Reviewerly/exHarmony huggingface dataset:
Incremental Analysis of Evolving Alloy Models

Alloy is a well-known tool-set for building and analyzing software designs and models. Alloy’s key strengths are its intuitive notation based on relational logic, and its powerful analysis engine backed by propositional satisfiability (SAT) solvers to help users find subtle design flaws. However, scaling the analysis to the designs of real-world systems remains an important technical challenge. This paper introduces a new approach, iAlloy, for more efficient analysis of Alloy models. Our key insight is that users often make small and frequent changes and repeatedly run the analyzer when developing Alloy models, and the development cost can be reduced with the incremental analysis over these changes. iAlloy is based on two techniques – a static technique based on a lightweight impact analysis and a dynamic technique based on solution re-use – which in many cases helps avoid potential costly SAT solving. Experimental results show that iAlloy significantly outperforms Alloy analyzer in the analysis of evolving Alloy models with more than 50% reduction in SAT solver calls on average, and up to 7x speedup.
A novel in situ calibration of object distance of an imaging lens based on optical refraction and two-dimensional DIC

The strain results of two-dimensional digital image correlation (2D-DIC) are known to be markedly affected by out-of-plane motion in practical measurements. To correct strain results, it is necessary to measure the object distance of the camera imaging lens. Due to the difficulty of conventional calibration using a translation stage, a novel in situ calibration based on optical refraction is proposed herein to determine the object distance. In our calibration, recorded images show a radial outward displacement field when a glass plate is placed in front of a planar specimen. This study presents a formula derivation of the displacement and strain distributions theoretical model. Based on the strain model and the calculated strain fields obtained using a 2D-DIC algorithm, the object distance can be obtained with a high measurement accuracy. Experiments were conducted to verify the validity and reliability of the proposed calibration. Comparison results between a conventional translation calibration and the proposed calibration indicate that the proposed calibration can reach a measurement accuracy of 0.19% (1.2 mm in this paper). This experiment demonstrates that the proposed calibration is an easily implemented method for the in situ computation of the object distance of an imaging lens.
Reinforcement Learning: Connections, Surprises, Challenges

The idea of implementing reinforcement learning in a computer was one of the earliest ideas about the possibility of AI, but reinforcement learning remained on the margin of AI until relatively recently. Today we see reinforcement learning playing essential roles in some of the most impressive AI applications. This article presents observations from the author's personal experience with reinforcement learning over the most recent 40 years of its history in AI, focusing on striking connections that emerged between largely separate disciplines and on some of the findings that surprised him along the way. These connections and surprises place reinforcement learning in a historical context, and they help explain the success it is finding in modern AI. The article concludes by discussing some of the challenges that need to be faced as reinforcement learning moves out into real world.
Digital reading for teaching and learning

The term “digital reading” is defined. The author proves that digital reading is a natural stage of reading evolution as a civilizing process. Being “the other side” of writing, reading has been reflecting the changes of 5 thousand years of its history. The reading practices have been changing with changing writing practices. Today, as soon as the writing systems has been increasingly digitized and obtained some media features, the reading has been modifying itself, too. Digital reading has become introduced into the sphere of learning and education. Firstly, it is due to the education sector being intensively digitized, and secondly, because the text (which means reading, too) forms the basis for education. The author examines both advantages and risks of digital reading, in particular that of the hypertexts. She argues that student’s mastering digital reading requires to be supported by “a competent adult”. That is why the role of the libraries, and in particular, children’s, school, or academic libraries working in the digital environment, has been significantly increasing.
Digital image correlation and optical strain measuring in bendability assessment of ultra-high strength structural steels

Air-bending is a widely used forming process for ultra-high strength steel because it is fast, cost-effective and flexible way to form material. The bendability of a material can be expressed by minimum bending radius R/tmin, which is the relation of the smallest inner radius to the sheet thickness the material can be bent without damage. Damage usually occurs on the outer surface of the bend in the form of intense strain localization that further progresses to cracking. The minimum bending radius contains no other information that affects the bendability such as the lower tool width or the desired bending angle. Hence, developing more detailed test procedure is critical to better describe the behavior of ultra-high strength steel sheet in bending. In this paper, a method for more detailed assessment of bendability for ultra-high strength structural steel is presented. Using optical strain measuring techniques and digital image correlation coupled with bending tests in a universal tensile test machine one can measure the strain evolution at the outer surface of the bend and determine the critical strains that limit the bendability of these materials
Automating Dependence-Aware Parallelization of Machine Learning Training on Distributed Shared Memory

Machine learning (ML) training is commonly parallelized using data parallelism. A fundamental limitation of data parallelism is that conflicting (concurrent) parameter accesses during ML training usually diminishes or even negates the benefits provided by additional parallel compute resources. Although it is possible to avoid conflicting parameter accesses by carefully scheduling the computation, existing systems rely on programmer manual parallelization and it remains a question when such parallelization is possible.
GPU-based efficient computation of power diagram

Power diagrams are widely used in graphics and engineering. One of the most complex operations defined on the centroidal capacity-constrained power diagrams is the geometrical construction, which takes more than 50% of the total computing time. In order to overcome this performance bottleneck, we propose a novel GPU-based power diagram construction algorithm. To this end, we first introduce the jump flooding algorithm for parallel rendering of the power diagram, and present an approach for extracting the geometrical vertices and edges. Next, we introduce two novel GPU-based algorithms to improve the computational performance. The first algorithm allows a hybrid GPU-CPU implementation by coupling the existing CPU-based algorithm while the second algorithm is a pure GPU implementation for the platforms where GPU hardware is capable of significant speedups. In our implementation, we utilize the discrete Lloyd's algorithm for centroidal constraints and a GPU-based analytical algorithm for weights and capacities. Experiment results show that our approach improves the effciency of the power diagram construction up to several orders of magnitude.
minStab: Stable Network Evolution Rule Mining for System Changeability Analysis

Growing number of evolving systems creates demand for system evolution analytics with modern computational intelligence algorithms and tools. In this paper, we introduce new measures of stability and changeability for system evolution analysis over time. We proposed a Stable Network Evolution Rule Mining and a Changeability Metric for an evolving system. For this, we use two different characteristics of Network Evolution Rules (NERs). First, given a network of a system state S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> , we call an NER interesting in S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> if its support and confidence exceed given thresholds (minimum support and minimum confidence). Second, given a set of networks for a set of states (SS), we define the stability of an NER to be the percentage of states in SS in which the rule is interesting. We call an NER stable in SS if its stability exceeds a given threshold named as minimum stability (minStab). Based on this, we developed an intelligent tool, which is used for experiments on evolving systems. We applied our approach to a number of real-world systems including: software system, natural language system, retail market system, and IMDb system. It results Stable NERs and Changeability Metric value for each evolving system.
A Context-Aware Accurate Wellness Determination (CAAWD) Model for Elderly People Using Lazy Associative Classification

Wireless Sensor Network (WSN) based smart homes are proving to be an ideal candidate to provide better healthcare facilities to elderly people in their living areas. Several currently proposed techniques have implementation and usage complexities (such as wearable devices and the charging of these devices) which make these proposed techniques less acceptable for elderly people, while the behavioral analysis based on visual techniques lacks privacy. In this paper, a context-aware accurate wellness determination (CAAWD) model for elderly people is presented, where behavior monitoring information is extracted by using simple sensor nodes attached to household objects and appliances for the analysis of daily, frequent behavior patterns of elderly people in a simple and non-obtrusive manner. A contextual data extraction algorithm (CDEA) is proposed for the generation of contextually comprehensive behavior-training instances for accurate wellness classification. The CDEA presents an activity's spatial-temporal information along with behavioral contextual correlation aspects (such as the object/appliance of usage and sub-activities of an activity) which are vital for accurate wellness analysis and determination. As a result, the classifier is trained in a more logical manner in the context of behavior parameters which are more relevant for wellness determination. The frequent behavioral patterns are classified using the lazy associative classifier (LAC) for wellness determination. The associative nature of LAC helps to integrate spatial-temporal and related contextual attributes (provided by CDEA) of elderly behavior to generate behavior-focused classification rules. Similarly, LAC provides high accuracy with less training time of the classifier, includes minimum-support behavior patterns, and selects highly accurate classification rules for the classification of a test instance. CAAWD further introduces the ability to contextually validate the authenticity of the already classified instance by taking behavioral contextual information (of the elderly person) from the caregiver. Due to the consideration of spatial-temporal behavior contextual attributes, the use of an efficient classifier, and the ability to contextually validate the classified instances, it has been observed that the CAAWD model out-performs currently proposed techniques in terms of accuracy, precision, and f-measure.
Miniaturized omnidirectional flexible side-view endoscope for rapid monitoring of thin tubular biostructures

Endoscopic imaging allows longitudinal observation of epithelial pathologies in tubular organs throughout the body. However, the imaging and optical diagnosis of tubular biostructures such as small animal models and small pediatric organs require appropriately miniaturized devices. A miniaturized catadioptric flexible side-view endoscope is proposed with omnidirectional field of view (FOV) in the transverse direction and sub-mm-scale feature resolution. The FOV in the longitudinal direction is 50°. Images are unwrapped and stitched together to form composite images of the target by two different algorithms, revealing a composite FOV of more than 3.5 cm × 360°. The endoscope is well suited for minimally invasive rapid monitoring of thin tubular organs in pediatric patients, as well as for imaging of small animal disease models at near-cellular resolution.
Random three-step phase retrieval approach based on difference map Gram–Schmidt orthonormalization and Lissajous ellipse fitting method

To achieve high measurement accuracy with less computational time in phase-shifting interferometry, a random phase retrieval approach based on difference map Gram–Schmidt othonormalization and Lissajous ellipse fitting method (DGS-LEF) is proposed, it easy to implement and only needs three phase shifted interferograms, it doesn't need pre-filtering, moreover, the phase shift can be random except for too small relative phase shift between two adjacent interferograms, last but not least, this method is effective for the symmetrical and asymmetrical fringes. The simulations and experiments verify the correctness and feasibility of DGS-LEF.
Fast and live hypervisor replacement

Hypervisors are increasingly complex and must be often updated for applying security patches, bug fixes, and feature upgrades. However, in a virtualized cloud infrastructure, updates to an operational hypervisor can be highly disruptive. Before being updated, virtual machines (VMs) running on a hypervisor must be either migrated away or shut down, resulting in downtime, performance loss, and network overhead. We present a new technique, called HyperFresh, to transparently replace a hypervisor with a new updated instance without disrupting any running VMs. A thin shim layer, called the hyperplexor, performs live hypervisor replacement by remapping guest memory to a new updated hypervisor on the same machine. The hyperplexor leverages nested virtualization for hypervisor replacement while minimizing nesting overheads during normal execution. We present a prototype implementation of the hyperplexor on the KVM/QEMU platform that can perform live hypervisor replacement within 10ms. We also demonstrate how a hyperplexor-based approach can used for sub-second relocation of containers for live OS replacement.
Adaptively Transfer Category-Classifier for Handwritten Chinese Character Recognition

Handwritten character recognition (HCR) plays an important role in real-world applications, such as bank check recognition, automatic sorting of postal mail, the digitization of old documents, intelligence education and so on. Last decades have witnessed the vast amount of interest and research on handwritten character recognition, especially in the competition of HCR tasks on the specific data sets. However, the HCR task in real-world applications is much more complex than the one in HCR competition, since everyone has their own handwriting style, e.g., the HCR task on middle school students is much harder than the one on adults. Therefore, state-of-the-art methods proposed by the competitors may fail. Moreover, there is not enough labeled data to train a good model, since manually labelling data is usually tedious and expensive. So one question arises, is it possible to transfer the knowledge from related domain data to train a good recognition model for the target domain, e.g., from the handwritten character data of adults to the one of students? To this end, we propose a new neural network structure for handwritten Chinese character recognition (HCCR), in which we try to make full use of a large amount of labeled source domain data and a small number of target domain data to learn the model parameters. Furthermore, we make a transfer on the category-classifier level, and adaptively assign different weights to category-classifiers according to the usefulness of source domain data. Finally, experiments constructed from three data sets demonstrate the effectiveness of our model compared with several state-of-the-art baselines.
Digitalization of education in the leading universities of Saint Petersburg

The sphere of high technologies in the developed countries is gradually becoming the main source of economic growth, defines structural changes in economy and contributes to modernization of systems. That is why information-communication technologies represent the main instrument of modernization of the industrial production system, development of social infrastructure, structural transformation of economy. Implementation of information-communication technologies requires specialists who have totally new competencies which causes reorganization of the higher education system. The process of digitalization in the higher education system is carried out over the course of recent years. In this context, the aim of the study is estimation of education digitalization in leading universities of Saint Petersburg. The main directions of digitalization of education have been systematized within the framework of the study, basic risks and supposed results have been revealed. Estimation of digitalization of leading universities is being carried out in the following directions: implementation of education process, optimization of business-processes and optimization of document flow and organization of the process of the academic teaching staff retraining, organization of access to scientific works of universities. As a result, it has been revealed that only 3 leading universities of the city take active part in digitalization: they create online-courses, their own electronic educational environment; they provide access to their staff members' studies. The analysis of the structure of expenses in these universities has revealed insufficiency of financing for teaching staff information technologies. We should also note a general tendency of reducing expenses on digitalization in the educational sphere. In future, this will cause underdevelopment of education in teaching new technologies and preparing specialists with competencies non-demanded on the market.
An improved 2+1 phase-shifting algorithm

An improved 2+1 phase-shifting algorithm based on the flat image fitting calibration is proposed. The 2+1 phase-shifting algorithm proposed by Zhang uses one uniform gray image whose gray is the same as the mean gray of the phase-shifting sinusoidal grating patterns instead of one of these grating patterns in traditional three-step phase measuring profilometry to reduce measuring error caused by motion. It maintains that the light intensity distribution of captured flat image is equal to the dc component of the captured phase-shifting deformed patterns. But extensive experimental results show that there is a certain inconsistency between the light intensity distribution of the captured flat image and the dc component of the deformed patterns. It will bring some nonnegligible errors. So a calibration procedure is introduced before measuring to obtain a calibration coefficient between the captured flat image and the dc component of the captured fringe pattern by the least squares fit. While measuring, the veritable dc component of the captured deformed pattern is extracted by just multiplying the calibration coefficient with captured flat image. Thus, the above nonnegligible errors can be effectively improved. Furthermore, the proposed algorithm is more flexible because its shifting-phase is no longer fixed to be π/2 but optional. The experimental results show the effectiveness and practicability of the improved 2+1 phase-shifting algorithm.
A Trusted-ID Referenced Key Scheme for Securing SCADA Communication in Iron and Steel Plants

Supervisory control and data acquisition (SCADA) is a widely implemented structure to achieve remote measurement and control in many iron and steel plants. In traditional consideration, more attention on physical network separation methods is paid to isolate the SCADA system from management network to keep SCADA in a considered ”safe” state. In addition, lots of security solution providers are focusing on the network side security assurance without involving the SCADA communication level protection. This paper investigates a new trusted-ID referenced key scheme for securing SCADA communications efficiently. The advanced encryption standard algorithm is used in the data transmission for its fast calculating speed, and the elliptic curve cryptography digital signature algorithm is used to confirm the data package that is from the right ID which can avoid the measured values and the control instructions to be maliciously modified by attacker. This solution for securing SCADA communication provides an efficient way to protect the data and protocol between the controllers and the remote terminal units (RTUs), and offers an authentication for the communication, which can avoid Man-In-The-Middle attack. Random numbers are used as a session key that can avoid the replay attack. cipher-block chaining mode message authentication code calculation is used to meet the data integrity requirement. Gong Needham Yahalom logic is used to prove the security of this solution, and an example is given to verify its validity.
Impact of Human-Centered Design Process (HCDP) on Software Development Process

Human centered design involves human perspective at every stage of the development process. This paper encompasses the impact of applying human-centered design process (HCDP) to the software development process (SDP). Applying human centered design process to the software development process needs to focus on human factors involved to enhance the usability and for effective user experience. This research focuses on achieving high usability and user experience by improving and applying the human centered design activities on the software development process. For this purpose we proposed our HCD model and a PSC (People using Software in a Context) analysis for achieving the good user experience and integrate it with the human centered design process to ensure high usability. This research will investigate on how applying HCDP influence software development process in order to improve usability and the quality of user experience. We have developed a simple calculator application to see the impact of applying our HCD model to SDP and identify issues with and without following our proposed HCD model. Results revealed that proposed HCD model is good to answer the issues identified in the conventional software development process.
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI)

The 3rd International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interactions (VAM-HRI) will bring together HRI, Robotics, and Mixed Reality researchers to address challenges in mixed reality interactions between humans and robots. Topics relevant to the workshop include development of robots that can interact with humans in mixed reality, use of virtual reality for developing interactive robots, the design of augmented reality interfaces that mediate communication between humans and robots, comparisons of the capabilities and perceptions of robots and virtual agents, and best design practices. VAM-HRI 2020 will follow on the success of VAM-HRI 2018 and 2019, and advance the cause of this nascent research community