Reviewerly/exHarmony huggingface dataset:
The investigation of the problems of the digital competences formation for Industry 4.0 workforce

The article describes results of a research performed by its authors at Peter the Great St. Petersburg Polytechnic University with the main purpose to determine the status of usage of digital technologies and state-of-the-art communication means by students while studying, and also to make conclusions on achievements in digital competences formation and readiness of university students to work for the future digital industry. The research was conducted on the basis of questionnaire prepared by the authors of the article comprising three sections and including 18 questions. The total number of respondents made 187 persons from two University institutes. Analysis of the results obtained was made both on the whole selection and on the areas of focus and students' readiness levels. Conclusions were made regarding the substantial part of students who are conservative toward up-to-date Information and Communication technologies, and, consequently, the insufficient level of digital competences of these students that may cause problems in the future, during Industry 4.0 companies staffing. Recommendations on solving the problem identified are given in the article.
Dense small face detection based on regional cascade multi‐scale method

In the field of object detection, the research on the problem of detecting small face is the most extensive, but when there are objects with obvious scale differences in the image, the detection performance is not obvious, which is due to the scale invariance properties of the deep convolutional neural networks. Although in recent years, there have been some methods proposed to solve this problem such as FPN and SNIP, which is based on feature pyramid. However, they have not fundamentally solved the problem. A regional cascade multi-scale detection method has been proposed. First, a global detector and several local detectors have been trained, respectively. The global detector is trained by the original training set, while the local detector is trained by the sub-training set generated by the original training set. Second, the global detector can detect object roughly and the local detectors can produce more detailed results that improve the performance of global detector. Finally, to integrate the detection results of global detector and local detectors as the output, non-maximum suppression methods are used. The method can be carried in any depth model of object detection, has good scalability, and is more suitable for dense face detection.
Boosting segmentation with weak supervision from image-to-image translation.

In many cases, especially with medical images, it is prohibitively challenging to produce a sufficiently large training sample of pixel-level annotations to train deep neural networks for semantic image segmentation. On the other hand, some information is often known about the contents of images. We leverage information on whether an image presents the segmentation target or whether it is absent from the image to improve segmentation performance by augmenting the amount of data usable for model training. Specifically, we propose a semi-supervised framework that employs image-to-image translation between weak labels (e.g., presence vs. absence of cancer), in addition to fully supervised segmentation on some examples. We conjecture that this translation objective is well aligned with the segmentation objective as both require the same disentangling of image variations. Building on prior image-to-image translation work, we re-use the encoder and decoders for translating in either direction between two domains, employing a strategy of selectively decoding domain-specific variations. For presence vs. absence domains, the encoder produces variations that are common to both and those unique to the presence domain. Furthermore, we successfully re-use one of the decoders used in translation for segmentation. We validate the proposed method on synthetic tasks of varying difficulty as well as on the real task of brain tumor segmentation in magnetic resonance images, where we show significant improvements over standard semi-supervised training with autoencoding.
Automated Essay Evaluation Based on Fusion of Fuzzy Ontology and Latent Semantic Analysis

New learning researches proved that creativity is an essential concern in the arena of education. The best means to evaluate learning outcomes and students’ creativity is essay questions. However, to evaluate these questions is a time-consuming task and subjectivity in scoring assessments remains inevitable. Automated essay evaluation systems (AEE) provide a cost-effective and consistent alternative to human marking. Therefore, numerous automatic essay-grading systems have been developed to lessen the demands of manual essay grading. However, these systems concentrate on syntax and vocabulary, and no consideration is paid to the semantic and coherence of the essay. Moreover, few of the existing systems are able to give informative feedback that is based on extensive domain knowledge to students. In this paper, a system is evolved that uses latent semantic analysis (LSA) and fuzzy ontology to evaluate essays, where LSA will be responsible for checking the semantic. Fuzzy ontology is used to check the consistency and coherence of the essay as it is the best way to overcome the vagueness of the language, and the system will also provide a score with feedback to the student. Experimental results were good in evaluating the essay syntactically and semantically.
Wavelet transform associated with linear canonical Hankel transform

The main goal of this paper is to study about the continuous as well as discrete wavelet transform in terms of linear canonical Hankel transform (LCH‐transform) and discuss some of its basic properties. Parseval's relation and reconstruction formula of continuous linear canonical Hankel wavelet transform (CLCH‐wavelet transform) is obtained. Moreover, semidiscrete and discrete LCH‐wavelet transform are also discussed.
Development of an Active High-Speed 3-D Vision System

High-speed recognition of the shape of a target object is indispensable for robots to perform various kinds of dexterous tasks in real time. In this paper, we propose a high-speed 3-D sensing system with active target-tracking. The system consists of a high-speed camera head and a high-speed projector, which are mounted on a two-axis active vision system. By measuring a projected coded pattern, 3-D measurement at a rate of 500 fps was achieved. The measurement range was increased as a result of the active tracking, and the shape of the target was accurately observed even when it moved quickly. In addition, to obtain the position and orientation of the target, 500 fps real-time model matching was achieved.
Data trusts: more data than trust? The perspective of the data subject in the face of a growing problem

The recent report, Growing the Artificial Intelligence Industry in the UK (Hall, Wendy, and Jerome Pesenti. 2017, https://www.gov.uk/government/publications/growing-the-artificial-intelligence-industry-in-the-uk) suggests the use of a 'data trust' to facilitate data sharing. Whilst government and corporations are focusing on their need to facilitate data sharing, the perspective of many individuals is that too much data is being shared. The issue is not only about data, but about power. The individual does not often have a voice when issues relating to data sharing are tackled. Regulators can cite the 'public interest' when data governance is discussed, but the individual's interests may diverge from that of the public. This paper considers the data subject's position with respect to data collection leading to considerations about surveillance and datafication. Proposals for data trusts will be considered applying principles of English trust law to possibly mitigate the imbalance of power between large data users and individual data subjects. Finally, the possibility of a workable remedy in the form of a class action lawsuit which could give the data subjects some collective power in the event of a data breach will be explored. Despite regulatory efforts to protect personal data, there is a lack of public trust in the current data sharing system.
Security analysis for chaotic maps-based mutual authentication and key agreement using smart cards for wireless networks

The authentication of mobile users and establishing a shared key for wireless networks is becoming more emerging, as the number of users who want to roam into a foreign network to use a service increases as well as the increase in the number of such networks. Recently, a number of such protocols are proposed which their security rely on modular exponentiations or scalar multiplications over elliptic curves. In 2013, C. Guo et al. proposed a novel such protocol using a chaos-based key-establishment algorithm which was proposed by L. Kocarev and Z. Tasev in 2003. Their protocol makes it possible for both mobile user and the foreign agent to be mutually authenticated by a host agent. A deeper security analysis of their protocol shows some vulnerabilities. In this paper, we will enhance this protocol by applying fixes to these vulnerabilities and state a set of security requirements for it. We will also investigate the computational complexity of the enhanced protocol and efficient ways to compute the iterations of the first type of Chebyshev polynomials.
Effect of security investment strategy on the business value of managed security service providers

Managed security service providers (MSSPs) have long provided clients with cost-effective methods and professional solutions for addressing issues related to information security. MSSPs provide three categories of security services, namely, prevention, detection, and response, to satisfy their clients’ security requirements and realize business value. This study develops a system dynamics model of the correlation between the security investment strategies of an MSSP and the effect of its business value. Simulations under opportunistic and targeted attacks are performed to discuss the effects of the various security investment strategies of an MSSP on its business value. The study results indicate that investing in prevention has a stronger effect on the business value of an MSSP than investing in detection and response and that security investments on opportunistic attacks are more efficient than those on targeted attacks. Sensitivity analysis shows the robustness of the system dynamics model proposed in this study.
A Light-Weight White-Box Encryption Scheme for Securing Distributed Embedded Devices

Distributed embedded devices are widely used in sensor networks and the Internet of Things for gathering and sending data. Many of them are deployed in an unattended manner (e.g., sensor nodes and tag readers), while others may be easily lost (e.g., smart wristbands and watches). These distributed embedded devices could be potentially captured and accessed in an unauthorized manner due to their physical natures. From a security perspective, they are typically working in the white-box attack context, where adversaries have total visibility on the implementations of built-in cryptosystems and full control over their execution processes. It is undoubtedly a significant challenge to deal with white-box attacks on these devices. Existing encryption algorithms for white-box attack contexts require large memory footprint and thus are not suitable for resource- constrained embedded devices. To address this challenge, we propose a novel light-weight encryption scheme for protecting data confidentiality. The encryption is conducted with specialized secret components, and the encryption algorithm requires a small volume of static data for storing critical information. In addition, this scheme uniquely supports efficient key-updating at very small cost. The security and the cost of the proposed scheme have been theoretically analyzed with positive results, and the extensive experimental evaluations indicate that the new scheme satisfies the requirements of distributed embedded devices in terms of limited memory usage and low computational cost.
Predicting the helpfulness of game reviews: A case study on the Steam store

Online user reviews play an important role in the assessment of product quality, and thus these reviews should be evaluated carefully. This study evaluates the helpfulness of game reviews on the online Steam store. It collects a large set of user reviews of different game genres and builds a classi fication model to predict whether these reviews are helpful or not. This model can accurately predict the helpfulness of the reviews based on different thresholds. This work also investigates various types of textual and word embedding features and analyzed their importance for predictions. Furthermore, it develops a regression-based model that can predict the score or rating of game reviews on Steam.
Integrated navigation for Mars final approach based on Doppler radar and X-ray pulsars with atomic clock error

Final approach phase has been recognized as one of the key phases in Mars landing progress. The performance of the navigation system in this phase determines the positioning accuracy of the atmospheric entry point, which further influences the actualization of Mars high-precision pinpoint landing. Regarding this phase, conventional navigation strategies rely on the Earth-based measurement whose time delay is serious due to the long distance between the spacecraft and the ground telemetering station. Therefore, an X-ray pulsars and Doppler radar integrated navigation algorithm is proposed in the current study to ensure the real-time measurement information acquisition and to enhance the reliability of the navigation system. The non-Gaussian bias error of the atomic clock is also considered in the measurement model to improve the accuracy of estimation results. Meanwhile, the augmented state rank Kalman filter (ARKF) is introduced to match the new nonlinear navigation model involving non-Gaussian noises. Numerical simulation results show that the proposed navigation algorithm can make full use of the navigation information provided by X-ray pulsars and Doppler radar, and can effectively reduce the influence of atomic clock errors and guarantee a better performance than traditional methods.
A Hybrid Modeling Approach for an Automated Lyrics-Rating System for Adolescents

The South Korean government operates human-based lyrics-rating systems to reduce adolescents’ exposure to inappropriate songs. In this study, we developed lyrics classification models for an automated lyrics-rating system for adolescents. There are two kinds of inappropriate lyrics for adolescents: (1) lyrics with inappropriate words and (2) lyrics with inappropriate content based on the semantic context. To tackle the first issue, we propose $$ {\text{logCD}}_{\alpha } $$ as a method for generating a lexicon of inappropriate words. It attained the highest performance among the lexicon-based filtering methods examined. Further, to deal with the second issue, we propose a hybrid classification model that combines $$ {\text{logCD}}_{\alpha } $$ with an RNN based model. The hybrid model composed of a ‘lexicon-checking model’ and a ‘context-checking model’ achieved the highest performance among all of the models examined, highlighting the effectiveness of combining the models to specifically target each of the two types of inappropriate lyrics.
Uses of Virtual Reality for Communication in Financial Services: A Case Study on Comparing Different Telepresence Interfaces: Virtual Reality Compared to Video Conferencing

This paper examines the use of Virtual Reality to conduct meetings online and compares them with an approach using traditional video teleconferencing software. The research was conducted using Virtual Reality meeting software and Skype for Business as test environments to compare a Virtual Reality meeting versus a Video conferencing environment. The results point to the improved feelings of presence, closeness, and arousal for Virtual Reality environments, but also demonstrate a difference in the experience for female and male participants. In particular, the use of Avatar’s instead of real-life video images of the participants was preferred by female participants which points to a possible de-biasing ability for Virtual Reality environments. The results also indicate a need to explore different interfaces designs and discuss the lessons learnt from the historical adoption of technology by the financial services community.
Attack Defense Trees with Sequential Conjunction

Attack defense trees are used to show the interaction between potential attacks on a system and the system defenses.In this paper we present a formal semantic model for attack defense trees with sequential composition, allowing for the description of attacks that are performed as a sequence of steps.The main contributions of our work are a formal representation of attack defense trees with sequential conjunction, a demonstration that this representation is equivalent to a process-algebraic one, and an algorithm for identifying the existence of attacks.We illustrate with an attack on over the air updates.
A Smart Device Using Low-Cost Sensors to Detect Earthquakes

Due to the significant development of hardware and software technologies in mobile and embedded computing, small hand-held devices such as smartphones have been used to detect earthquakes. In the past few years, there were efforts to detect earthquakes using a low-cost acceleration sensors inside a smartphone. However, it is not only costly to use a smartphone for merely detecting earthquakes, but also a waste of computing resources because smartphones comes with a powerful CPU, plentiful memory, and other auxiliary sensors. Also, a smartphone always needs to be connected to Internet. In this paper, we introduce a stand-alone earthquake detection device equipped with an acceleration sensor and Wi-Fi, First, we systematically evaluated a set of acceleration sensors by assessing their performance and accuracy to select the most suitable acceleration sensor, and then developed an designated device that can detect earthquakes and send alerts to nearby devices. Furthermore, to distinguish earthquakes from daily motions, we employed a Artificial Neural Network(ANN) technique with the earthquake dataset obtained from the Pohang earthquake in South Korea, 2017. Our result shows that a low-cost acceleration sensor can be used to detect an earthquake, thereby enhancing the public safety of communities vulnerable to earthquakes.
Bi-Level Thresholding

In gesture recognition, one challenge that researchers and developers face is the need for recognition strategies that mediate between false positives and false negatives. In this article, we examine bi-level thresholding, a recognition strategy that uses two thresholds: a tighter threshold limits false positives and recognition errors, and a looser threshold prevents repeated errors (false negatives) by analyzing movements in sequence. We first describe early observations that led to the development of the bi-level thresholding algorithm. Next, using a Wizard-of-Oz recognizer, we hold recognition rates constant and adjust for fixed versus bi-level thresholding; we show that systems using bi-level thresholding result in significantly lower workload scores on the NASA-TLX and significantly lower accelerometer variance when performing gesture input. Finally, we examine the effect that bi-level thresholding has on a real-world dataset of wrist and finger gestures, showing an ability to significantly improve measures of precision and recall. Overall, these results argue for the viability of bi-level thresholding as an effective technique for balancing between false positives, recognition errors, and false negatives.
Towards Understanding User Preferences for Explanation Types in Model Reconciliation

Recent work has formalized the explanation process in the context of automated planning as one of model reconciliation - i.e. a process by which the planning agent can bring the explainee's (possibly faulty) model of a planning problem closer to its understanding of the ground truth until both agree that its plan is the best possible. The content of explanations can thus range from misunderstandings about the agent's beliefs (state), desires (goals) and capabilities (action model). Though existing literature has considered different kinds of these model differences to be equivalent, literature on the explanations in social sciences has suggested that explanations with similar logical properties may often be perceived differently by humans. In this brief report, we explore to what extent humans attribute importance to different kinds of model differences that have been traditionally considered equivalent in the model reconciliation setting. Our results suggest that people prefer the explanations which are related to the effects of actions.
Examining PBKDF2 security margin—Case study of LUKS

Passwords are widely used to protect our sensitive information or to gain access to specific resources. They should be changed frequently and be strong enough to prevent well-known attacks. Unfortunately, user-chosen passwords are usually short and lack sufficient entropy. A possible solution to these problems is to adopt a Key Derivation Function (KDF) that allows legitimate users to spend a moderate amount of time on key derivation, while imposing CPU/memory-intensive operations on the attacker side. In this paper, we focus on long-term passwords secured by the Password-Based Key Derivation Function 2 (PBKDF2) and present the case study of Linux Unified Key Setup (LUKS), a disk-encryption specification commonly implemented in Linux based operating systems. In particular, we describe how LUKS protects long-term keys by means of iteration counts defined at runtime, and analyze how external factors may affect the iteration counts computation. In doing so, we provide means of evaluating the iteration count values defined at run-time and experimentally show to what level PBKDF2 is still capable of providing sufficient security margin for a LUKS implementation.
Experimental evaluation of a camera rig extrinsic calibration method based on retro-reflective markers detection

Nowadays, Motion Capture (MC) systems are used more and more to evaluate the accuracy of Visual Odometry and visual Simultaneous Localization and Mapping (SLAM) algorithms. However, the misalignment between the camera optical frame and the camera body frame, as tracked by a MC system, leads to a drift between the reconstructed trajectory and the reference one. In this work, we present a calibration method which estimates the relative orientation and position between this two reference frames. The proposed method is highly efficient because it uses a calibration target composed by a set of retro-reflective markers which are tracked by the MC system itself. Three segmentation methods and two different optimization approaches have been tested. The uncertainty propagation analysis, performed by means of a Monte Carlo simulation, shows that it is possible to calibrate the extrinsic parameters of a stereo-camera with a position accuracy of one millimeter and an orientation accuracy better than 1°.