Reviewerly/exHarmony huggingface dataset:
A Study on the Relationship between Personality Theory and Education of Chinese Ancient

The purpose of this study is to investigate the relationship between personality theory and education of Chinese ancient and to provide data that can be applied to educational field and social organization. In the history of ancient Chinese education, Confucius is the educator who argued that we should not discriminate against human beings the earliest, In Confucius"s educational practice, we can conclude that there is no significant distinction in teaching is Confucius" leading idea. Mencius said that he could become an shèngrén like Yáo Shùn according to human efforts. In addition, human beings have a very small discrimination of innate qualities, but the influence of education and environment has changed, and human discrimination has occurred.<BR> The purpose of this study was to inject ethical norms of feudal society into the contents of humanity and then to rationalize these norms with personality theory for the purpose of education.,Chinese ancient educators emphasize the subjective activeness of human beings, the action of environment and education with the view that personality is changeable.,Mencius emphasized that the most important thing in the education method is to look back at oneself and save it by oneself.
Implications of developments in machine learning for people with cognitive disabilities

Machine learning is the process of training a computer program to reproduce and generalize the relationship between some kind of input and some kind of output. High profile successes for machine learning are attracting interest and investment. Can this technology be applied in ways that benefit people with cognitive disabilities and those who support them? What are the current limitations in the technology that affect these applications, and what are the prospects for overcoming them? What actions can maximize the benefits for people with cognitive disabilities?
Can AI be for Good in the Midst of Cyber Attacks and Privacy Violations?

Artificial Intelligence (AI) is affecting every aspect of our lives from healthcare to finance to driving to managing the home. Sophisticated machine learning techniques with a focus on deep learning are being applied successfully to detect cancer, to make the best choices for investments, to determine the most suitable routes for driving as well as to efficiently manage the electricity in our homes. We expect AI to have even more influence as advances are made with technology as well as in learning, planning, reasoning and explainable systems. While these advances will greatly advance humanity, organizations such as the United Nations have embarked on initiatives such as "AI for Good" and we can expect to see more emphasis on applying AI for the good of humanity especially in developing countries. However, the question that needs to be answered is Can AI be for Good when when the AI techniques can be attacked and the AI techniques themselves can cause privacy violations? This position paper will provide an overview of this topic with protecting children and children's rights as an example.
Best-Worst-Play (BWP): A metaphor-less Optimization Algorithm

Abstract A novel algorithm which is an ensemble of two metaphor-less algorithms is presented in this paper. The algorithm is inspired by Rao-1 and Jaya algorithms. Since the algorithm always plays around with the best and worst solutions; the algorithm is named as Best-Worst-Play (BWP) algorithm. The algorithm does not require any algorithm specific parameters, however, algorithm control parameters are required. To test the effectiveness and performance of the proposed algorithm, a number of unconstrained and constrained benchmark functions are considered. It is found that proposed algorithm has outperformed several well-established metaphor based algorithms. The proposed BWP algorithm may be used by researchers to solve the unconstrained and constrained optimization problems
An accelerometer based fall detection system using Deep Neural Network

Fall detection has integral role in elderly health care. Inertial sensors are popular choices to develop fall detection algorithms due to their portability, wearability, and no privacy concerns. Previous algorithms have used acceleration based features for fall detection; however such algorithms require feature engineering skills. In this paper, we propose a Deep Neural Network(DNN) for fall detection. A 5 seconds waveform, unfiltered and low-pass filtered with 10 Hz and 20 Hz, is fed to DNN for fall detection. The model is validated on two different datasets available online. The results report 95.1% classification accuracy, 94.4% sensitivity, 95.5% specificity for a dataset and 86% classification accuracy, 75% sensitivity, 92.1% specificity for other dataset. For the feasibility in real time application, the algorithm was implemented on an android based mobile system. The average computation time was noted to be 50 milliseconds. Finally, the algorithm proved to be independent of filtering operations (p = 0.551) suggesting the approach to be useful in noisy environment.
ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection.

Autoencoders (AE) have recently been widely employed to approach the novelty detection problem. Trained only on the normal data, the AE is expected to reconstruct the normal data effectively while fail to regenerate the anomalous data, which could be utilized for novelty detection. However, in this paper, it is demonstrated that this does not always hold. AE often generalizes so perfectly that it can also reconstruct the anomalous data well. To address this problem, we propose a novel AE that can learn more semantically meaningful features. Specifically, we exploit the fact that adversarial robustness promotes learning of meaningful features. Therefore, we force the AE to learn such features by penalizing networks with a bottleneck layer that is unstable against adversarial perturbations. We show that despite using a much simpler architecture in comparison to the prior methods, the proposed AE outperforms or is competitive to state-of-the-art on three benchmark datasets.
Quantum image classifier with single photons

Machine learning, with promising applications in quantum computation, has been introduced to a variety of quantum mechanical platforms, where its interplay with quantum physics offers exciting prospects toward quantum advantages. A central difficulty, however, lies in the access and control of the large Hilbert space required by quantum machine learning protocols, through the limited number of noisy qubits available to near-term quantum devices. Whereas it is recognized that a viable solution lies in the design of quantum algorithms that incorporates quantum entanglement and interference, a demonstration of quantum machine learning protocols capable of solving practical tasks is still lacking. Here we report the classification of real-life, hand-drawn images on a quantum mechanical platform of single photons. Adopting a tensor-network-based machine learning algorithm with an entanglement-guided optimization, we achieve an efficient representation of the quantum feature space using matrix product states. This allows us to demonstrate image classification with a high success rate using single-photon interferometry networks. Our experiment establishes a general and scalable framework for quantum machine learning, which is readily accessible on other physical platforms.
Optimization in the construction of nearly cardinal and nearly symmetric wavelets

We present two approaches to the construction of scaling functions and wavelets that generate nearly cardinal and nearly symmetric wavelets on the line. The first approach casts wavelet construction as an optimization problem by imposing constraints on the integer samples of the scaling function and its associated wavelet and with an objective function that minimizes deviation from cardinality or symmetry. The second method is an extension of the feasibility approach by Franklin, Hogan, and Tam to allow for symmetry by considering variables generated from uniform samples of the quadrature mirror filter, and is solved via the Douglas-Rachford algorithm.
Scholarly publication venue recommender systems

Purpose The purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR) on the application of recommender systems (RSs) to suggest a scholarly publication venue for researcher's paper. Design/methodology/approach To identify the relevant papers published up to August 11, 2018, an SLR study on four databases (Scopus, Web of Science, IEEE Xplore and ScienceDirect) was conducted. We pursued the guidelines presented by Kitchenham and Charters (2007) for performing SLRs in software engineering. The papers were analyzed based on data sources, RSs classes, techniques/methods/algorithms, datasets, evaluation methodologies and metrics, as well as future directions. Findings A total of 32 papers were identified. The most data sources exploited in these papers were textual (title/abstract/keywords) and co-authorship data. The RS classes in the selected papers were almost equally used. DBLP was the main dataset utilized. Cosine similarity, social network analysis (SNA) and term frequency–inverse document frequency (TF–IDF) algorithm were frequently used. In terms of evaluation methodologies, 24 papers applied only offline evaluations. Furthermore, precision, accuracy and recall metrics were the popular performance metrics. In the reviewed papers, “use more datasets” and “new algorithms” were frequently mentioned in the future work part as well as conclusions. Originality/value Given that a review study has not been conducted in this area, this paper can provide an insight into the current status in this area and may also contribute to future research in this field.
VarMixup: Exploiting the Latent Space for Robust Training and Inference

The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has led to the development of many defense approaches. Among them, Adversarial Training (AT) is a popular and widely used approach for training adversarially robust models. Mixup Training (MT), a recent popular training algorithm, improves the generalization performance of models by introducing globally linear behavior in between training examples. Although still in its early phase, we observe a shift in trend of exploiting Mixup from perspectives of generalisation to that of adversarial robustness. It has been shown that the Mixup trained models improves the robustness of models but only passively. A recent approach, Mixup Inference (MI), proposes an inference principle for Mixup trained models to counter adversarial examples at inference time by mixing the input with other random clean samples. In this work, we propose a new approach - \textit{VarMixup (Variational Mixup)} - to better sample mixup images by using the latent manifold underlying the data. Our experiments on CIFAR-10, CIFAR-100, SVHN and Tiny-Imagenet demonstrate that \textit{VarMixup} beats state-of-the-art AT techniques without training the model adversarially. Additionally, we also conduct ablations that show that models trained on \textit{VarMixup} samples are also robust to various input corruptions/perturbations, have low calibration error and are transferable.
On the analysis of hyper-parameter space for a genetic programming system with iterated F-Race

Evolutionary algorithms (EAs) have been with us for several decades and are highly popular given that they have proved competitive in the face of challenging problems' features such as deceptiveness, multiple local optima, among other characteristics. However, it is necessary to define multiple hyper-parameter values to have a working EA, which is a drawback for many practitioners. In the case of genetic programming (GP), an EA for the evolution of models and programs, hyper-parameter optimization has been extensively studied only recently. This work builds on recent findings and explores the hyper-parameter space of a specific GP system called neat-GP that controls model size. This is conducted using two large sets of symbolic regression benchmark problems to evaluate system performance, while hyper-parameter optimization is carried out using three variants of the iterated F-Race algorithm, for the first time applied to GP. From all the automatic parametrizations produced by optimization process, several findings are drawn. Automatic parametrizations do not outperform the manual configuration in many cases, and overall, the differences are not substantial in terms of testing error. Moreover, finding parametrizations that produce highly accurate models that are also compact is not trivially done, at least if the hyper-parameter optimization process (F-Race) is only guided by predictive error. This work is intended to foster more research and scrutiny of hyper-parameters in EAs, in general, and GP, in particular.
Training in shaping employee information security awareness

The purpose of this paper is to present the effectiveness of training in the development of employee awareness in the area of information security. Two kinds of primary research were carried out: surveys conducted among employees of various organizations, the essence of which involved a comparison of the awareness level in terms of security among people who had participated and those who had not participated in information security training; and a comparative analysis of results of an audit of information security awareness conducted among employees of a large organization before and after conveying information security training. Research results showed significant effectiveness of training as a method not only of information security knowledge extension but also, and most importantly, one that has a significant impact on actual behaviors of employees in the studied area. Due to the fact that the greatest gap in security measures involves the lack of employee awareness, and because training is an effective method of shaping the said awareness, organizations should develop and implement an adequate training program raising the level of employee awareness in terms of information security. It should be remembered that the program cannot be a one-off event but rather a cyclical one. While the importance of awareness in information security is well described in the subject literature, there is a shortage of publications, which show a direct influence of training on employees’ level of knowledge and behaviors in terms of information security. This paper, in an interesting, dual way, points to an actual impact of training both on expanding knowledge and on behaviors in terms of information security.
Multiobjective Coyote Algorithm Applied to Electromagnetic Optimization

The Coyote Optimization Algorithm (COA) is a population-based nature-inspired metaheuristic for global optimization that considers the social relations of the coyote proposed originally to single-objective optimization. In this paper, the numerical results are reported to validate a novel proposed multiobjective COA (MOCOA) to solve the Testing Electromagnetic Analysis Method (TEAM) workshop benchmark problem 25. Simulation results demonstrate the validity of the proposed MOCOA to find nondominated solutions that represent good trade-offs among the objectives in the evaluated problem.
SSD-ML: Hierarchical Object Classification for Traffic Surveillance

We propose a novel CNN detection system with hierarchical classification for traffic object surveillance. The detector is based on the Single-Shot multibox Detector (SSD) and inspired by the hierarchical classification used in the YOLO9000 detector. We separate localization and classification during training, by introducing a novel loss term that handles hierarchical classification. This allows combining multiple datasets at different levels of detail with respect to the label definitions and improves localization performance with non-overlapping labels. We experiment with this novel traffic object detector and combine the public UADETRAC, MIO-TCD datasets and our newly introduced surveillance dataset with non-overlapping class definitions. The proposed SSD-ML detector obtains 96:4% mAP in localization performance, outperforming default SSD with 5:9%. For this improvement, we additionally introduce a specific hard-negative mining method. The effect of incrementally adding more datasets reveals that the best performance is obtained when training with all datasets combined (we use a separate test set). By adding hierarchical classification, the average classification performance increases with 1:4% to 78:6% mAP. This positive result is based on combining all datasets, although label inconsistencies occur in the additional training data. In addition, the final system can recognize the novel ‘van’ class that is not present in the original training data.
Precise Positioning of Linear Motor Mover Directly From the Phase Difference Analysis

This article presents a new visual detection method with high precision, robustness, and processing rate to estimate the linear motor displacements. Subpixel level location is achieved by using the phase-difference matrix and a structured aperiodic fence stripe placed on the mover. Based on Fourier shifted properties, this subpixel method builds a linear relation between the mover's spatial displacements and the direct frequency-domain phase difference (not the phase correlation matrix) to calculate the translational shift in a series of stripe images; thereby, precisely determining the mover location of linear motor. Unlike the traditional phase correlation algorithm, the proposed method converts 2-D stripe images into 1-D signals via the projection technique and then uses the least squares method to linearly fit the phase difference slope, thereby acquiring subpixel shifts with less time complexity. Due to the motion parameter that is directly estimated from the phase difference, this method is also insensitive to the gray-scale change with certain anti-interference capability, thereby making it suitable for measuring the moving position of the mover. Experimental results show that the proposed vision-based detection method is efficient and applicable for real-time accurate positioning of linear motor, and the measuring root mean square error is about 0.05 mm.
A congestion-aware decision-driven architecture for information-centric Internet-of-Things applications

This paper introduces a decision-driven architecture for the information-centric Internet-of-Things (IoT) scenarios. The hallmark of this work is the conjugate of the awareness about the congestion situations along with the quality of the decisions’ supporting information. Controlling the incoming decisions’ upsurge is accomplished on a fashion that respects their degree of emergency. Wherefore, the Active Weighted Random Early Detection (AWRED) algorithm is integrated into the autonomic admission module to underpin the congestion control concept. Afterwards, an adaptive information-centric scheduling module is proposed in order to devise an optimized information-aware execution schedule for the admitted real-time decisions. The proposed scheduling algorithm is augmented with a Global best Harmony search (GHS) based amending approach. This integrated scheduling concept empowers the proposed algorithm from tuning the schedules of the admitted real-time decisions such that the completeness opportunities of decisions are elevated without sacrificing the rest architecture objectives. Simulations results demonstrate the robustness of the proposed architecture performance against various congestion degrees, an essential feature in a highly dynamic environment as the IoT. Furthermore, it has been proved that the proposed architecture significantly surpasses state-of-art approaches in terms of both quality of information (QoI) and quality of service (QoS) obligations especially during congested situations.
Neural Networks are Surprisingly Modular

The learned weights of a neural network are often considered devoid of scrutable internal structure. To discern structure in these weights, we introduce a measurable notion of modularity for multi-layer perceptrons (MLPs), and investigate the modular structure of MLPs trained on datasets of small images. Our notion of modularity comes from the graph clustering literature: a module is a set of neurons with strong internal connectivity but weak external connectivity. We find that training and weight pruning produces MLPs that are more modular than randomly initialized ones, and often significantly more modular than random MLPs with the same (sparse) distribution of weights. Interestingly, they are much more modular when trained with dropout. We also present exploratory analyses of the importance of different modules for performance and how modules depend on each other. Understanding the modular structure of neural networks, when such structure exists, will hopefully render their inner workings more interpretable to engineers. Note that this paper has been superceded by Clusterability in Neural Networks, arXiv:2103.03386!
Dependency Treebanks of Ancient Greek Prose

This dataset is a collection of dependency syntax trees of representative texts from ancient Greek prose authors (Aeschines, Antiphon, Appian, Athenaeus, Demosthenes, Dionysius of Halicarnassus, Herodotus, Josephus, Lysias, Plutarch, Polybius, Thucydides, and Xenophon), totaling to date 550,000+ tokens. It is hand-annotated by one person, using the Arethusa program on the Perseids website. Original texts were obtained from the Perseus Digital Library, and some (as indicated) were computer pre-parsed at the Pedalion Project. The database is stored in a stable form (2019-12-31) on Zenodo (DOI: 10.5281/zenodo.3596076) and in a continuously updated form on GitHub in .xml format (https://vgorman1.github.io/). The repository can be used for pedagogical purposes and for research in linguistics analysis and corpus linguistics, stylistics, natural language processing, classification, and literary and historical analysis.
A direct proof of APN-ness of the Kasami functions

Using recent results on solving the equation $$X^{2^k+1}+X+a=0$$ over a finite field $${{\mathbb {F}}}_{2^n}$$ provided by the second and the third authors, we address an open question raised by the first author in WAIFI 2014 concerning the APN-ness of the Kasami functions $$x\mapsto x^{2^{2k}-2^k+1}$$ with $$\gcd (k,n)=1$$ , $$x\in {{\mathbb {F}}}_{2^n}$$ .
A Real-Time Architecture of 360-Degree Panoramic Video Streaming System

In this paper, we discuss how to develop a system architecture that produces and transmits 360 degree panoramic video data with multiple cameras in real-time. Our system consists of three stages; panoramic video production using ORB(Oriented FAST and rotated BRIEF)[1] feature point detection, H.264 video compression format and RTSP(Real Time Streaming Protocol) streaming. In particular, 360 degree panoramic video is produced by acceleration of camera calibration, stitching, blending and encoding processes by GPU parallel programming named “CUDA”. We measured the fps(frames per second) of panoramic video streaming that is transmitted to the client and verified that our system has at least 30fps at 4K output resolution. Thus, we could see that our system is suitable for 360 degree panoramic video streaming service in real time.