Reviewerly/exHarmony huggingface dataset:
Effects of singular value spectrum on the performance of echo state network

Reservoir computing (RC), as an efficient and simple paradigm of training recurrent networks, has attracted considerable attention for its superior performance on many tasks. Echo state network (ESN) is one of typical representatives of RC, whose good performance mainly depends on the selection of right parameters. Hence, it is necessary for users to understand the inter-relationship between the performance and the parameters of ESN. Using computational experiments and statistical analysis, this paper systematically analyzes the effects of singular value spectrum on the performance of ESN, such as memory capacity, robustness, generalization performance, dynamics and so on. To simultaneously control the singular value spectrum and the sparsity when designing an ESN, two algorithms are introduced to construct the reservoir with the predefined singular value spectrum and sparsity, and their benefits are assessed on some benchmark problems.
Neural Self-Training through Spaced Repetition

Self-training is a semi-supervised learning approach for utilizing unlabeled data to create better learners. The efficacy of self-training algorithms depends on their data sampling techniques. The majority of current sampling techniques are based on predetermined policies which may not effectively explore the data space or improve model generalizability. In this work, we tackle the above challenges by introducing a new data sampling technique based on spaced repetition that dynamically samples informative and diverse unlabeled instances with respect to individual learner and instance characteristics. The proposed model is specifically effective in the context of neural models which can suffer from overfitting and high-variance gradients when trained with small amount of labeled data. Our model outperforms current semi-supervised learning approaches developed for neural networks on publicly-available datasets.
Mode-dependent-loss model for multimode photon-subtracted states

Multimode photon subtraction provides an experimentally feasible option to construct large non-Gaussian quantum states in continuous variable quantum optics. The non-Gaussian features of the state can lead towards the more exotic aspects of quantum theory, such as negativity of the Wigner function. However, the payoff for states with such delicate quantum properties is their sensitivity to decoherence. In this paper, we present a general model that treats the most important source of decoherence in a purely optical setting: losses. We use the framework of open quantum systems and master equations to describe losses in $n$-photon-subtracted multimode states, where each photon can be subtracted in an arbitrary mode. As a main result, we find that mode-dependent losses and photon subtraction generally do not commute. In particular, the losses do not only reduce the purity of the state, they also change the modal structure of its non-Gaussian features. We then conduct a detailed study of single-photon subtraction from a multimode Gaussian state, which is a setting that lies within the reach of present-day experiments.
THE POTENTIAL OF 3D VIRTUAL REALITY (VR) FOR LANGUAGE LEARNING: AN OVERVIEW

This reflection paper attempts to draw attention to the current situation of 3D Virtual Reality (VR) in the field of language learning.During the last decade, researchers and practitioners have made use of synthetic virtual environments and open virtual spaces to help develop students' language skills.In this sense, computer-generated environments have been used to simulate students' presence within the virtual context.Nevertheless, advancements in technology may lead to change the understanding of virtual environments thanks to the use of 3D VR.Teachers may be able to situate students within the virtual situation, giving them the possibility of experiencing real-life interaction in which target language skills could be developed.Thus, this paper attempts to reflect on the potential as well as possible limitations of this technology for language learning.
Efficient optoelectronic reservoir computing with three-route input based on optical delay lines

In the reservoir computing (RC) system based on delay line structure, the dynamical reservoir has short-term memory capability and can map instantaneous time signals to a high-dimensional space, so that it can effectively classify and recognize complicated time signals. A new RC system is proposed in this paper, where three-way signals are simultaneously input into the dynamical reservoir, and simultaneously processed and classified. Therefore, the information processing speed of the RC system is promoted manyfold. The reservoir is composed of a semiconductor laser and two optical feedback loops, in which two input signals are injected into two optical feedback loops, and another input signal is added to the driving current of the semiconductor laser. The computation efficiency of the RC system is validated by two tasks of the optical packet header recognition and digital speech recognition. By optimizing the parameters, the multi-input RC system can simultaneously recognize three-way 3-bit, 8-bit, 16-bit, and 32-bit optical packet headers with the optimal word error rate (WER) of 0%, and recognize three-way digital speech signals with the optimal WER of 0.2%. It is proven that this multi-input system outperforms the single-inputting RC system based on delay line structure.
An asymmetric multi-image cryptosystem based on cylindrical diffraction and phase truncation

An asymmetric multi-image cryptosystem is proposed by using the cylindrical diffraction and the phase truncation, which can avoid the vulnerability of the linear cryptosystems, simultaneously encrypt multiple images and decrypt images individually. In the proposed cryptosystem, four original images are modulated by four different random phases, respectively, and then their results are combined into one image by using convolution. The combined image is encoded into a real-value ciphertext by phase truncation after cylindrical diffraction, while the phase parts are saved as asymmetric decryption keys. Due to applications of the asymmetric cylindrical diffraction and the nonlinear operation of phase truncation, the cryptosystem can resist numerous types of attacks. The two main features of the proposed cryptosystem are to encrypt multi-image without time increasing linearly and individually decrypt multi-image with highly flexible. The security and flexibility of the proposed cryptosystem are demonstrated by numerical simulation results.
Approximate Random Dropout for DNN training acceleration in GPGPU

The training phases of Deep neural network (DNN) consumes enormous processing time and energy. Compression techniques utilizing the sparsity of DNNs can effectively accelerate the inference phase of DNNs. However, it can be hardly used in the training phase because the training phase involves dense matrix-multiplication using General Purpose Computation on Graphics Processors (GPGPU), which endorse regular and structural data layout. In this paper, we propose the Approximate Random Dropout that replaces the conventional random dropout of neurons and synapses with a regular and online generated patterns to eliminate the unnecessary computation and data access. We develop a SGD-based Search Algorithm that producing the distribution of dropout patterns to compensate the potential accuracy loss. We prove our approach is statistically equivalent to the previous dropout method. Experiments results on multilayer perceptron (MLP) and long short-term memory (LSTM) using well-known benchmarks show that the speedup rate brought by the proposed Approximate Random Dropout ranges from 1.18-2.16 (1.24-1.85) when dropout rate is 0.3-0.7 on MLP (LSTM) with negligible accuracy drop.
Variable Reduction-based Prediction through Modified Genetic Algorithm

Due to the massive influence in the use of prediction models in different sectors of society, many researchers have employed hybrid algorithms to increase the accuracy level of the prediction model.The literature suggests that the use of Genetic Algorithms (GAs) can sufficiently improve the performance of other prediction models; thus, this study.This paper introduced a new avenue of prediction integrating GA with the novel Inversed Bi-segmented Average Crossover (IBAX) operator paired with rank-based selection function to the KNN algorithm.The 70% of data from 597 records of student-respondents in the evaluation of the faculty instructional performance from the four State Universities and Colleges (SUC) in Caraga Region, Philippines were used as training set while the 30% was used for testing.The simulation result showed that the use of the proposed prediction model with the integration of the modified GA outperformed the KNN prediction model where GA with average crossover and roulette wheel selection function was used.The KNN where k value is three (3) was identified to be the optimal model for prediction with the 95.53% prediction accuracy compared to KNN with 1, 5, and 7 k values.
IMPLEMENTASI KEBIJAKAN PENERIMAAN PESERTA DIDIK BARU BERDASARKAN SISTEM ZONASI DI KOTA BANDUNG

The new student admission based on zoning system has been implemented in Indonesia since 2017 through the Ministerial Regulation of Education and Culture No. 17/2017, then renewed by the Ministerial Regulation of Education and Culture No. 18/2018. The regulation has been enacted for the Local Office of Education in all regencies/cities in Indonesia. To be implemented, every regency and city have enacted the operational regulations in accordance with each local conditions. The Local Government of Bandung City has implemented the zoning system since 2017, namely the Regulation of Mayor of Bandung No. 553/2017 and the Regulation of Mayor of Bandung No. 456/2018. The both regulations have the different rule of game, particularly the application of quota. The zoning quota of new student admission in 2017 was regulated only for 40-50 percent, but Bandung City in 2018 applied the zoning system for 100 percent. The aims of applying the zoning system were to closely bring the students to their residences, to reduce the costs of school transportation, and to eliminate the dichotomy between the favorite and unfavorite schools. The research uses the paired sample t-Test to compare the both Mayoral Regulations and to then find the dominant factor as the cause for occuring the conflict in the process of new student admission in Bandung City. Matland’s ambiguity-conflict model is used to explain the implementation of new student admission policy. Results of the research indicate that the dichotomy of favorite and unfavorite schools can not be removed, not just because of the public mindset, but also because of the unsupporting policy of local government, and the unreadiness of local government to provide the qualified infrastructures for the education in all schools.Keywords: School Dichotomy, Policy Implementation, New Student Admission, Zoning System.
Implications of the Bioinclusive Ethic on Collaborative and Participatory Design.

(2019). Implications of the Bioinclusive Ethic on Collaborative and Participatory Design. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 1571-1586.
The European Higher Education Area: A road to the future or at way’s end?

The goal of the Bologna Process was to develop a European Higher Education Area within a decade. The goal and the process proved to be attractive, new countries steadily applied for accession, and the Bologna Process has brought about substantial reforms. At the same time, the drive and optimism of the early years has given way to more measured optimism and even a sense of disillusion as we approach the 20th anniversary of the Bologna Declaration. The article outlines six phases in the development of the European Higher Education Area and then looks at some of the main challenges the EHEA faces as it approaches its third decade. Structural reforms have been the hallmark of the EHEA, and in this area the main challenges concern implementation rather than the development of new structures even if some policy challenges also remain. In the run-up to the 2018 Ministerial conference, EHEA faced a bitter debate on the character of the EHEA itself, linked to the questions of how to foster implementation of commitments undertaken and what it means to be a voluntary process. The fundamental values on which the EHEA builds are now threatened in some EHEA members, the role of the EHEA in a global context, and its relevance and governance constitute other challenges.Received: 08 April 2019Accepted: 29 April 2019Published online: 29 May 2019
Deep Manifold Alignment for Mid-Grain Sketch Based Image Retrieval

We present an algorithm for visually searching image collections using free-hand sketched queries. Prior sketch based image retrieval (SBIR) algorithms adopt either a category-level or fine-grain (instance-level) definition of cross-domain similarity—returning images that match the sketched object class (category-level SBIR), or a specific instance of that object (fine-grain SBIR). In this paper we take the middle-ground; proposing an SBIR algorithm that returns images sharing both the object category and key visual characteristics of the sketched query without assuming photo-approximate sketches from the user. We describe a deeply learned cross-domain embedding in which ‘mid-grain’ sketch-image similarity may be measured, reporting on the efficacy of unsupervised and semi-supervised manifold alignment techniques to encourage better intra-category (mid-grain) discrimination within that embedding. We propose a new mid-grain sketch-image dataset (MidGrain65c) and demonstrate not only mid-grain discrimination, but also improved category-level discrimination using our approach.
Makerspace for skills development in the industry 4.0 era

Goal / Purpose: Universities are increasingly investing in makerspaces. These learning spaces are presented as a place where students can share their projects, can innovate using rapid prototyping equipment, use low and high technology that serves as a starting point for students to launch start-ups, get advice on how to place a product in the market, and relate to potential lenders. This paper aims to discuss whether companies can benefit from these projects and whether skills for engineers can be developed in this environment. The paper provides insightful perceptions of the actions developed by one emergent university to develop innovative methodologies to support industry and students, to provide potential partnerships that fund projects in order to better prepare professionals for the industry 4.0.&#x0D; Design / Methodology / Approach: A discussion of the subject was carried out based on the existing literature and an exploratory study in an existing makerspace in the Netherlands. It was one qualitative research based on a case study. Field observation and research questions were based on the technical skills of engineering described in theory.&#x0D; Results: This article presented the idea that the makerspaces can be a great source of innovation if they are appropriately designed. The connection between universities and companies, aligned to active methodologies for teaching and learning meets a global need within a corporate universe to get faster and simpler and make the team see the project in a more holistic and complete manner. However, the goal is to bridge the gap with professors, researchers, makers, start-ups, and companies who want to use business-to-business practices. It can be said that these are beneficial characteristics of a makerspace observed: (i) students' learning is more active, (ii) more interaction between students and professors, through different areas (iii) at an early stage, students get in contact with the professional reality of their field, as the projects are related to real Engineering problems, and (iv) students develop transversal skills.&#x0D; Practical implications: This paper identifies the potential that Makerspaces offer as a strategic approach to teaching and learning related to the fourth industrial era. This paper implies that universities and academics that wish to tailor their education to industry 4.0 need understand this model better, and where appropriate invest in educational infrastructures such as this in universities, companies or cities. This will ensure that universities are educating professionals with the most suitable skills for industry and society, generating innovation in creative teaching and learning spaces.&#x0D; Research Limitations: The study is limited to one empirical research, analysis, and observation of a case study that can serve as a basis for future studies in other locations.
Communication Channel Between Teachers and Students in Chemistry Education: WhatsApp

WhatsApp is a smartphone application used for instant messaging.It has been popular in the community in recent years, but it also has been used in education.In the field of education, there are studies related to field-specific subjects in English and chemistry education related to the use of WhatsApp during school hours and outside of school time.However, there could not be reached any study investigating how a specific field teacher and students use this application in voluntary groups without being bound to a particular program.For this purpose, semi-structured interviews were conducted with chemistry teachers by using WhatsApp application to communicate with students.A study was conducted with eight volunteer chemistry teachers working in State Anatolian High Schools in Çankiri and Ankara provinces.In this study conducted with the teachers at high school level, it was found out that WhatsApp application is used for five main purposes: First, these are increasing the students' motivation for the course, strengthening the communication, encouraging information sharing among students, using as an opportunity to learn, and planning the study process.The messages that accelerate the tempo of study to increase the motivation of the students about the course, sending clues about the content of the course, expressions that make them feel the success, messages indicating that their identity and personalities are valuable, the photographs of the moments experienced together were evaluated within the first scope.Second were the messages, to strengthen communication, celebrating special days and achievements, and increasing the sense of belonging.Third, information sharing content was composed of audio record, problem, solution of the problem, visuals, and documents related to a subject.Fourth, instant answers given about an unclear topic, related questions and answers were discussed in the opportunity to learn category.Finally, reminding the deadline for projects and assignments, time for exam preparation.Giving feedback were evaluated within the process planning.In addition, the learning outcomes offered by WhatsApp which is easy to use and a low-cost communication channel can be added as an advantage.Besides, providing easy access to learning materials outside the classroom, recording a speech easily, and transmitting the recording to everyone concurrently were stated as positive opinions.On the other hand, teachers indicated that there are disadvantages of the application.Teachers stated that it would be difficult to use the application in a group of students with no smartphone or Internet connection and it would not be fair in terms of communication.The teachers also stated that their motivation decreases when the students are unconcerned about questions asked by the teachers.In addition, the lack of appropriate language in communication among students in the group, in which the teacher is involved, creates educational difficulty in practice.Because of such student attitudes, the teachers are moving away from the practice by stopping the messages.
Defense Against Poisoning Attack via Evaluating Training Samples Using Multiple Spectral Clustering Aggregation Method

The defense techniques for machine learning are critical yet challenging due to the number and type of attacks for widely applied machine learning algorithms are significantly increasing. Among these attacks, the poisoning at... | Find, read and cite all the research you need on Tech Science Press
More on the question ‘When does absence of evidence constitute evidence of absence?’ How Bayesian confirmation theory can logically support the answer

In forensic science it is not rare that common sayings are used to support particular inferences. A typical example is the adage ‘The absence of evidence is not evidence of absence’. This paper analyzes the rationale hidden behind such statement and it offers a structural way to approach the analysis of this particular adage throughout a careful analysis of four different scenarios.
Optimal Path Planning with Clothoid Curves for Passenger Comfort

Highly automated vehicles operating at SAE automation level 4 and 5 will not require the occupants' attention to be on the road at all.They will be free to amuse themselves as passengers.This will have the side effect of making them more vulnerable to motion sickness.Automated vehicles must plan paths which are feasible for the vehicle and comfortable for its occupants.In railway and highway design, paths with clothoid based transitions provide feasibility and comfort.This paper proposes a method for generating such a path using constrained non-linear optimization and compares it to an existing method based on root finding.
Information visualisation for efficient knowledge discovery and informed decision in design by shopping

Design space exploration (DSE) describes the systematic activity of discovery and evaluation of the elements in a design space in order to identify optimal solutions by reducing the design space to an area of performance. Designers sample thousands of design points iteratively, explore the design space, gain knowledge about the problem and make design decision. The literature tells us that DSE results in a decision of quality called informed decision, which is supported by information visualisation. The representation of design points is seen as primordial to gain an understanding of the problem and make an informed decision. In our work, we have sought to identify what type of graph is best suited to the discovery phase, and enables designers to make an informed decision. We designed a web platform with four design problems, and carried out an experiment with 42 participants. We found a graph that was better suited to making a decision of quality and to gaining greater understanding: the scatter plot matrix.
AUTOMATIC CAMERA SYSTEM CALIBRATION WITH A CHESSBOARD ENABLING FULL IMAGE COVERAGE

Abstract. Geometric camera calibration is a mandatory prerequisite for many applications in computer vision and photogrammetry. Especially when requiring an accurate camera model the effort for calibration can increase dramatically. For the calibration of the stereo-camera used for optical navigation a new chessboard based approach is presented. It is derived from different parts of existing approaches which, taken separately, are not able to meet the requirements. Moreover, the approach adds one novel main feature: It is able to detect all visible chessboard fields with the help of one or more fiducial markers simply sticked on a chessboard (AprilTags). This allows a robust detection of one or more chessboards in a scene, even from extreme perspectives. Except for the acquisition of the calibration images the presented approach enables a fully automatic calibration. Together with the parameters of the interior and relative orientation the full covariance matrix of all model parameters is calculated and provided, allowing a consistent error propagation in the whole processing chain of the imaging system. Even though the main use case for the approach is a stereo camera system it can be used for a multi-camera system with any number of cameras mounted on a rigid frame.