Reviewerly/exHarmony huggingface dataset:
Restarting the Education Model in the Digital Economy

The primary resource of a post-industrial knowledge-based society is the creativity of a personality, his/her ability to generate new knowledge and information, which directly depend on the quality of training of skilled professionals. It is education that forms the human capacity of highly educated professionals; without it, any innovative nature of development of the economy is out of the question. A dynamic model of economic development should correspond to an adaptive education system that promptly responds to the demands on the labor market and is able to reproduce in-demand professionals for the future. Solving this problem requires new educational process implementation mechanisms, a new universal set of competencies that will not depend on the trade that is learned and will make it possible for a future professional to quickly adapt to changes in the environment. In order to maintain its large share in the educational market, traditional education will need to undergo a transformation at the level of forms aimed at developing authenticity, reducing teaching to its original form, endowing it with its original content and tenor. Gamification of learning has the potential to become an efficient tool for the modernization of learning that gives a boost to self-development. Even today, we are witnessing how rapidly the market for educational gaming environments is growing; hence, it will be able to become an actual competitor to the traditional education system in the near future. As a result, educational institutions will be forced to include interesting and educational-functional gaming tools in the educational process. The authors substantiate their idea that IT technologies are able to give new momentum to the development of inclusive education, which makes it possible to ensure professional self-fulfillment for people with disabilities, give them the opportunity to feel themselves as productive members of society, to be needed in the professional area.
WITHDRAWN: High embedding capacity dual digital watermarking using stationary wavelet transform

Digital watermarking technologies are increasingly growing from ownership recognition to the recovery of the tampered digital images. This paper proposes a dual watermarking algorithm in which a robust and fragile watermark is embedded separately into standard and medical images to provide ownership identity and to locate the invisible tampered sections of the image. The redundant coefficients obtained through stationary wavelet transform (SWT) help to increase the host image holding ability of the watermark without deteriorating the imperceptibility and robustness of the watermark. The fragile watermark helps to determine the location of the tampers in the image. This scheme achieves significantly high robustness while maintaining comparably better imperceptibility than the other state of the art methods.
STUDD: a student–teacher method for unsupervised concept drift detection

Concept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem monitor the loss of predictive models. However, this approach falls short in many real-world scenarios, where the true labels are not readily available to compute the loss. In this context, there is increasing attention to approaches that perform concept drift detection in an unsupervised manner, i.e., without access to the true labels after the model is deployed. We propose a novel approach to unsupervised concept drift detection based on a student-teacher learning paradigm. Essentially, we create an auxiliary model (student) to mimic the primary model's behaviour (teacher). At run-time, our approach is to use the teacher for predicting new instances and monitoring the mimicking loss of the student for concept drift detection. In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.
The Memristor Laser

Here we demonstrate the first-ever semiconductor laser with non-volatile tuning functionality. A GaAs-on Silicon quantum dot laser is integrated with an Al <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> memristor, which results in lasing mode being manipulated by memristor states. The memristor controls the microring laser output wavelength in a non-volatile manner.
Towards a Reliable Evaluation of Local Interpretation Methods

The growing use of deep neural networks in critical applications is making interpretability urgently to be solved. Local interpretation methods are the most prevalent and accepted approach for understanding and interpreting deep neural networks. How to effectively evaluate the local interpretation methods is challenging. To address this question, a unified evaluation framework is proposed, which assesses local interpretation methods from three dimensions: accuracy, persuasibility and class discriminativeness. Specifically, in order to assess correctness, we designed an interactive user feature annotation tool to provide ground truth for local interpretation methods. To verify the usefulness of the interpretation method, we iteratively display part of the interpretation results, and then ask users whether they agree with the category information. At the same time, we designed and built a set of evaluation data sets with a rich hierarchical structure. Surprisingly, one finding is that the existing visual interpretation methods cannot satisfy all evaluation dimensions at the same time, and each has its own shortcomings.
Monocular Depth Estimation Using Encoder-Decoder Architecture and Transfer Learning from Single RGB Image

Depth estimation from a single RGB image has been one of the most important research topics in recent days as it has several important applications in self-supervised driving in autonomous cars, image reconstruction, and scene segmentation. Depth estimation from a single monocular image has been challenging as compared to stereo images due to the lack of spatio-temporal features per frame that makes 3D depth perception easier. Existing models and solutions in monocular depth estimation often resulted in low resolution and blurry depth maps and often fail to identify small object boundaries. In this paper, we propose a simple encoder-decoder based network that can predict high-quality depth images from single RGB images using transfer learning. We have utilized important features extracted from pre-trained networks, and after initializing the encoder with fine-tuning and important augmentation strategies, the network decoder part computes the high-end depth maps. The network has fewer trainable parameters and small iterations, though it outperforms the existing state-of-the-art methods and captures accurate boundaries when evaluated on two standard datasets, KITTI, and NYU Depth V2.
A Real-time Interactive Visualizer for Large Classroom

In improving the teaching and learning experience in a classroom environment, it is crucial for a teacher to have a fair idea about the students who need help during a lecture. However, teachers of large classes usually face difficulties in identifying the students who are in a critical state. The current methods for classroom visualization are limited in showing both the status and location of a large number of students in a limited display area. Additionally, comprehension of the states adds cognitive load on the teacher working in a time-constrained classroom environment. In this article, we propose a two-level visualizer for large classrooms to address the challenges. In the first level, the visualizer generates a colored matrix representation of the classroom. The colored matrix is a quantitative illustration of the status of the class in terms of student clusters. We use three colors: red, yellow, and green, indicating the most critical, less critical, and the normal cluster on the screen, respectively. With tap/click on the first level, detailed information for a cluster is visualized as the second level. We conducted extensive studies for our visualizer in a simulated classroom with 12 tasks and 27 teacher participants. The results show that the visualizer is efficient and usable.
SALKG: A Semantic Annotation System for Building a High-quality Legal Knowledge Graph

Knowledge graph has become an essential tool for semantic analysis with the development of natural language processing and deep learning. A high-quality knowledge graph is handy for building a high-performance knowledge-driven application. Despite recent advances in information extraction (IE) techniques, no suitable automated methods can be applied to constructing a domain-specific, comprehensive, and high-quality knowledge graph. However, a semi-automatic strategy, which can ensure the basic quality requirements of a knowledge graph, has been successfully implemented in the elementary science domain. This paper presents a semantic annotation system developed for building a high-quality legal knowledge graph (SALKG) using the semi-automatic strategy. We introduce its system design, architecture, algorithms, functions, and implementation. To investigate the effectiveness of SALKG, we conduct a preliminary annotation experiment with 280 legal texts which were collected from the Harvard Caselaw Access Project. The user evaluation from 32 graduate students demonstrates the high usability of SALKG in semantic annotation and the potential for building a high-quality legal knowledge graph. The system can also be adapted to other fields for constructing domain-specific knowledge graphs.
Broadening Differential Privacy for Deep Learning Against Model Inversion Attacks

Deep learning models have achieved great success in many real-world tasks such as image recognition, machine translation, and self-driving cars. A large amount of data are needed to train a model, and in many cases, the training data are private. Publishing or sharing a deep learning model trained on private datasets could pose privacy concerns. We study the model inversion attacks against deep learning models, which attempt to reconstruct the features of training data corresponding to a given class given access to the model. While deep learning with differential privacy is state-of-the-art for training privacy-preserving models, whether they can provide meaningful protection against model inversion attacks remains an open question. In this paper, we first improve the existing model inversion attacks (MIA) to successfully reconstruct training images from neural network based image recognition models. Then, we demonstrate that deep learning with the standard record-level differential privacy does not provide quantifiable protection against MIA. Subsequently, we propose class-level and subclass-level differential privacy and develop algorithms to provide a quantifiable privacy guarantee against MIA. Experiments on real datasets demonstrate that our proposed privacy notions and mechanisms can effectively defend against MIA while maintaining model accuracy.
An Adversarial Attack Defending System for Securing In-Vehicle Networks

In a modern vehicle, there are over seventy Electronics Control Units (ECUs). For an in-vehicle network, ECUs communicate with each other by following a standard communication protocol, such as Controller Area Network (CAN). However, an attacker can easily access the in-vehicle network to compromise ECUs through a WLAN or Bluetooth. Though there are various deep learning (DL) methods suggested for securing in-vehicle networks, recent studies on adversarial examples have shown that attackers can easily fool DL models. In this research, we further explore adversarial examples in an in-vehicle network. We first discover and implement two adversarial attack models that are harmful to a Long Short Term Memory (LSTM)-based detection model used in the in-vehicle network. As shown in our experiments, adversaries can attack the LSTM-based detection model with a success rate of over 98%. Then, we propose an Adversarial Attack Defending System (AADS) for securing an in-vehicle network. Specifically, we focus on brake-related ECUs in an in-vehicle network. Our extensive experimental results demonstrate that the proposed AADS achieves over 99 % accuracy for detecting adversarial attacks.
Quantum detector tomography of a high dynamic-range superconducting nanowire single-photon detector

Abstract We demonstrate and verify quantum detector tomography of a superconducting nanowire single-photon detector (SNSPD) in a multiplexing scheme which permits measurement of up to 71 000 photons per input pulse. We reconstruct the positive operator valued measure (POVM) of this device in the low photon-number regime, and use the extracted parameters to show the POVMs spanning the whole dynamic range of the device. We verify this by finding the mean photon number of a bright state. Our work shows that a reliable quantum description of large-scale SNSPD devices is possible, and should be applicable to other multiplexing configurations.
Bayesian network based general correspondence retrieval method for depth sensing with single-shot structured light

This study reinvestigated one of the most fundamental problems in structure light depth sensing field: correspondence retrieval of features between patterns and images. We formulate the global optimum correspondence retrieval by maximizing a conditional probability of correspondence given observed features, which is depicted by a Bayesian network. Different from traditional “code-only” based correspondence retrieval methods, the proposed Bayesian network based method exploits the positional correlations of correspondences of neighboring features, namely, the correspondences of poorly detected features are estimated with the aid of the correspondences of well detected features. The method performs especially well on challenging scenes with rich depth variations, abrupt depth changes, edges, etc. Experiments show that the proposed method increase the correspondence accuracy by about 40% on challenging scenes, compared with traditional “code-only” based correspondence retrieval methods.
Modern technologies in the development of professional competence in teachers from professional (vocational) education schools

Abstract The article discloses the peculiarities of developing professional competence in professional training teachers. This process mostly involves using traditional pedagogical technologies. The article presents results of the ascertaining experiment and indicates the need for continuing professional development of teachers based on innovative principles. It also suggests using up-to-date pedagogical technologies of distance and project-based learning and production technologies to develop professional competence in actors in the educational process (teachers and students). This refers to the implementation of a competence-based approach in the professional education system. It is reflected in the development of professional competence based on some steps. They include expanding informational and educational space for actors in the educational process and individualizing learning using modern information resources (distance technologies); ensuring the interaction between actors in the educational process, as well as the joint realization of searching, cognitive and research activities (project-based technologies); organizing the educational process under the social partnership, taking into account the timely response of the professional education system to the emergence of innovations in production and the service sector (production technologies).
CICERONE- A Real Time Object Detection for Visually Impaired People

Abstract Our work provides a solution to people who are suffering from partial blindness due to diseases of the eye or due to accidents. Unlike people who are born blind, they depend a lot on other people for doing their day to day activities. Our project is a boon to such people as our end product; a smart walking stick detects the trained objects that are used daily by the person and intimates the person using audio messages. This project changes the visual world into an audio world by informing the visually impaired of the objects in their environment. These people can use this particular prototype for self -navigating their way. A YOLO (You look only once) algorithm is used in our project. Real-time objects in an image are detected with their names represented on a bounding box and these names are converted to speech signals. The conversion to audio signals is done by using an e-Speak tool which forms Googles Text to Speech (gTTS) system. The prototype consists of several modules. The Raspberry Pi camera module takes the image and transfers it to the Raspberry Pi desktop. Then, real-time object detection is carried out by using YOLO network. The detected image is converted to speech by using the gTTS module and the audio result is provided to the user through a headset. For achieving the required portability, the battery backup is being used.
Credit Card Fraud Is a Computer Security Problem

Most apps and merchants do not want to deal with financial fraud, but, if they accept payments, they will eventually have to. Our position is that credit card fraud prevention is a technical problem that needs technical solutions.
Evolving convolutional autoencoders using multi-objective Particle Swarm Optimization

Convolution autoencoders have shown that they have useful applications in multiple areas related to image classification and generation. The architecture of deep neural networks has a huge impact on the performance of the network. In recent years, metaheuristics have become quite popular for the optimization of the architectures of deep neural networks. In this paper, we propose a multi-objective Particle Swarm Optimization (PSO) for designing flexible convolution autoencoders by evolving the arrangement of convolution and pooling layers along with the number of parameters. A novel encoding strategy for PSO particles is introduced, which allows flexible positioning of pooling layers. To enhance the exploration of the search space, velocity and the position update methods for variable-length particles in PSO are also modified. The proposed method is evaluated on different datasets for image classification. The experimental results show that the resultant architectures are more generalized, optimized, and accurate than their competitors.
How to predict choice using eye-movements data?

In recent decades, eye-movement detection technology has improved significantly, and eye-trackers are available not only as standalone research tools but also as computer peripherals. This rapid spread gives further opportunities to measure the eye-movements of participants. The current paper provides classification models for the prediction of food choice and selects the best one. Four choice sets were presented to 112 volunteered participants, each choice set consisting of four different choice tasks, resulting in altogether sixteen choice tasks. The choice sets followed the 2-, 4-, 6- and 8-alternative forced-choice paradigm. Tobii X2-60 eye-tracker and Tobii Studio software were used to capture and export gazing data, respectively. After variable filtering, thirteen classification models were elaborated and tested; moreover, eight performance parameters were computed. The models were compared based on the performance parameters using the sum of ranking differences algorithm. The algorithm ranks and groups the models by comparing the ranks of their performance metrics to a predefined gold standard. Techniques based on decision trees were superior in all cases, regardless of the choice tasks and food product categories. Among the classifiers, Quinlan's C4.5 and cost-sensitive decision trees proved to be the best-performing ones. Future studies should focus on the fine-tuning of these models as well as their applications with mobile eye-trackers.
Training pre-service mathematics teacher to use mnemonic techniques

Abstract The article reveals the issue of the appropriateness of training pre-service mathematics teachers to use the techniques of mnemonics in professional activities. This issue is caused by the intensification of the educational process, when the amount of information accumulated by mankind is many times greater than the amount of knowledge that can be learned by a particular person. It is established that in the process of teaching mathematics, mnemonics should be used as a way of perceiving new information due to the formation of associative connections using special methods and techniques. The expediency of training pre-service mathematics teachers to use various methods of mnemonics (“Binding”, “Transformation”, “Amplification”) is substantiated. A positive attitude of mathematics teachers towards the use of mnemonics techniques was revealed, as well as a low level of students' understanding of the advisability of using mnemonics techniques in professional activities. The classification of software used to create visual models is presented. A training was developed and introduced into the practice of university education on the development of mnemonic techniques for the presentation of educational mathematical material. The prospects of scientific research through the development of methodological support for the training of pre-service mathematics teachers to use the techniques of mnemonics in professional activities are determined.
Influences of a Virtual Reality Experience on Dissociation, Mindfulness, and Self-Efficacy

An experiment on a nonclinical sample to investigate influences of virtual reality (VR) on dissociation, mindfulness, and self-efficacy is reported. The participants were immersed for 20 minutes with a VR system in a large virtual apartment and have been charged to look around and find a set of objects previously observed on pictures. Questionnaires pre- and postimmersion were administered to measure the levels of such psychological constructs; cybersickness was assessed as well. Results showed an upward trend in dissociation and a significant increase of mindfulness after immersion in the VR environment. No influences on self-efficacy were found. Findings are discussed with reference to past studies, where a different picture emerged, by considering possible differences due to changes in technology and context.
Unified monogamy relation of entanglement measures

The monogamy of quantum entanglement captures the property of limitation in the distribution of entanglement. Various monogamy relations exist for different entanglement measures that are important in quantum information processing. Our goal in this work is to propose general monogamy inequalities for several entanglement measures on entangled qubit systems. The present results provide a unified model for various entanglement measures including the concurrence, the negativity, the entanglement of formation, Tsallis-q entropy, Rényi-q entropy, and Unified-(q, s) entropy. We then propose tightened monogamy inequalities for multipartite qubit systems and derive upper bounds of aforementioned entanglement measures for multipartite pure state under generalized bipartitions. We finally prove a generic result that most of tripartite high-dimensional entangled pure states have no entanglement monogamy. These results are useful for exploring the entanglement theory, quantum information processing and secure quantum communication.