Reviewerly/exHarmony huggingface dataset:
New Quantum BCH Codes of Length n=2(q4−1)

In this paper, narrow-sense and non-narrow-sense BCH codes of length n=2(q4−1) that contain their Hermitian duals over Fq2 are studied, where q is an odd prime power. Theory of cyclotomic coset is applied to confirm the maximum designed distances and dimensions of these BCH codes. Finally, some new quantum codes are obtained from such BCH codes, many of our quantum codes are much better than those from narrow-sense BCH codes in [12].
Application of a Risk Matrix for Hazard Risk Assessment in Busan Metropolitan City

The frequency and impact of disasters have increased recently because of various reasons, including climate change, an increase in social complexity, and so on.Limited financial and manpower resources for disaster management impede ample countermeasures to mitigate various risks that trigger disasters, and this requires the identification of disasters with relatively high priority for effective and efficient disaster management.This study performs hazard risk assessment using a risk matrix for Busan Metropolitan City, indicating disasters with relatively high priority.The results suggest that infectious diseases, fine and yellow dust, storms and floods, atomic accidents, and earthquakes have relatively high priority.The results may be used as baseline data for establishing a disaster preparedness strategy and for developing future research.Standardization of the methodology for hazard risk assessment is also required to secure the efficiency of the data collection and the reliability of the assessment.
Memory access integrity: detecting fine-grained memory access errors in binary code

As one of the most notorious programming errors, memory access errors still hurt modern software security. Particularly, they are hidden deeply in important software systems written in memory unsafe languages like C/C++. Plenty of work have been proposed to detect bugs leading to memory access errors. However, all existing works lack the ability to handle two challenges. First, they are not able to tackle fine-grained memory access errors, e.g., data overflow inside one data structure. These errors are usually overlooked for a long time since they happen inside one memory block and do not lead to program crash. Second, most existing works rely on source code or debugging information to recover memory boundary information, so they cannot be directly applied to detection of memory access errors in binary code. However, searching memory access errors in binary code is a very common scenario in software vulnerability detection and exploitation. In order to overcome these challenges, we propose Memory Access Integrity (MAI), a dynamic method to detect fine-grained memory access errors in off-the-shelf binary executables. The core idea is to recover fine-grained accessing policy between memory access behaviors and memory ranges, and then detect memory access errors based on the policy. The key insight in our work is that memory accessing patterns reveal information for recovering the boundary of memory objects and the accessing policy. Based on these recovered information, our method maintains a new memory model to simulate the life cycle of memory objects and report errors when any accessing policy is violated. We evaluate our tool on popular CTF datasets and real world softwares. Compared with the state of the art detection tool, the evaluation result demonstrates that our tool can detect fine-grained memory access errors effectively and efficiently. As the practical impact, our tool has detected three 0-day memory access errors in an audio decoder.
Round-trip training approach for bilingually low-resource statistical machine translation systems

Statistical Machine Translation (SMT) is making good progress in recent years. Since SMT systems are based on data-driven approach, they learn from millions or even billions of words from human-translated texts. The quality of SMT systems heavily depends on the data that we use for training step, not only its quality and amount, but also on how relevant it is for the texts that we wish to translate. However, human labeling is very costly and time consuming. In this article we develop a learning mechanism by proposing a round-trip training scenario as a reliable retraining approach through a communication framework for making effective use of monolingual text to tackle the training data scarcity, and improve translation quality. We present detailed experimental results using Spanish-English as a high-resource language pair, and Persian-Spanish as a low-resource language pair. We demonstrate that in all cases translation quality is improved.
Virtual Dome System Using HMDs: An Alternative to the Expensive and Less Accessible Physical Domes

It is known that the dome display, as one of the Immersive Virtual Environments (IVEs), has its prominent advantages that viewers can experience highly immersive sensation thanks to the frameless image with a wide Field of View (FOV). However, fulldome projection systems have limited accessibility because of the technical complexity and high construction cost. Recently, the development and popularization of Head-Mounted Displays (HMDs) have changed the ways of inquiry and production in many media industries. In this research, the authors developed a virtual dome system using HMDs and assessed it quantitatively from the perspective of user immersion. An experiment was conducted to compare the difference of users’ immersion between a physical dome and a virtual one using modified Immersive Tendency Questionnaire (ITQ) and the Virtual Reality Immersion (VRI) Questionnaire. 44 participants took part in this research. In conclusion, as far as user immersion is concerned, the virtual dome system is capable of bringing a similar, if not better, experience when compared with the physical one. Although the defects intrinsic to a virtual dome system, such as the limited resolution, uncomfortableness to wear and the lack of shared experience should not be overlooked, this paper proved that the virtual dome system can be a relatively low-cost and more accessible alternative for one to experience a fulldome movie, and thus worth further study and application.
Anti-jamming Effectiveness Evaluation of Radio Fuze based on the grey analytic hierarchy process

Considering the working environment and characteristics of the radio fuze, the seven indicators that respectively reflect the radio fuze's anti-repression and anti-deception interference ability are selected to construct an anti-jamming performance evaluation index system. Based on this system, an evaluation model based on Grey Hierarchy Analysis (GAHP) was constructed, and the judgment matrix, weight vector, evaluation set, evaluation sample matrix, whitening weight function, and evaluation weight matrix and other key factors of the model were analyzed and determined. The scientific and feasibility of this method is verified by an example, which provides a more accurate and effective method for quantitatively studying the anti-interference performance of radio fuze.
Pedestrian Intention Detection Using Faster RCNN and SSD

In the domain of Intelligent Monitoring, Smart Driving and Robotics, Pedestrian intention detection is a prime discipline of object recognition. Currently, several pedestrian detection techniques are proposed however, just a handful are re-ported in the domain of pedestrian ‘intention’ detection. Due to the complications of the image background and pedestrian posture diversity, pedestrian intention detection is still a challenge which requires concise algorithms. In this paper, Single Shot Detector (SSD) is compared with Faster Region Convolutional Neural Network (Faster RCNN) architecture of deep neural network by applying different Convolutional Neural Network (CNN) models. Experiments have been conducted in a wide spectrum to obtain various models of Faster RCNN and SSD through compatible alterations in algorithm and parameters tuning. In this paper, Faster R-CNN and SSD architecture have been trained and their results are compared. New and simple evaluation performance parameters are suggested namely: Percentage Detection Index, Percentage Recognition Index and Precision score as compared to the traditional mean average precision (mAp) found in literature. While training these architectures with 1350 images, Faster RCNN learned three times faster than SSD with 2% increased accuracy.
On Monomorphisms and Subfields

Summary This is the second part of a four-article series containing a Mizar [2], [1] formalization of Kronecker’s construction about roots of polynomials in field extensions, i.e. that for every field F and every polynomial p ∈ F [ X ] \F there exists a field extension E of F such that p has a root over E . The formalization follows Kronecker’s classical proof using F [ X ] /&lt;p&gt; as the desired field extension E [5], [3], [4]. In the first part we show that an irreducible polynomial p ∈ F [ X ] \F has a root over F [ X ] /&lt;p&gt; . Note, however, that this statement cannot be true in a rigid formal sense: We do not have F ⊆ [ X ] / &lt; p &gt; as sets, so F is not a subfield of F [ X ] /&lt;p&gt; , and hence formally p is not even a polynomial over F [ X ] / &lt; p &gt; . Consequently, we translate p along the canonical monomorphism ϕ : F → F [ X ] /&lt;p&gt; and show that the translated polynomial ϕ ( p ) has a root over F [ X ] /&lt;p&gt; . Because F is not a subfield of F [ X ] /&lt;p&gt; we construct in this second part the field ( E \ ϕF )∪ F for a given monomorphism ϕ : F → E and show that this field both is isomorphic to F and includes F as a subfield. In the literature this part of the proof usually consists of saying that “one can identify F with its image ϕF in F [ X ] /&lt;p&gt; and therefore consider F as a subfield of F [ X ] /&lt;p&gt; ”. Interestingly, to do so we need to assume that F ∩ E = ∅, in particular Kronecker’s construction can be formalized for fields F with F ∩ F [ X ] = ∅. Surprisingly, as we show in the third part, this condition is not automatically true for arbitray fields F : With the exception of 𝕑 2 we construct for every field F an isomorphic copy F′ of F with F′ ∩ F′ [ X ] ≠ ∅. We also prove that for Mizar’s representations of 𝕑 n , 𝕈 and 𝕉 we have 𝕑 n ∩ 𝕑 n [ X ] = ∅, 𝕈 ∩ 𝕈 [ X ] = ∅ and 𝕉 ∩ 𝕉 [ X ] = ∅, respectively. In the fourth part we finally define field extensions: E is a field extension of F iff F is a subfield of E . Note, that in this case we have F ⊆ E as sets, and thus a polynomial p over F is also a polynomial over E . We then apply the construction of the second part to F [ X ] /&lt;p&gt; with the canonical monomorphism ϕ : F → F [ X ] /&lt;p&gt; . Together with the first part this gives - for fields F with F ∩ F [ X ] = ∅ - a field extension E of F in which p ∈ F [ X ] \F has a root.
Uncertainty Management in Software Projects: A Case Study in a Public Company

Software development meets the various challenges of rapidly growing markets. To address such challenges projects to design and adopt specific development approaches. However, various project management approaches do not consider the uncertainties that exist in projects. In this paper, we present findings from a case study in which we explore how to apply the Management Uncertainty Software Project (MUPS) approach. We do so by the empirical investigation at a public organization in Brazil. The objective of this study is to contribute to the body of knowledge regarding the potential benefits of MUSP approach. The conclusions of the empirical study will help both researchers and practitioners to understand better which benefits are already being realized in practice, and how they can best be realized.
Comment: Variational Autoencoders as Empirical Bayes

We thank Professor Efron for his informative and unifying review of empirical Bayes.In this comment, we discuss the connection between empirical Bayes and the variational autoencoder (VAE), a popular statistical inference framework in the machine learning community.We hope this connection motivates new algorithmic approaches for empirical Bayesians and gives new perspectives on VAEs for machine learners. EMPIRICAL BAYES AND VAESThe key idea of empirical Bayes is to estimate a prior distribution from data.Consider a model where each observation is independently generated by a different, unobserved random variable.The empirical Bayesian first uses all observations to estimate a prior over the latent variables; she then infers these variables using the fitted prior.In this model, each latent variable is associated with only one data point.Yet, through the fitted prior, the empirical Bayesian profits by incorporating information from the entire data set into each inference.This view of empirical Bayes reminds us of the variational autoencoder (VAE) (Kingma and Welling, 2013), an approach to approximate Bayesian inference for a particular class of latent variable models.A VAE refers to both the user-specified generative model and a strategy for approximate posterior inference.Given a dataset, a VAE simultaneously fits the forward model (i.e., the generative model) that describes the data and a function that approximates Bayesian inversion for the generative model.This inversion maps a data point to
Operational Indicators of the Learning Management System in Virtual Spaces Supported by ICT

The paper presents the conceptual framework and operational indicators of the Learning Management System in virtual spaces supported by ICT. The framework of the management system has three layers that are operational, management-operative, and management-decision. The conceptual foundation of the system is related to the Science of Education, the Science of Computing and the Science of Administration, and takes into account the representative model of the teaching-learning process of the student in virtual spaces supported by ICT. The dimensions, characteristics, variables, and indicators of the management system are assembled on the model in question. The indicators presented in the article correspond to 153 indicators of the operational layer. These were evaluated internationally with a confidence level of more than 90%. The operative indicators are contained in the dimensions of identification, academic, pedagogical-didactic, formative, of assessment of the virtual learning, and technological of the system of management of the virtual learning. The importance of the indicators presented ensures the success of the operational phase of the management system. Therefore, once the operation of the system is guaranteed, the success of the management-operative and management-decision layers of the system is assured. The correct fulfillment of the layers above guarantees the achievement of both the student’s learning in the virtual course and the institution that offers virtual teaching through virtual learning platforms supported by ICT.
Application of Artificial Intelligence in Primary and Secondary Schools: A Case Study of Scratch

In recent years, with the rapid rise of Scratch around the world, teachers take it as the natural artificial intelligence teaching carrier and develop abundant teaching cases according to different age characteristics. Based on the author’s study and practical experience, this paper first analyzed the manufacturing process of the Scratch works, then discussed the advantages of the Scratch in the cultivation of the students’ creative thinking, and finally summed up how to inspire the students’ creative thinking by the Scratch course.
Multilateral Relationship between Information Literacy, Self-Concept and Metacognitive Ability

This study examined information literacy, metacognitive abilities and selfconcept capabilities.The evaluation of this research indicated that self-concept is the totality of psychological, emotional, psychosomatic and mental development that provide confidence to individual in the ability to search, use, appraise and assess information resources, which are critical qualities needed to enable information literacy; an individual needs to be conscious and develop aptitude to identify useful information.Metacognitive ability is important because of the age of information overload which an individual is overwhelmed with which signified that information available is exceeding processing capacity of an average individual.Accordingly, once information overload ensues, it is possible that a decrease or decline in quality of decision-making will happen.In view of this, metacognitive ability becomes necessary in order to equip people with the critical ability to contemplate rigorously before action is taken.The objective of this research is to embark on content analysis of the subject matter of information literacy, metacognitive abilities and self-concept in which existing research was thoroughly evaluated in order to identify gap in research and bring out new knowledge.This research uses qualitative content analysis as a method of data collection in which existing journals and other information resources were evaluated.Research has been silenced on the triangular relationship of metacognitive ability, self-concept and information literacy, that is, the gap this research attempt to bridge.
Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control

Reinforcement learning (RL) has had many successes, but significant hyperparameter tuning is commonly required to achieve good performance. Furthermore, when nonlinear function approximation is used, non-stationarity in the state representation can lead to learning instability. A variety of techniques exist to combat this --- most notably experience replay or the use of parallel actors. These techniques stabilize learning by making the RL problem more similar to the supervised setting. However, they come at the cost of moving away from the RL problem as it is typically formulated, that is, a single agent learning online without maintaining a large database of training examples. To address these issues, we propose Metatrace, a meta-gradient descent based algorithm to tune the step-size online. Metatrace leverages the structure of eligibility traces, and works for both tuning a scalar step-size and a respective step-size for each parameter. We empirically evaluate Metatrace for actor-critic on the Arcade Learning Environment. Results show Metatrace can speed up learning, and improve performance in non-stationary settings.
Residents Aware Network for Intelligent Assistance to Enable Aging-in-Place

As society is aging it is imperative to find suitable technologies that can enable seniors to live in their own homes longer to minimize the social costs associated with institutional care. While there are many apps and gadgets to deal with individual situations such as fall detection, smart medicine dispenser, remote doctor visit etc. there are no comprehensive holistic solutions that can deal with most of the challenges associated with aging. This is a complex task involving many disciplines. At West Virginia University (USA), we put together an interdisciplinary group including Electrical Engineers, Computer Scientists, Computer Engineers, Medical and Healthcare Professionals and Social Workers to create a realistic testbed to demonstrate integrated solutions that can be scaled-up by start-up companies using a wide variety of resources available at the University. This system known as RANIA (Residents Aware Network for Intelligent Assistance) is being developed by a group of nearly 100 students under the guidance of faculty from the College of Engineering, School of Health Sciences and the School of Social Work. In addition, we have also created a Collaboratory to enable participation from institutions around the world to create their own unique solutions suitable for specific environments. We believe this project can have profound impact on aging populations around the world by establishing the technical feasibility and economic viability of technologies that can enhance quality of living not only by delaying the need for institutional care but also reducing social costs associated with caring for seniors.
Teachers’ Self-Assessment of Their Teaching Effectiveness at Higher Secondary Level in Pakistan: A Case Study

Self-assessment is a process of continuous reflection, self-monitoring, and selfjudgment, to review an individual’s strengths and weaknesses and helps to discover areas which need improvement. It involves an individual’s learning from his experiences, by judging his personal, educational as well as his professional performance. Selfassessment enables teachers to get control over their progress. It can serve as one of the most influential tools for the measurement of teacher quality. The researcher of this study administered a questionnaire which consisted of two parts. Part one comprised of SITE II (Self-assessment Instrument for Teacher Evaluation II) on which teachers rated themselves, and part two was designed to explore teachers’ opinion about the effectiveness of self-assessment. The sample comprised of 107 teachers of higher secondary level. The basic motive of the researcher was to enhance learning through self-assessment to know if such self-assessment exercises may promote quality teaching in classrooms. It was found that the respondents viewed that self-assessment helps them understand their strengths and weaknesses. It establishes teachers’ involvement in self-learning, which can serve as a sound basis in the teaching process. By doing self-assessment, teachers can re-consider as to how they are teaching in their classrooms.
ORCID in the Open Science scenario: opportunities for academic libraries

The persistent identification of authors and contributors plays a decisive role within the Open Science landscape. The increasing number of published research products and new open publishing models and infrastructures requires author identification which goes beyond fields or infrastructures and guarantees interoperability. ORCID iD is presented as a persistent identifier for researchers in this context. As information managers and organisers, many academic libraries have taken the lead in offering ORCID-related services and implementing it in their systems. This paper scans the implementation models across Europe and the actions carried out by libraries. Finally, it also depicts perspectives for integration in the Austrian library and research context.
Configuration and Use of Pervasive Augmented Reality Interfaces in a Smart Home Context: A Prototype

The Augmented Reality (AR) is believed to be a trending mechanism for intelligent environments since it provides additional layers of information on top of the physical world. Pervasive AR extends this concept through context-awareness, 3D space positioning and tracking mechanisms, resulting in experiences without interruptions. This paper proposes a pervasive AR application to create and configure such experiences, allowing management and control of appliances in a Smart Home context Based on a virtual content re-calibration process, the application is able to place and highlight what in the smart home can be interacted with a high level of accuracy and resilience to changes in dynamic environments. We also present examples of other appliances that can be integrated into the management and control features. Finally, we propose possible scenarios besides the Smart Home where the application might also be used.