Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Approaches to Teaching Students with Hearing Impairments and Students without Disabilities at a Technical University

This paper provides a comparative analysis of approaches to teaching natural sciences disciplines on the example of chemistry to hearing impaired students and students without disabilities. Hearing impaired students make up the most difficult category of students at university of a general type with a verbal form of education due to their hearing impairment and concomitant disturbances in body functions that cause their disability. The key problem of their training at technical university is the limited content accessibility of the disciplines of the natural science cycle, including chemistry. They can master these disciplines only as subjects of adapted (inclusive) basic professional educational programs with creating special (distinctive) learning conditions for them. The authors developed and tested a methodological approach to solving the problem of content accessibility of educational resources in chemistry for hearing impaired students at technical university by creating special conditions for their mastering, taking into account their limitations. Particular educational conditions include support of hearing impaired students by additional adaptational course of chemistry with a focus on individualized teaching applying a number of cognitive technologies. In this regard, the offered approach is compensatory in nature. It allows teachers to identify the individual educational and rehabilitation needs of students with hearing impairment and meet those using organizational, technological and pedagogical solutions, thus leveling their educational opportunities with ones of students without disabilities. Due to the use of assistive tools and technologies compatible with the educational environment, it is possible to make it accessible to various categories of students.
Analysis of English teaching based on convolutional neural network and improved random forest algorithm

At present, English teaching does not play the role of a smart classroom, and it is difficult to grasp the student status and characteristics in real time in actual teaching. Based on this, starting from the video image and static image and the actual situation of English classroom teaching, this study, based on the convolutional neural network and random forest algorithm, performs static image human behavior recognition under different image representation conditions, and studies the influence of background information of image and spatial distribution information of image features on recognition accuracy. Then, based on the similarity between different behavior classes, a static image human body behavior recognition method based on improved random forest is proposed. In addition, through theoretical research, an algorithm model that can identify the characteristics of English classrooms is constructed, and the static and dynamic images of English teaching are taken as an example to conduct experimental analysis. The research shows that the proposed method has certain effects and can provide theoretical reference for subsequent related research.
Graded-index optical fiber transverse-spatial-mode entanglement

We present a study of spontaneously arisen spatially entangled photon pairs via intermodal four-wave mixing in a graded-index multimode optical fiber. Unique dispersive features of the fiber allow spectral indistinguishability of two different phase-matched processes, producing entangled pairs of spatial qubits. The bases are realized as superpositions of orthogonal transverse fiber modes having opposite parities. In particular, we take into consideration the spectral properties of the processes by examining their joint spectral amplitudes. It is shown that illuminating graded-index optical fiber with different pump wavelengths has an impact upon efficiency parameters accordingly the degree of spatial entanglement and gives rise to photon pairs with various spectral purities. Photons with higher spectral purity enable desired single-photon based interactions to take place, whereas photons with lower spectral purity exhibit hybrid entanglement in frequency and transverse mode. We also discuss Wigner function formalism and parity-displacement-based realization to characterize spatial properties of the states, as well as to verify quantum entanglement through a violation of Clauser-Horne-Shimony-Holt inequality.
RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight

Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this paper, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight. A path-guided optimization (PGO) approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce a perception-aware planning strategy to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively. We will release our implementation as an open-source package for the community.
Design of Single-Bit Fault-Tolerant Reversible Circuits

This article introduces redundant design approaches for reversible circuits that have the ability to detect and tolerate single-bit fault without the need of conventional voting scheme. Experiments preformed show that the proposed scheme reduces the gate cost on average with up to 28% as compared with tri-modular redundant circuits.
Possible Models Computation and Revision – A Practical Approach

This paper describes a method of computing plausible states of a system as a logical model. The problem of analyzing state-based systems as they evolve over time has been studied widely in the automated reasoning community (and others). This paper proposes a specific approach, one that is tailored to situational awareness applications. The main contribution is a calculus for a novel specification language that is built around disjunctive logic programming under a possible models semantics, stratification in terms of event times, default negation, and a model revision operator for dealing with incomplete or erroneous events – a typical problem in realistic applications. The paper proves the calculus correct wrt. a formal semantics of the specification language and it describes the calculus’ implementation via embedding in Scala. This enables immediate access to rich data structures and external systems, which is important in practice.
Cavity-assisted controlled phase-flip gates

Cavity-mediated two-qubit gates, for example between solid-state spins, are attractive for quantum network applications. We propose three schemes to implement a controlled phase-flip gate mediated by a cavity. The main advantage of all these schemes is the possibility to perform them using a cavity with high cooperativity, but not in the strong-coupling regime. We calculate the fidelity of each scheme in detail, taking into account the most important realistic imperfections, and compare them to highlight the optimal conditions for each scheme. Using these results, we discuss which quantum system characteristics might favor one scheme over another.
Multi-fitness learning for behavior-driven cooperation

Evolutionary learning algorithms have been successfully applied to multiagent problems where the desired system behavior can be captured by a single fitness signal. However, the complexity of many real world applications cannot be reduced to a single number, particularly when the fitness (i) arrives after a lengthy sequence of actions, and (ii) depends on the joint-action of multiple teammates. In this paper, we introduce the multi-fitness learning paradigm to enable multiagent teams to identify which fitness matters when in domains that require long-term, complex coordination. We demonstrate that multi-fitness learning efficiently solves a cooperative exploration task where teams of rovers must coordinate to observe various points of interest in a specific but unknown order.
On a question of Mordell

Significance A Diophantine equation is a polynomial equation to which one seeks solutions in integers. There is a notable disparity between the difficulty of stating Diophantine equations and that of solving them. This feature was formalized in the 20th century by Matiyasevich’s negative answer to Hilbert’s tenth problem: It is impossible to tell whether some Diophantine equations have solutions or not. One need not look very far to find examples whose status is unknown. A striking example was noted by Mordell in 1953: The equation <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msup> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msup> <mml:mrow> <mml:mi>z</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:math> has the solutions <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mn>1,1,1</mml:mn> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mn>4,4</mml:mn> <mml:mo>,</mml:mo> <mml:mo>−</mml:mo> <mml:mn>5</mml:mn> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> (and permutations); are there any others? This paper concludes a 65-y search with an affirmative answer to Mordell’s question and strongly supports a related conjecture of Heath-Brown.
Design of Chinese Character Coded Targets for Feature Point Recognition Under Motion-Blur Effect

Coded targets have been widely used as a type of active visual feature points in fields such as close-range photogrammetry, robot navigation, 3D reconstruction, and augmented reality. However, coded targets in degraded images, such as images with motion blur effect, are hard to recognize. To this end, a set of novel Chinese character coded targets (CCTs) is designed and tested. A CCT is a square visual marker, shown as a relative small circular feature overlaid in the middle of a square Chinese character. A white circular ring is embedded in a black circle concentrically to serve as the circular feature, which facilitates extraction of the center point of the marker for localization. Whereas the distinctive peripheral Chinese character appearance of each CCT is utilized for identification. By synthesizing simulated CCTs with different degrees of motion blur and various postures with the real background images, a Faster Region-based Convolutional Neural Network (Faster R-CNN) is trained to locate and recognize the CCTs in motion blurred images. Experimental results on both artificial and actual motion blurred images demonstrate the superiorities of the designed CCTs as well as the proposed localization and recognition pipeline.
Vacuous standards – Subversion of the OSAC standards-development process

In the context of development of standards for forensic science, particularly standards initially developed by the U.S. Organization of Scientific Area Committees for Forensic Science (OSAC), this perspective paper raises concern about the publication of vacuous standards. Vacuous standards generally state few requirements; the requirements they do state are often vague; compliance with their stated requirements can be achieved with little effort - the bar is set very low; and compliance with their stated requirements would not be sufficient to lead to scientifically valid results. This perspective paper proposes a number of requirements that we believe would be essential in order for a standard on validation of forensic-science methods to be fit for purpose.
Diagnostics of professional competence of IT students based on digital footprint data

The article goal is to demonstrate the possibilities of the approach to diagnosing the level of IT graduates’ professional competence, based on the analysis of the student’s digital footprint and the content of the corresponding educational program. We describe methods for extracting student professional level indicators from digital footprint text data — courses’ descriptions and graduation qualification works. We show methods of comparing these indicators with the formalized requirements of employers, reflected in the texts of vacancies in the field of information technology. The proposed approach was applied at the Institute of Mathematics and Computer Science of the University of Tyumen. We performed diagnostics using a data set that included texts of courses’ descriptions for IT areas of undergraduate studies, 542 graduation qualification works in these areas, 879 descriptions of job requirements and information on graduate employment. The presented approach allows us to evaluate the relevance of the educational program as a whole and the level of professional competence of each student based on objective data. The results were used to update the content of some major courses and to include new elective courses in the curriculum.
Detecting and Understanding Online Advertising Fraud in the Wild

While the online advertisement is widely used on the web and on mobile applications, the monetary damages by advertising frauds (ad frauds) have become a severe problem. Countermeasures against ad frauds are evaded since they rely on noticeable features (e.g., burstiness of ad requests) that attackers can easily change. We propose an ad-fraud-detection method that leverages robust features against attacker evasion. We designed novel features on the basis of the statistics observed in an ad network calculated from a large amount of ad requests from legitimate users, such as the popularity of publisher websites and the tendencies of client environments. We assume that attackers cannot know of or manipulate these statistics and that features extracted from fraudulent ad requests tend to be outliers. These features are used to construct a machine-learning model for detecting fraudulent ad requests. We evaluated our proposed method by using ad-request logs observed within an actual ad network. The results revealed that our designed features improved the recall rate by 10% and had about 100,000-160,000 fewer false negatives per day than conventional features based on the burstiness of ad requests. In addition, by evaluating detection performance with long-term dataset, we confirmed that the proposed method is robust against performance degradation over time. Finally, we applied our proposed method to a large dataset constructed on an ad network and found several characteristics of the latest ad frauds in the wild, for example, a large amount of fraudulent ad requests is sent from cloud servers.
Triggers and strategies related to the collaborative information-seeking behaviour of researchers in ResearchGate

Purpose The purpose of this paper is to identify the triggers, strategies and outcomes of collaborative information-seeking behaviours of researchers on the ResearchGate social networking site. Design/methodology/approach Data were collected from the population of researchers who use ResearchGate. The sample was limited to the Ph.D. students and assistant professors in the library and information science domain. Qualitative interviews were used for data collection. Findings Based on the findings of the study, informal communications and complex information needs lead to a decision to use collaborative information-seeking behaviour. Also, easy access to sources of information and finding relevant information were the major positive factors contributing to collaborative information-seeking behaviour of the ResearchGate users. Users moved from collaborative Q&amp;A strategies to sharing information, synthesising information and networking strategies based on their needs. Analysis of information-seeking behaviour showed that ResearchGate users bridged the information gap by internalizing new knowledge, making collaborative decisions and increasing their work's visibility. Originality/value As one of the initial studies on the collaborative information-seeking behaviour of ResearchGate users, this study provides a holistic picture of different triggers that affect researchers' information-seeking on ResearchGate.
İnsansız Hava Araçları ve Uydu Görüntülerinden Elde Edilen Veri Seti ile Havaalanlarının Tespitinin Yapılmasında SSD ve Faster R-CNN Algoritmalarının Karşılaştırılması

Today, image processing has been used in many different sectors, especially in health, production and military fields, for various purposes directly in human life.The development of deep learning algorithms and starting to use of computer vision has accelerated the studies such as critical target, important location and strategic region determination especially in the military field.In this study, the airport has been determined on the landing runways.Training, test and evaluation data sets were created by using both medium and high-altitude unmanned air vehicles and satellite images.SSD-Single Shot Multibox algorithm and Faster R-CNN algorithm were used by re-training during the determination process.The results of both algorithms were evaluated within the extend of evaluation criteria such as accuracy, sensitivity, specificity, false positive rate, false negative rate, positive pred value, F score, error rate, result and training time.The image detection accuracy with SSD algorithm was 76,61%, with Faster R-CNN algorithm the image detection accuracy was 99.52% according to valuation dataset.With this study, which of the two architectures has been revealed to be successful in determining critical areas in unmanned aerial vehicles and satellite images.
Prediction of blood culture outcome using hybrid neural network model based on electronic health records

Abstract Background Blood cultures are often performed to detect patients who has a serious illness without infections and patients with bloodstream infections. Early positive blood culture prediction is important, as bloodstream infections may cause inflammation of the body, even organ failure or death. However, existing work mainly adopts statistical models with laboratory indicators, and fails to make full use of textual description information from EHRs. Methods We study the problem of positive blood culture prediction by using neural network model. Specifically, we first construct dataset from raw EHRs. Then we propose a hybrid neural network which incorporates attention based Bi-directional Long Short-Term Memory and Autoencoder networks to fully capture the information in EHRs. Results In order to evaluate the proposed method, we constructe a dataset which consists of totally 5963 patients who had one or more blood cultures tests during hospitalization. Experimental results show that the proposed neural model gets 91.23% F-measure for this task. Conclusions The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.
Teaching Computer Science and Information Technology Studies for Students of Musical and Pedagogical Specialties

The processes of informatization transform the environment of professional activity of a contemporary music teacher, cause the formation of new educational needs of students of musical and pedagogical specialties, necessitate significant changes in the activities of a music teacher related to the creation and use of digital educational resources, the widespread introduction of techniques and methods of network interaction. Music computer technology (MCT) allows for applying effectively the pedagogical experience of traditional music teaching and the new features of a modern musical computer.&#x0D;
Strength Adjustment and Assessment for MCTS-Based Programs [Research Frontier]

2048 is a single-player stochastic puzzle game. This intriguing and addictive game has been popular worldwide and has attracted researchers to develop game-playing programs. Due to its simplicity and complexity, 2048 has become an interesting and challenging platform for evaluating the effectiveness of machine learning methods. This dissertation conducts comprehensive research on reinforcement learning and computer game algorithms for 2048. First, this dissertation proposes optimistic temporal difference learning, which significantly improves the quality of learning by employing optimistic initialization to encourage exploration for 2048. Furthermore, based on this approach, a state-of-the-art program for 2048 is developed, which achieves the highest performance among all learning-based programs, namely an average score of 625377 points and a rate of 72% for reaching 32768-tiles. Second, this dissertation investigates several techniques related to 2048, including the n-tuple network ensemble learning, Monte Carlo tree search, and deep reinforcement learning. These techniques are promising for further improving the performance of the current state-of-the-art program. Finally, this dissertation discusses pedagogical applications related to 2048 by proposing course designs and summarizing the teaching experience. The proposed course designs adopt 2048-like games as materials for beginners to learn reinforcement learning and computer game algorithms. The courses have been successfully applied to graduate-level students and received well by student feedback.
Recursive Data Structures in SPARK

SPARK is both a deductive verification tool for the Ada language and the subset of Ada on which it operates. In this paper, we present a recent extension of the SPARK language and toolset to support pointers. This extension is based on an ownership policy inspired by Rust to enforce non-aliasing through a move semantics of assignment. In particular, we consider pointer-based recursive data structures, and discuss how they are supported in SPARK. We explain how iteration over these structures can be handled using a restricted form of aliasing called local borrowing. To avoid introducing a memory model and to stay in the first-order logic background of SPARK, the relation between the iterator and the underlying structure is encoded as a predicate which is maintained throughout the program control flow. Special first-order contracts, called pledges, can be used to describe this relation. Finally, we give examples of programs that can be verified using this framework.