Reviewerly/exHarmony huggingface dataset:
Improvement of code fragment fitness to guide feature construction in XCS

In complex classification problems, constructed features with rich discriminative information can simplify decision boundaries. Code Fragments (CFs) produce GP-tree-like constructed features that can represent decision boundaries effectively in Learning Classifier Systems (LCSs). But the search space for useful CFs is vast due to this richness in boundary creation, which is impractical. Online Feature-generation (OF) improves the search of useful CFs by growing promising CFs from a dynamic list of preferable CFs based on the ability to produce accurate and generalised, i.e. high-fitness, classifiers. However, the previous preference for high-numerosity CFs did not encapsulate information about the applicability of CFs directly. Consequently, learning performances of OF with an accuracy-based LCS (termed XOF) struggled to progress in the final learning phase. The hypothesis is that estimating the CF-fitness of CFs based on classifier fitness will aid the search for useful constructed features. This is anticipated to drive the search of decision boundaries efficiently, and thereby improve learning performances. Experiments on large-scale and hierarchical Boolean problems show that the proposed systems learn faster than traditional LCSs regarding the number of generations and time consumption. Tests on real-world datasets demonstrate its capability to find readable and useful features to solve practical problems.
The effect of the cutoff frequency on the sound production of a clarinet-like instrument

The input impedance of woodwind instruments is characterized by at least two bands due to the lattice of open toneholes, a stop band at low frequencies, and a pass band at higher frequencies where the acoustic energy is able to propagate past the first open tonehole and into the lattice. The cutoff frequency that separates these two bands is an approximate value that is determined by the geometry of the lattice of open toneholes. It is expected that the frequency at which the stop band transitions to the pass band affects the sound produced by the instrument, but it is not known how this frequency affects the competition between self-sustained oscillation and radiation. A simplified model of a clarinet-like resonator is conceived such that the first input impedance peak and the cutoff frequency can be independently chosen. Experimental prototypes are built and the measured impedance of these prototypes is compared with the simulations. Resonators with very similar low frequency behavior but very different cutoff frequencies are then compared using digital synthesis to evaluate the influence of the cutoff frequency on sound production. The cutoff frequency impacts the synthesized pressure and acoustic volume velocity in the mouthpiece, particularly regarding the spectral content at high frequencies.
Asymmetric Mapping Quantization for Nearest Neighbor Search

Nearest neighbor search is a fundamental problem in computer vision and machine learning. The straightforward solution, linear scan, is both computationally and memory intensive in large scale high-dimensional cases, hence is not preferable in practice. Therefore, there have been a lot of interests in algorithms that perform approximate nearest neighbor (ANN) search. In this paper, we propose a novel addition-based vector quantization algorithm, Asymmetric Mapping Quantization (AMQ), to efficiently conduct ANN search. Unlike existing addition-based quantization methods that suffer from handling the problem caused by the norm of database vector, we map the query vector and database vector using different mapping functions to transform the computation of L-2 distance to inner product similarity, thus do not need to evaluate the norm of database vector. Moreover, we further propose Distributed Asymmetric Mapping Quantization (DAMQ) to enable AMQ to work on very large dataset by distributed learning. Extensive experiments on approximate nearest neighbor search and image retrieval validate the merits of the proposed AMQ and DAMQ.
The Knowledge Repository Management System Architecture of Digital Knowledge Engineering using Machine Learning to Promote Software Engineering Competencies

The knowledge repository management system architecture of digital knowledge engineering using machine learning (KRMS-SWE) to promote software engineering competencies is comprised of four parts, as follows: 1) device service, 2) application service, 3) module service of the KRMS-SWE and 4) machine learning service and storage unit. The knowledge creation, storage, testing and assessing of students’ knowledge in software engineering is carried out using a knowledge verification process with machine learning and divided into six steps, as follows: pre-processing, filtration, stemming, indexing, data mining and interpretation and evaluation. The overall result regarding the suitability of the KRMS-SWE is &#x0D; assessed by five experts who have high levels of experience in related fields. The findings reveal that this research approach can be applied to the future development of the KRMS-SWE.
Automatic Generation and Evolution of Personalized Curriculum Based on Genetic Algorithm

In order to solve the automatic generation and evolution of personalized curriculum, the method of using genetic algorithm to realize the evolution of personalized learning content is proposed to solve the dynamic personalized needs of users. Through the research and implementation of personalized curriculum generation technology, firstly, the structures of curriculum generation, genetic algorithm and curriculum scene, as well as the method and technology of personalized curriculum generation and evolution system framework are described in detail. Then, the framework structure based on genetic algorithm is determined, and the user model is updated. Finally, experiments are carried out based on genetic algorithm. The research on the experiment of automatic generation and evolution of personalized curriculum shows that the application of genetic algorithm in the process of curriculum generation and evolution makes students' learning content evolve with the change of their knowledge state in the process of learning, effectively promotes students' interest in learning, and improves learning efficiency and effect.
Ambiguity in Explicit Discourse Connectives

Discourse connectives are known to be subject to both usage and sense ambiguity, as has already been discussed in the literature.But discourse connectives are no different from other linguistic expressions in being subject to other types of ambiguity as well.Four are illustrated and discussed here.
DuG: Dual speaker-based acoustic gesture recognition for humanoid robot control

Acoustic gesture recognition based on the Doppler effect is a natural HCI (human computer interface) mechanism for cooperation with robots. Environmental noise, nearby human motions, and user adaptability compromise the gesture recognition accuracy and weaken the efficacy of the interaction mode. To this end, we propose a novel Dual Speaker Gesture (DuG) recognition scheme in which the sensing environment consists of two speakers and a microphone. The two speakers transmit inaudible tones at different frequencies and the microphone changes in the dual-channel sound carry rich features originating from the Doppler shifts caused by hand motions, where we introduce a feature called top-bandwidth to distinguish the environment motions noise and discard. In particular, we develop a fusion framework to combine a priori empirical model with a hidden Markov model(HMM) to enhance user adaptability and improve classification accuracy. A set of 11 gestures has a mean recognition rate up to 99.36% with 10 participants. And five users participating in a driving robot experiment show that it maintains average 98% accurate gesture recognition even under a common HMM model. DuG boosts the gesture recognition performance under acoustic noise and nearby environment motion interference.
Eye-tracking data and mathematical tasks with focus on mathematical reasoning

This data article contains eye-tracking data (i.e., dwell time and fixations), Z-transformed cognitive data (i.e., Raven's Advanced Progressive Matrices and Operation span), and practice and test scores from a study in mathematics education. This data is provided in a supplementary file. The method section describes the mathematics tasks used in the study. These mathematics tasks are of two kinds, with and without solution templates, to induce different types of mathematical reasoning.
Frequency Domain Steganography with Reversible Texture Combination

Texture Combination is a process of re-sampling a smaller texture image to synthesize a new texture with similar appearance.This texture combination is weaved with Steganography to conceal secret text messages.In this paper, a novel texture combination based Steganographic method in frequency domain is proposed to hide and send secret messages.In contrast to the existing techniques, this method generates a Stego synthetic texture of arbitrary size rather than embedding in the original image.This method offers an embedding capacity that is proportional to the size of the Stego synthetic texture.Moreover its reversible capability allows recovering secret messages and the source texture.The texture combination is performed in frequency domain making use of Discrete Cosine Transform (DCT) which makes it almost impossible for a Steganalytic algorithm to defeat this approach.Experimental results verify that the proposed method provides various embedding capacities, produces visually good texture images and recover secret messages.
An Empirical Investigation of Effort Estimation in Mobile Apps Using Agile Development Process

Effort estimation is essential in order for a project manager and development team members to be able to successfully plan for a software project.The planning and development of mobile applications present many challenges.The aim of this study is to provide and report an overview on the state of the practice of effort estimation techniques that companies use for their mobile app projects.This study focuses on organisations which apply the Agile development process during their projects.We conducted structured and semi-structured interviews with 20 Agile practitioners at 18 different organisations.The results revealed that Planning Poker (PP) and Expert Judgment (EJ) were the most frequently used estimation techniques in mobile app projects.
Machine Learning Models for Measuring Syntax Complexity of English Text

In this paper we propose a methodology to assess the syntax complexity of a sentence representing it as sequence of parts-of-speech and comparing Recurrent Neural Networks and Support Vector Machine. We have carried out experiments in English language which are compared with previous results obtained for the Italian one.
THE USE OF ROLE PLAY TO IMPROVE STUDENTS' SPEAKING SKILL

This research entitled the use of Role Play Technique to improve students’ speaking skill. The objective of the research is to improve Speaking skill using Role Play technique. This research used Classroom Action Research. Role play can be trusted to help students speaking ability. The population of the research were the students at SMKN 3 Karawang in Academic years 2017 – 2018. The total population of students in this academic years is 300 Students. The number of sample was 30 students. The data was gathered from test and observation. The average score of test was Cycle I 58.08 and Cycle II 58.06. After the implementation of Role Play activities in every cycle, the students’ speaking score were getting better. It can be seen from the result of students’ average score Cycle I was 83.07 and Cycle II was 77.08. It can be concluded that Role play can improve students’ speaking skill.Keywords: Role play, speaking, CAR
Coded Decentralized Learning With Gradient Descent for Big Data Analytics

Machine learning is an effective technique for big data analytics. We focus on the study of big data analytics with decentralized learning in large-scale networks. Fountain codes are applied to the decentralized learning process to reduce communication load for exchanging intermediate learning parameters among fog nodes. Two scenarios, i.e., disjoint datasets and overlapping datasets, are analyzed. Comparison results show that communication load can be reduced significantly by the Fountain-based scheme for large-scale networks, especially when the quality of communication links is relatively bad and/or the number of fog nodes is large.
A new secure proxy-based distributed virtual machines management in mobile cloud computing

With the massive growing utilization of limited mobile devices in everyday life, the data exchange of distributed applications for such mobile devices becomes a major concern.In light of this urgency for managing the voluminous data, mobile computing is integrated within cloud computing, which brings a new technology called Mobile Cloud Computing (MCC) [1,2].With the MCC, the exchange of digital content has become easier and faster.The MCC relies on a virtualization mechanism while providing services for the cloud's users.Virtualization is a very promising mechanism in cloud computing, which increases the efficiency of exploiting shared hardware resources such as memory, cache, and CPU of servers [3].
The Survey on Intellectual Property Based on Blockchain Technology

The characteristics of decentralization, tamper-resistance and transaction anonymity of blockchain can resolve effectively the problems in traditional intellectual property such as the difficulty of electronic obtaining for evidence, the high cost and low compensation when safeguarding the copyrights. Blockchain records the information through encryption algorithm, removes the third party, and stores the information in all nodes to prevent the information from being tampered with, so as to realize the protection of intellectual property. Starting from the bottom layer of blockchain, this paper expounds in detail the characteristics and the technical framework of blockchain. At the same time, according to the existing problems in transaction throughput, time delay and resource consumption of blockchain system, optimization mechanisms such as cross-chain and proof of stake are analyzed. Finally, combined with the characteristics of blockchain technology and existing application framework, this paper summarizes the existing problems in the industry and forecasts the development trend of intellectual property based on blockchain technology.
A study of two evolutionary/tabu search approaches for the generalized max-mean dispersion problem

Evolutionary computing is a general and powerful framework for solving difficult optimization problems, including those arising in expert and intelligent systems. In this work, we investigate for the first time two hybrid evolutionary algorithms incorporating tabu search for solving the generalized max-mean dispersion problem (GMaxMeanDP) which has a variety of practical applications such as web page ranking, community mining, and trust networks. The proposed algorithms integrate innovative search strategies that help the search to explore the search space effectively. We report extensive computational results of the proposed algorithms on six types of 160 benchmark instances, demonstrating their effectiveness and usefulness. In addition to the GMaxMeanDP, the proposed algorithms can help to better solve other problems that can be formulated as the GMaxMeanDP.
Transferring from ex-vivo to in-vivo: Instrument Localization in 3D Cardiac Ultrasound Using Pyramid-UNet with Hybrid Loss

Automated instrument localization during cardiac interventions is essential to accurately and efficiently interpret a 3D ultrasound (US) image. In this paper, we propose a method to automatically localize the cardiac intervention instrument (RF-ablation catheter or guidewire) in a 3D US volume. We propose a Pyramid-UNet, which exploits the multi-scale information for better segmentation performance. Furthermore, a hybrid loss function is introduced, which consists of contextual loss and class-balanced focal loss, to enhance the performance of the network in cardiac US images. We have collected a challenging ex-vivo dataset to validate our method, which achieves a Dice score of 69.6% being 18.8% higher than the state-of-the-art methods. Moreover, with the pre-trained model on the ex-vivo dataset, our method can be easily adapted to the in-vivo dataset with several iterations and then achieves a Dice score of 65.8% for a different instrument. With segmentation, instruments can be localized with an average error less than 3 voxels in both datasets. To the best of our knowledge, this is the first work to validate the image-based method on in-vivo cardiac datasets.
Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling

We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for different patients. Existing methods including Bayesian networks and structured prediction either do not incorporate high-dimensional signals or fail to model conditional dependencies among variables. To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency. Predictions are then made via iteratively updating variables using backpropagation (BP) to maximize corresponding posterior probability. Furthermore, we extend BIN to composite BIN (CBIN), which involves the iterative prediction process in the training stage and improves both accuracy and computational efficiency by adaptively smoothing the optimization landscape. Experiments on synthetic and real-world datasets (a sleep study and a dermatology dataset) show that CBIN is a single model that can achieve state-of-the-art performance and obtain better accuracy in most inference tasks than multiple models each specifically trained for a different task.
From Statistical Transportability to Estimating the Effect of Stochastic Interventions

Learning systems often face a critical challenge when applied to settings that differ from those under which they were initially trained. In particular, the assumption that both the source/training and the target/deployment domains follow the same causal mechanisms and observed distributions is commonly violated. This implies that the robustness and convergence guarantees usually expected from these methods are no longer attainable. In this paper, we study these violations through causal lens using the formalism of statistical transportability [Pearl and Bareinboim, 2011] (PB, for short). We start by proving sufficient and necessary graphical conditions under which a probability distribution observed in the source domain can be extrapolated to the target one, where strictly less data is available. We develop the first sound and complete procedure for statistical transportability, which formally closes the problem introduced by PB. Further, we tackle the general challenge of identification of stochastic interventions from observational data [Sec.~4.4, Pearl, 2000]. This problem has been solved in the context of atomic interventions using Pearl's do-calculus, which lacks complete treatment in the stochastic case. We prove completeness of stochastic identification by constructing a reduction of any instance of this problem to an instance of statistical transportability, closing the problem.
Single-Image Super-Resolution: A Survey

Single-image super-resolution has been broadly applied in many fields such as military term, medical imaging, etc. In this paper, we mainly focus on the researches of recent years and classify them into non-deep learning SR algorithms and deep learning SR algorithms. For each classification, the basic concepts and algorithm processes are introduced. Furthermore, the paper discusses the advantages and disadvantages of different algorithms, which will offer potential research direction for the future development of SR.
3D Printed Deformable Surfaces for Shape-Changing Displays

We use interlinked 3D printed panels to fabricate deformable surfaces that are specifically designed for shape-changing displays. Our exploration of 3D printed deformable surfaces, as a fabrication technique for shape-changing displays, shows new and diverse forms of shape output, visualizations, and interaction capabilities. This article describes our general design and fabrication approach, the impact of varying surface design parameters, and a demonstration of possible application examples. We conclude by discussing current limitations and future directions for this work.
NATIFLife-A Smart Sensor Network for Assistive Domotics

In this paper research activities developed within the NATIFLife project are presented. The project, funded by INTERREG V-A Italy-Malta Cooperation Programme, aims at developing an Integrated Platform of Assistive Technology solutions supporting elderly and people with impairments in their domestic environment. An overview of the NATIFLife project is provided here, then the architecture of the Assistive Platform, together with the research activities are described.
Cryptography with Disposable Backdoors

Backdooring cryptographic algorithms is an indisputable taboo in the cryptographic literature for a good reason: however noble the intentions, backdoors might fall in the wrong hands, in which case security is completely compromised. Nonetheless, more and more legislative pressure is being produced to enforce the use of such backdoors. In this work we introduce the concept of disposable cryptographic backdoors which can be used only once and become useless after that. These exotic primitives are impossible in the classical digital world without stateful and secure trusted hardware support, but, as we show, are feasible assuming quantum computation and access to classical stateless hardware tokens. Concretely, we construct a disposable (single-use) version of message authentication codes, and use them to derive a black-box construction of stateful hardware tokens in the above setting with quantum computation and classical stateless hardware tokens. This can be viewed as a generic transformation from stateful to stateless tokens and enables, among other things, one-time programs and memories. This is to our knowledge the first provably secure construction of such primitives from stateless tokens. As an application of disposable cryptographic backdoors we use our constructed primitive above to propose a middle-ground solution to the recent legislative push to backdoor cryptography: the conflict between Apple and FBI. We show that it is possible for Apple to create a one-time backdoor which unlocks any single device, and not even Apple can use it to unlock more than one, i.e., the backdoor becomes useless after it is used. We further describe how to use our ideas to derive a version of CCA-secure public key encryption, which is accompanied with a disposable (i.e., single-use, as in the above scenario) backdoor.