Reviewerly/exHarmony huggingface dataset:
The Challenges of Labeling Vulnerability-Contributing Commits

Software projects developed using version control are enhanced incrementally through commits, some of which inevitably introduce security vulnerabilities. The features of these vulnerability-contributing commits (VCCs) could be used to train a VCC detector or to inform software development best-practices. Previous work has attempted to label VCCs in open-source software projects for this purpose. We present a manual approach to VCC labeling using the fix commits listed in Common Vulnerabilities and Exposures (CVEs). We show that a published automated method of VCC labeling disagrees with our manual method on 42% of VCCs. We argue that the automated method, while effective in scaling VCC labeling, is therefore not sufficiently accurate. Finally, we discuss the benefits and drawbacks of trying to predict vulnerable software components rather than VCCs.
Natural Language Processing for Writing and Speaking

In this chapter we introduce the topic of automated scoring for written and spoken constructed responses and provide a detailed overview of the methodological underpinnings of natural language processing (NLP) that enable it. In the first section, we start with a high-level overview of the types of assessment tasks that automated text and speech scoring systems have been applied to and highlight the relevant aspects of the construct for each task type that motivate the use of automated scoring. In the second section, we provide an introduction to fundamental concepts from the fields of linguistics and NLP that are used in automated scoring technologies. In the third section, we introduce the standard processing pipeline for designing and using an automated scoring system for written or spoken language and provide in-depth overviews of the types of linguistic features that are typically extracted as well as the state-of-the-art NLP and speech processing methodologies that enables this. Finally, we conclude with a discussion of the readiness of automated scoring of text and speech for different use cases and discuss potential future directions for the field.
Bipartite Gaussian quantum steering, entanglement, and discord and their interconnection via a parametric down-converter

In this study, we quantify quantum steering, quantum entanglement, and quantum discord and their interconnection using the technique of parametric down-conversion. Initially, two single-mode Gaussian states together with a non-linear crystal in a cavity are considered. The behavior of the three kinds of quantum correlations depend on the phase of the coherent pump field, purity, and non-classicality of the input states, and the damping rates of the cavity. The amount and time evolution of the quantum correlations enhances with the difference between the non-classicality of the initial states. In presence of the damping rates, the quantum steering and quantum entanglement (quantum discord) increase (decreases) with the purity of the input cavity field. We note that the amount and survival time of the quantum correlations can be controlled by varying the relative phase associated to the coherent pump field. The boundaries of the three kinds of quantum correlations are defined and explained with respect to each other, which form a hierarchy.
Smartphone-Assisted Telecytopathology: An Intraobserver Concordance Study

Telecytology is the practice of cytology at a distance. The images captured by a camera are sent to the cytopathologist at a different location who views the images and reaches a diagnosis. Recently, smartphone-assisted telepathology has been evaluated for different subspecialities of pathology including cytology for second opinion.For the purpose of the study, a total of 151 cases of fine needle aspiration and 10 cases of urine cytology reported by a single pathologist were retrieved from the records. The images of all the cases were captured by a trained pathologist using the primary camera of a smartphone from the ocular of a binocular microscope. The images were sent by WhatsApp to the same pathologist who had made the conventional microscopy diagnosis. The images were viewed on the smartphone screen by the pathologist who replied with the diagnosis after analysing all the images with or without digital zoom.A total of 154 cases out of 161 were correctly diagnosed on smartphone-assisted telepathology (overall intraobserver concordance of 95.6%). For head and neck swellings, the concordance was 65/70 (92.9%), for breast lesions, it was 23/24 (95.8%), and for miscellaneous swellings, it was 57/57 (100%). For urine cytology, the concordance rate was 9/10 (90%).Though the results of this study are encouraging, further improvement in the smartphone camera resolution and internet connectivity would enhance the utility of smartphone-assisted telecytopathology.
EEG-based tonic cold pain assessment using extreme learning machine

The purpose of this study is to present a novel method which can objectively identify the subjective perception of tonic pain. To achieve this goal, scalp EEG data are recorded from 16 subjects under the cold stimuli condition. The proposed method is capable of classifying four classes of tonic pai n states, which include No pain, Minor Pain, Moderate Pain, and Severe Pain. Due to multi-class problem of our research an extended Common Spatial Pattern (ECSP) method is first proposed for accurately extracting features of tonic pain from captured EEG data. Then, a single-hidden-layer feedforward network is used as a classifier for pain identification. With the aid of extreme learning machine (ELM) algorithm, the classifier is trained here. The advantages of ELM-based classifier can obtain an optimal and generalized solution for multi-class tonic cold pain. Experimental results demonstrate that the proposed method discriminates the tonic pain successfully. Additionally, to show the superiority for the ELM-based classifier, compared results with the well-known support vector machine (SVM) method show the ELM-based classifier outperform than the SVM-based classifier. These findings may pay the way for providing a direct and objective measure of the subjective perception of tonic pain.
Posture Recognition Technology Based on Kinect

Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and three-dimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.
Poster: Development of Faculty Competences in Online Teaching

Universities are to expand their educational activities in the online environments, and an increasing number of instructors are involved in the design and delivery of online courses. To ensure productive online teaching and learning, the Faculty Development Institute of the Kazan National Research Technological University is developing approaches to training faculty for the transition to virtual learning environment. A model of training faculty for teaching online has been developed that integrated the classroom course with permanent online-support. Multimedia and interactive tools were actively used in the educational process. A survey was also conducted to study faculty members’ opinion as regards teaching online, barriers and factors that could foster their participation in teaching online.
Output Diversified Initialization for Adversarial Attacks

Adversarial attacks often involve random perturbations of the inputs drawn from uniform or Gaussian distributions, e.g., to initialize optimization-based white-box attacks or generate update directions in black-box attacks. These simple perturbations, however, could be sub-optimal as they are agnostic to the model being attacked. To improve the efficiency of these attacks, we propose Output Diversified Sampling (ODS), a novel sampling strategy that attempts to maximize diversity in the target model's outputs among the generated samples. While ODS is a gradient-based strategy, the diversity offered by ODS is transferable and can be helpful for both white-box and black-box attacks via surrogate models. Empirically, we demonstrate that ODS significantly improves the performance of existing white-box and black-box attacks. In particular, ODS reduces the number of queries needed for state-of-the-art black-box attacks on ImageNet by a factor of two.
Target driven visual navigation exploiting object relationships

Recently driven visual navigation strategies have gained a lot of popularity in the computer vision and reinforcement learning community. Unfortunately, most of the current research tends to incorporate sensory input into a reward-based learning approach, with the hope that a robot can implicitly learn its optimal actions through recursive trials. These methods seldom generalize across domains as they fail to exploit natural environment object relationships. We present Memory-utilized Joint hierarchical Object Learning for Navigation in Indoor Rooms (MJOLNIR), a target-driven visual navigation algorithm, which considers the inherent relationship between target objects, along with the more salient parent objects occurring in its surrounding. Extensive experiments conducted across multiple environment settings show $\approx \textbf{30 %}$ improvement over the existing state-of-the-art navigation methods in terms of the success rate. We also show that our model learns to converge much faster than other algorithms. We will make our code publicly available for use in the scientific community.
Improvement of the digital image correlation close to the borders of an object

Abstract The measurement error of the finite‐element‐based Digital Image Correlation is reduced along the borders of an object by using the precise measurement, obtained by Virtual Image Correlation (VIC). The proposed method is called Virtual and Digital Image Correlation (VDIC). The boundary, identified by the VIC with a subpixel precision, is used firstly to create an adapted mesh and secondly to create a pixel mask. Futhermore, the VDIC also uses the VIC measurement of the boundary in the deformed state as a constraint on the radial displacement field along the border. The optimal values of the parameters of the VDIC are discussed throughout a sensitivity analysis. The compared performances of the constrained and unconstrained VDIC are obtained thanks to a synthetic test of a plate with a hole in tension. Finally, the method is checked on a sample geometry which includes holes and a U‐shaped notch.
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Let p be a prime number, in this paper, we investigate the structures of all constacyclic codes of length ps over the ring Ru2,v2,pm=Fpm[u,v]∕〈u2,v2,uv−vu〉. The units of the ring Ru2,v2,pm can be divided into following five forms: α, λ1=α+δ1uv, λ2=α+γv+δuv, λ3=α+βu+δuv, λ4=α+βu+γv+δuv, where α,β,γ,δ1∈Fpm∗ and δ∈Fpm. We obtain the algebraic structures of all constacyclic codes of length ps over Ru2,v2,pm, except (α+δ1uv)-constacyclic codes, in terms of their polynomial generators and also find the number of codewords in each of these constacyclic codes. The number of constacyclic codes and duals of constacyclic codes corresponding to the units λ2,λ3 and λ4 are determined. We also provide examples to illustrate our results, which include several optimal codes.
Financial Big Data Hot and Cold Separation Scheme Based on HBase and Redis

In the era of big data, HBase has been widely used in scenarios of massive unstructured data. For the financial big data, due to the integrity and timing of it, unreasonable data storage and management usually lead to hot spots that decreases the query performance. In practice, the separation of hot and cold financial data will improve data query performance and utilization rate of cluster resources. In this paper, a hot and cold data separation scheme is designed, to store infrequently queried financial data to HBase, and frequently queried one to Redis. The cold data is reasonably planned and managed through pre-partitioning and row key design for HBase. A hot data cache based on Redis is realized to improve the query speed and reduces the pressure of HBase. In addition, due to the lack of Redis's inherent cache elimination strategy, we propose a caching strategy based on the frequencies of updating and querying operations. The experimental results show that the scheme can effectively avoid the hot storage problem, and improve the query performance, and improve the cache hit ratio of Redis. Therefore, the number of cold data access requests can be effectively reduced.
Effects of the Flipped Classroom on Understanding the Thermodynamic Concept at High School Students

This study explains the increase in 11thgrade student achievement on the thermodynamic subject with a flipped classroom approach that was assisted by learning animation videos developed using Powtoon application.This research was a quasi-experimental design with a pretest-posttest control group design.The sample of this study was two classes from five classes of 11th-grade students.Instruments for collecting data on achievement used test instruments in the form of multiple choices.Nontest instruments in the form of questionnaires with a Likert scale were used to measure student learning motivation and numeric abilities.The statistical analysis techniques were T-test, ANOVA, and linear regression.The results of this study found that there was an increase in student achievement between before and after being taught with flipped classroom learning models assisted by learning animation videos.Also, there were also significant differences in achievement between students taught with flipped classroom learning models supported by learning animation videos compared to students who were taught conventionally.In this study numerical abilities can be used as predictors.The contribution of numerical ability to students who were taught with flipped classroom models was higher than in another group.The limitation of this study is that this study did not pay attention to the strategies paired on the development of animated videos.The selection of learning strategies that are attached to the animated video will be used to determine the effect on learning.In general, research on the flipped classroom does not emphasize the type of video used.This study highlights the attractiveness of the animation made in the learning video.
A Software Framework to Create Behaviors for Androids and Its Implementation on the Mobile Android "ibuki"

Androids--humanoids with a human-like appearance are studied in limited communities but draw considerable attention. Considering an android integrates numbers of actuators, sensors and devices into a system, a standardized framework is beneficial to easy-move and extension of potential applications. In this report, we developed a ROS-based software framework for androids. With the framework, intuitively generating, blending and reusing the android motion is aimed to be realized. Some software applications are developed under this framework and they are implemented on a child-like android "ibuki''. Several android behaviors are created by implementing this framework on ibuki.
Tiger Leap for digital turn in the Estonian education

This article gives an overview of how Estonia has designed and implemented initiatives to equip its young people with digital skills starting from the 1990s when the Tiger Leap program was initiated. The overview will feature the state of affairs for implementing technology into education as part of the National Lifelong Learning Digital Turn program as of spring 2019. The article was written on the basis of a keynote presentation on ICEM 2018 Tallinn conference in Estonia under the name “Digital turn in Estonian Education Strategy.” This overview is a more comprehensive survey with references to sources. There is also new material added that has become available compared to 2018 September.
Survey on Machine Learning-Based Clustering Algorithms for IoT Data Cluster Analysis

Clustering the data is the first approach toward data analysis. Nowadays, the Internet of Things (IoT) produces data enormously and continuously. Majority of the machine learning-based clustering is proposed and implemented in large data integration and analysis. However, these can deal with only static and medium-sized data at a time. While dealing with big and dynamic data, those algorithms should not produce the encourageable results in terms of accuracy, reliability, and scalability. Therefore, in this paper, we study the traditional and advanced clustering algorithms for IoT data analysis in an incremental way. The clustering applications and clustering metrics are present for performing analysis and implementations. These algorithms outperform while comparing with state-of-the-art algorithms.
Pepper's Ghost and the augmented reality of modernity

Abstract The emergence of augmented-reality (AR) displays has inspired scholarship examining the social effects and communicative impacts of these visual technologies. But the broader concept of reality augmentation – of overlaying information onto everyday experience – has been likened to the imposition of social discourses and ideologies. This article examines the nineteenth-century stage illusion Pepper's Ghost as an early AR media system in service to the particular ideological mission of the Royal Polytechnic Institution in London. Despite its spectral imagery and historical parallels to spiritualism, Pepper's Ghost was instrumental in servicing the Polytechnic's goals of promoting rational modernity and scientific progress, which it accomplished by mediating the epistemic divide between superstition and science and blending Polytechnic ideology with the phenomenological experience of the screened image. In this article, I make visible the ideological function of two apparatuses: the Pepper's Ghost illusion as a media system, and the social institution of the Polytechnic itself. In the end, I situate the current revival of Pepper's Ghost stagings as a twenty-first-century phenomenon amid new restagings of Pepper's Ghost as performing 'holograms'.
Making the Invisible Visible: Illuminating the Hidden Histories of the World War I Tunnels at Vauquois Through a Hybridized Virtual Reality Exhibition

Visualizing History: Tunnels of Vauquois is an educational immersive virtual reality (VR) exhibit that makes the invisible history of World War I soldiers' experiences in the tunnels of Vauquois, France, visible to contemporary audiences. The exhibit presents the visitor with both discrete knowledge and the opportunity for emotional awareness. The virtual environment is recreated from scanned data of the original site. Visitors traverse the tunnel through the use of a head-mounted display, redirected walking, passive haptics, aligned physical and virtual environments, interactive tracked props, and narration. In this article, we describe the motivation and rationale for creating an immersive exhibit for informal learning environments, such as museums, libraries, and school VR laboratories. We also describe how the current exhibit functions and our iterative design process informed by observational feedback from pilot testing.
Design and Implementation of Multi Knowledge Base Expert System Using the SQL Inference Mechanism for Herbal Medicine

Abstract The expert system is part of artificial intelligence with basic knowledge which is built to transfer its ability into computer system. In this study, the expert system is a combination of two technologies namely health technology as a basic of knowledge and information technology as interface of patient with health expert. The knowledge of health expert about diseases and herbal medicine is implemented in artificial intelligence to support in making a decision. This multi knowledge expert system is using the SQL Inference mechanism. The multi knowledge concept is made with database design, so that the expert system in this study is able to diagnosis many diseases and has more solutions in health problems. This study is using herbal medicine in order to prevent its users from poisoning and allergic of synthetic medicines.
Single-photon quantum router based on asymmetric intercavity couplings

As a special quantum node in a quantum network, the quantum router plays an important role in storing and transferring quantum information. In this paper, we propose a quantum router scheme based on asymmetric intercavity couplings and a three-level Λ-type atomic system. This scheme implements the quantum routing capability very well. It can perfectly transfer quantum information from one quantum channel to another. Compared with the previous quantum routing scheme, our proposed scheme can achieve the transfer rate of single photons from one quantum channel to another quantum channel reaching 100%, the high transfer rate is located in the almost quadrant regions with negative values of the two variables λa and λb, and their maximum values emerge in the center point λa = λb = −1. Therefore, it is possibly feasible to efficiently enhance the routing capability of the single photons between two channels by adjusting the inter-resonator couplings, and the asymmetric intercavity coupling provides a new method for achieving high-fidelity quantum routers.
Normalization-based Neighborhood Model for Cold Start Problem in Recommendation System

Existing approaches for Recommendation Systems (RS) are mainly based on users’ past knowledge and the more popular techniques such as the neighborhood models focus on finding similar users in making recommendations. The cold start problem is due to inaccurate recommendations given to new users because of lack of past data related to those users. To deal with such cases where prior information on the new user is not available, this paper proposes a normalization technique to model user involvement for cold start problem or user likings based on the details of items used in the neighborhood models. The proposed normalization technique was evaluated using two datasets namely MovieLens and GroupLens. The results showed that the proposed technique is able to improve the accuracy of the neighborhood model, which in turn increases the accuracy of an RS.
Secured Electronic Voting Machine Using Biometric Technique with Unique Identity Number and IOT

Elections play an important role in our democratic country as people can select a person as a leader for the government. This paper was about a new proposed methodology having a highly secured process. This consists of mainly Aadhar, biometric and IOT. Aadhar ID is a unique card for everyone, biometric face recognition for security and IOT for the safe and immediate results. This proposed system has automatic counting of votes: highly data secured system, sending of data immediately and safe voting.