdropping and tampering. In response to these privacy concerns, two protocols have been proposed: DNS-over-TLS (DoT) and DNS-over-HTTPS (DoH). Previous work has demonstrated that, in general, response times with popular DoT and DoH resolvers are marginally slower than conventional DNS, but sometimes faster on emulated lossy networks. However, these measurements were not taken from home networks, nor at scale from many vantage points. Furthermore, they do not capture performance on real networks with low bandwidth or high latency and packet loss. In this paper, we study the performance of encrypted DNS protocols and DNS from thousands of home networks in the United States, over one month in 2020. We perform these measurements from the homes of 2,768 participating panelists in the Federal Communications Commission's (FCC) Measuring Broadband America program. We find that, across the aggregate dataset, median DoT and DoH response times are as much as 7 ms and 23.2 ms slower than conventional DNS. We study the effects of latency, bandwidth, and heterogeneity between Internet service providers on DNS performance and find that latency had the most significant effect on response times, particularly for DoH. We also find that there can be significant variation in DNS performance between resolvers, with median query response times differing by as much as 23.7 ms.
Research on the Current Situation and Countermeasures of English Teaching in Vocational Colleges in the Era of Big Data

Abstract This paper discusses the characteristics of big data and status of it in the field of English teaching, expounds the problems existing in current English teaching in vocational education, and analyses the ways to realize the optimal allocation of teaching resources, the transformation of teachers’ functions and roles, the development of individualized students and the teaching environment on the basis of big data. It aims to integrate classroom teaching with practical situations, truly transform students’ knowledge into practical English application skills and better serve their major.
Requirements Elicitation and Specification for Educational Technology Development: A Systematic Literature Mapping

The present work aimed to map the various requirements engineering techniques and methodologies for the construction of educational technologies through a systematic mapping study. The research protocol of this mapping included the planning, execution, and analysis of the results phases. This research added information about the diversity of methods and techniques of requirements engineering applied in the process of developing educational technologies. However, this research indicated that the phenomena related to the pedagogical practice experienced by teachers and students are sometimes relegated in this process. This work analyzed the primary studies of the last ten years, making it possible to identify gaps in this area of research to propose research and the generation of new forms of work in the development of educational systems.
Dense Isometric Non-Rigid Shape-From-Motion Based on Graph Optimization and Edge Selection

In this letter, we propose a novel framework for dense isometric non-rigid shape-from-motion (Iso-NRSfM) based on graph topology and edge selection. A weighted undirected graph, of which nodes, edges, and weighted values are respectively the images, the image warps, and the number of the common features, is built. An edge selection algorithm based on maximum spanning tree and sub-modular optimization is presented to pick out the well-connected sub-graph for the warps with multiple images. Using the infinitesimal planarity assumption, the Iso-NRSfM problem is formulated as a graph optimization problem with the virtual measurements, which are based on metric tensor and Christoffel Symbol, and the variables related to the derivatives of the constructed points along the surface. The solution of this graph optimization problem directly leads to the normal field of the shape. Then, using a separable iterative optimization method, we obtain the dense point cloud with texture corresponding to the deformable shape robustly. In the experiments, the proposed method outperforms existing work in terms of constructed accuracy, especially when there exists missing/appearing (changing) data, noisy data, and outliers.
The Passive Identification and Location of the Marine Moving Object

The identification and location of the marine moving object is a basic problem for the passive radars and it is widely used in the fields of ocean escort and salvage. In the background of our army's survey of the marine moving object, the research methods for two-ship and single ship passive locating are studied. The models of moving state for the target ship are established by the azimuth angle. Meanwhile, the possible inaccuracy is also analyzed in this essay.