Reviewerly/exHarmony huggingface dataset:
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence

Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting. While most existing work studies the problem in the context of computer vision or console games, this paper focuses on reinforcement learning in autonomous cyber defence under partial observability. We demonstrate that under the black-box setting, where the attacker has no direct access to the target RL model, causative attacks-attacks that target the training process-can poison RL agents even if the attacker only has partial observability of the environment. In addition, we propose an inversion defence method that aims to apply the opposite perturbation to that which an attacker might use to generate their adversarial samples. Our experimental results illustrate that the countermeasure can effectively reduce the impact of the causative attack, while not significantly affecting the training process in non-attack scenarios.
Protecting Electronic Evidence with Block Chain and Secret Sharing

We present a novel method to protect the electronic evidence with block chain and secret sharing. Block chain, with the capability of protecting the integrity of the data, is considered as the way to protect the electronic evidence. However, there is a time windows to be tampered with the evidence before the data are organized as the block in the chain. To overcome it, we protect the data with secret sharing by breaking the data into some shares which is kept by some different node. Thus, it is difficult to subvert the data because the attacker have to subvert these nodes. Our discussion show that our method is able to protect the electronic evidence.
Fast Adaptable Mobile Robot Navigation in Dynamic Environment

Autonomous navigation in dynamic environment heavily depends on the environment and its topology. Prior knowledge of the environment is not usually accurate as the environment keeps evolving in time. Since robot is continuously evaluating the environment as it proceeds, deciding the optimal way to traverse the environment to get to the goal, computationally efficient yet mathematically adaptive navigation algorithms are needed. In this paper, a navigation scheme for mobile robot, capable of dealing with time variant environment is proposed. This approach consists of a global planner (A*) and local planner (VFH) to assure an optimal and collision-free robot motion. The algorithm is tested both in simulation and experimentation in different environments that are known to result in failures in VFH and ROS navigation stack, for comparison purposes. Overall, the algorithm enables the robot to get to the goal faster and also produces a smoother path while doing so.
A SCENARIO OF CYBER CRIME IN CHHATTISGARH

— Cyber crimes like identity theft, PC forgery, ICR crime, online harassment, fake e-mail, ATMfraud, porn crime , copy write, are gradually increasing in Chhattisgarh. Criminals do this by sending fakefinancial and lottery offers through mail and messageto victim. This is the greatest threat to Chhattisgarh’se-commerce growth.This is the greatest threat to Chhattisgarh's e-commerce growth . Therefore, internet users knowledgeshould be updated about the ever changing nature of cyber crime In Chhattisgarh cyber crimes graph areincresing day by day such as identity theft, pc counterfeiting, ICR crime, online harassment, fack e- mails ,ATM froad, spoofing, pornography, piracy, hacking, phishing and spamming commonly these crimes aredone by sending trippy and fake financial offers/mail from criminals to victim.Therefore, internet users should be updated about the ever changing nature of the cyber crime , we shouldnot only inform them about the latest trends of internet related crimes, at the same time, they will be awareof many types of cyber crimes, so that the activities of cyber criminals can be avoided. Thus, from this wecan understand about the safety of data from the criminals. This paper evaluate the scenario of cyber crimein the state of Chhattisgarh.
Gradual Chroma Reduction and High-Level Visual Masking in Videos

This study investigates the inability of the human vision system to detect gradual changes in video quality, specifically chroma reduction. We present the results from a subjective study, in which participants compared video stimuli of three different types: pristine, mildly chroma-reduced background with a fixed rate, chroma reduced with a gradual rate from a pristine first frame to a fully achromatic background in the last one. Our results show that the second type yields quality ratings that are significantly lower than those of the gradually modified samples, which are on par with those of the pristine samples.
Searching the Adversarial Example in the Decision Boundary

Deep learning technology achieves state of the art result in many computer vision missions. However, some researchers point out that current widely used deep learning architectures are vulnerable to adversarial examples. Adversarial examples are inputs generated by applying small and often imperceptible perturbation to examples in the dataset, such that the perturbed examples can degrade the performance of the deep learning architecture.In the paper, we propose a novel adversarial examples generation method. Adversarial examples generated using this method can have small perturbation and have more diversity compare to adversarial examples generated by other method.
ADMM Consensus for Deep LSTM Networks

In modern real-world applications, the need of using a decentralized data processing approach has progressively increased, facing complexity and handling issues. Pervasive data and ubiquitous computational capacity have enabled the proficient use of distributed implementation of machine learning algorithms, especially for forecasting problems. We provide in this paper a new, fully distributed prediction approach based on the Long Short-Term Memory deep neural network. When placed in a network of interconnected agents, the single predictors are able to improve the prediction accuracy by means of the Alternating Direction Method of Multipliers consensus procedure on some network parameters. Experimental tests on real-world time series prove the efficacy of the proposed approach, which regulates the information exchange in the network through high-level structures in the considered models.
Prompting Metacognition via Video-Based Abductive Reasoning Tasks

Metacognition or “thinking about thinking” involves processes vital to higher-order thought that modern educators need to integrate into their learning outcomes when engaged in sufficiently rigorous course design. Due to the many responsibilities and disrupted nature of adult learners’ education, self-access learning is convenient in saving time and cost; and, when coupled with quality, focused video content, it can provide not just enticing, but also engaging, curricula. This chapter presents an ethnographic case study of an intermediate-level English pragmatics course using a video-based curriculum. Presented here are the findings about tasks involving abductive reasoning which have been evaluated for their success at prompting metacognition. Formal in-class learning and informal out-of-the-class mobile learning are compared. English pragmatics focuses on the often unspoken or indirectly communicated world of speakers’ intentions. The chapter concludes that to truly unlock speaking as a macro skill for second language learners is not simply a matter of “more practice, more confidence” but rather building up a learner’s confidence in his/her analytical judgement.
Monoceros: A New Approach for Training an Agent to Play FPS Games

In the deep reinforcement learning, the sparse reward problem directly impacts the quality of agent training. Existing methods have not been satisfactory, especially for the scenarios with high-dimensional state information. In this paper, we propose a new approach Monoceros to training a game agent. Monoceros can work for the scenarios with high-dimensional state information and alleviate the sparse reward problem during the agent training. Specifically, we present a composite reward function which combines both the knowledge implied in expert trajectories and manually-set reward functions. Moreover, we design a specific policy network to adapt to the high-dimensional information scenarios, and adopt the behavior clone as a pre-training strategy to accelerate the training process. Technically, Monoceros can be applied to train the agents to play First Person Shooter (FPS) games. We conduct extensive experiments on three scenarios in the VIZDoom platform. Experimental results show that in all the scenarios, the agent trained by Monoceros outperforms the agents trained by Arnold and GAIL, which are representative methods in the deep reinforcement learning and the imitation learning, respectively.
Hybrid Warfare, International Negotiation, and an Experiment in “Remote Convening”

The authors are leading a multinational effort to understand the effects of “hybrid” warfare on international commercial negotiation. The start‐up process is itself essentially a negotiation, among about forty individual practitioners and scholars with very diverse backgrounds, over whether and how they will work together. In a pandemic, a key risk is that the necessary cooperation and trust will be harder to build, particularly among professionals who are dealing with security‐sensitive issues and who have never met each other. This article discusses the current necessity of replacing the in‐person model for eliciting such cooperation which the authors had developed previously for large collaborative projects, and describes a “remote convening” replacement process.
The Practice of College English in the Classroom Under the Background of "Internet + Education"

The basic idea of the model of divided class (PAD class) is to divide the class time into two parts, half for the teacher's explanation, half for the students' group cooperation and discussion. The two parts are staggered to ensure that students have enough time to learn and internalize what they have learned. The classes are divided into three parts: teaching, internalization and discussion. The author applies the class-based teaching method to college English reading teaching and analyzes the influence of class-based teaching on students' reading ability and interest by using clustering algorithm.
Speech Pauses and Dialogue Acts

This study concerns the use of speech pauses, and especially breath pauses in a Danish corpus of spontaneous dyadic conversations. Speech pauses which have specific communicative functions are investigated in relation to their occurrences before and after other communicative units, all annotated and classified in the form of dialogue acts. Breath pauses have been addressed in only few studies even though they are important in communication and therefore should be accounted for when implementing human-machine dialogue systems. Dialogue acts, on the contrary, have been one of the backbones in dialogue systems since they generalize over different expressions of common communicative functions. In the current work, we describe the annotation of dialogue acts in the corpus and present an analysis of pauses using these annotations. To our best knowledge, dialogue acts have not been previously used for analyzing the functions of breath pauses. Our analysis shows that the most common type of pause having a communicative function in the Danish conversations are breath pauses. Breath pauses in the corpus have different uses, one of these being that of delimiting speech segments which are left unfinished and are then abandoned by the speaker (retractions in dialogue acts terminology) and therefore perceivable breathing can be a useful feature for determining spoken segments which must not be included in the dialogue history in human-machine dialogue systems.
The Solution of Approximation Problems on RBF Networks Learned by The Levenberg Marquardt Method

A promising tool for solving problems of approximating functions, including problems of gridless approximation, is radial basis functions networks, which is a special type of neural networks. Since the solution of the problem has formed in the process of network learning, it is important to develop fast learning algorithms for networks. Currently, first-order gradient methods have used to learn radial basis functions networks. These methods have a low rate of convergence and practically do not allow to achieve high accuracy of approximation for complex functions. The article proposes to use the Levenberg-Marquardt method for learning the network in solving approximation problems. An algorithm for learning the network by the Levenberg-Marquardt method has developed. The algorithm takes into account the specifics of the architecture of networks of radial basis functions and uses analytical calculation of the elements of the Jacobi matrix. The effectiveness of the algorithm has confirmed by solving model problems.
Nonparametric Bayesian Analysis of Hazard Rate Functions using the Gamma Process Prior

When failure time data are modelled using an inhomogeneous Poisson process, it is necessary to model the underlying hazard rate function λ(t). The most common approaches to the problem either select some parametric form for λ(t), or alternatively - conditional on some collected data set - approximate it using the non-parametric Kaplan-Meier estimator. In this paper we present simulation and inference for a non-parametric hazard rate function drawn from a Gamma Process Prior. We use a gamma-scaled Dirichlet Process prior to implement the Gamma Process prior, and construct a Markov Chain Monte Carlo sampler to carry out inference. We demon-strate the methodology with the simulation of a process with an increasing failure rate.
Distance-Dependent Barcodes for Context-Aware Mobile Applications

This article introduces the novel concept of distance-dependent barcodes, which provide users with different data based on their scanning distance. These barcodes employ color blending as the key technique to achieve distance-dependence. A simple yet robust encoding scheme is devised accordingly to distinguish between near and far users. Through several experimental results, the proposed technique is shown to be effective (in terms of clear separation between near and far scanners), reliable (as to successful scans), and practical (it can be used in off-the-shelf smartphones). A few representative use cases are then presented to establish distance-dependent barcodes as an enabling technology for context-aware mobile applications. They include casual interactions with public displays, where the role of users is determined based on their distance from a screen, and augmented reality in retail, where distance-dependent barcodes provide information on available goods with different granularities. Finally, distance-dependent barcodes are shown to be user-friendly and effective through a user study.
Design of a Morlet wavelet control algorithm using super–twisting sliding modes applied to an induction machine

In this paper, a Morlet wavelet and super-twisting control algorithm are designed and implemented to a three-phase induction motor. The mathematical model of the squirrel-cage induction motor to be controlled is approximated by the Morlet wavelet artificial neural network, which is trained on-line with the error filtered algorithm in order to reproduce the dynamics of the plant to be controlled. The structure of the artificial neural network is proposed in series-parallel configuration and block control form to design the sliding variety, where the super-twisting control algorithm is applied indirectly. For the non-measurable state variables of the plant, state observers of the super-twisting type are proposed to feed the inputs of the artificial neural network. The simulation of the complete system in closed loop is performed where the variables to be controlled are the angular velocity and the square modulus of flux linkages. The results obtained in Matlab/Simulink validate the efficiency of the proposed neural network for the identification of states and the application of the controller.
A new batch SOM algorithm for relational data with weighted medoids

The great majority of previous works on SOM concern quantitative vectorial data. Nowadays, relatively few SOM algorithms are able to manage relational data despite their usefulness. This paper proposes a new batch SOM algorithm for relational data with weighted medoids. The particularity of the proposed approach is to consider the cluster representatives as vectors of weights whose components measure how objects are weighted as a medoid in a given cluster. From an initial solution and for a fixed epoch and radius, the proposed training batch SOM algorithm provides a partition and cluster representatives by optimizing a suitable objective function aiming to preserve the topological properties of the data on the map. Experiments with datasets of UCI machine learning repository, in comparison with relevant medoid-based batch SOM for relational data algorithms, showed the usefulness of the proposed method.
Bayesian Generalized Horseshoe Estimation of Generalized Linear Models

Bayesian global-local shrinkage estimation with the generalized horseshoe prior represents the state-of-the-art for Gaussian regression models. The extension to non-Gaussian data, such as binary or Student-t regression, is usually done by exploiting a scale-mixture-of-normals approach. However, many standard distributions, such as the gamma and the Poisson, do not admit such a representation. We contribute two extensions to global-local shrinkage methodology. The first is an adaption of recent auxiliary gradient based-sampling schemes to the global-local shrinkage framework, which yields simple algorithms for sampling from generalized linear models. We also introduce two new samplers for the hyperparameters in the generalized horseshoe model, one based on an inverse-gamma mixture of inverse-gamma distributions, and the second a rejection sampler. Results show that these new samplers are highly competitive with the no U-turn sampler for small numbers of predictors, and potentially perform better for larger numbers of predictors. Results for hyperparameter sampling show our new inverse-gamma inverse-gamma based sampling scheme outperforms the standard sampler based on a gamma mixture of gamma distributions.
An Intelligent and Comprehensive Monitoring System for Health and Rehabilitation Center

With the development of modern society, health and rehabilitation centers are playing an increasingly important role. However, the shortage of medical resources and the low work efficiency of centers are becoming more and more prominent. In this paper, an intelligent and comprehensive monitoring system is designed for modern health and rehabilitation centers. Base on a variety of innovative sensors and facilities, the monitoring system can perceive and assemble multiple information automatically and continuously, such as a patient's daily physiological characteristics, rehabilitation training records, ward environment parameters, etc. All the data will be transmitted to mobile terminals and a cloud database. The proposed system can help medical staff evaluate the state of patients accurately and comprehensively when assigning the prescription and formulating treatment plans. In addition, it is of great significance for health and rehabilitation centers by releasing medical resources and improving the service efficiency.
Tangible Storytelling to Learn the Four Seasons: Design and Preliminary Observations

Tangible user interfaces offer a new medium for education that support concept development, with physical and virtual representations. It has manipulative properties allow students to utilize their haptic and motor skills which encourage learning and motivation. In this paper we developed an educational game based on a tangible environment that support interactive technology aiming to serve and enhance the learning process within the sciences field, we selected “The Four Seasons” topic. We proposed handcrafted objects as the physical objects that motivate kids to create their own stories for each season based on the available objects that represent basic elements for each season. We discussed the design process and development iterations as well as preliminary findings. This system has been evaluated in a field study with kids 4–7 years of age. We discuss the role of technology which can play in supporting children’s everyday creative storytelling and how it can raise the enthusiasm for leaning and engaging kids to the learning process.
세종실록을 통해 본 왕세자(문종) 교육의 특징

In this thesis, we look at the education system of the King Sejong during the Joseon Dynasty and the educational content of Munjong who was a Crown Prince at that time. We also look at the characteristics of the royal education during the early Joseon period, and why it was possible for the King Munjong to make various achievements during his short reign.King Sejong was the third son of Taejong, therefore, he was able to develop various disciplines without receiving education for the Crown Prince. These experiences of King Sejong became the basis of his interest in education for the Crown Prince and the eldest grandson of the King after he became the King. King Sejong, in particular, established the foundation of the education for Crown Prince of Joseon after the throne at the center of Jiphyeonjeon through various subjects in the Royal presence.In 1421 (the third year of King Sejong''s reign), King Munjong was educated as a Prince for up to 30 years, and he ruled the country on behalf of King Sejong for eight years. King Munjong''s diverse achievements during his short two-year reign, regardless of literature and martial arts, stemmed from King Sejong''s thorough education.The process of being born in Joseon as a Prince and ascertained to become a King is not only a lecture but also a practical process of taking care of the affairs of state administration and learning martial arts. In addition, the Prince should not only build Confucian knowledge but also cultivate military knowledge through activities such as archery.As such, King Munjong, who grew up under King Sejong''s systematic education, was able to rise safely to the throne and leave various achievements throughout the period of his reign. The education for the Prince during the early Joseon Dynasty can be seen as a process of growing into a sage King that can rule the nation by accumulating the basic knowledge of Confucianism, the founding ideology of Joseon.
VersaTile Convolutional Neural Network Mapping on FPGAs

Convolutional Neural Networks (ConvNets) are directed acyclic graphs with node transitions determined by a set of configuration parameters. In this paper, we describe a dynamically configurable hardware architecture that enables data allocation strategy adjustment according to ConvNets layer characteristics. The proposed flexible scheduling solution allows the accelerator design to be portable across various scenarios of computation and memory resources availability. For instance, FPGA block-RAM resources can be properly balanced for optimization of data distribution and minimization of off-chip memory accesses. We explore the selection of tailored scheduling policies that translate into efficient on-chip data reuse and hence lower energy consumption. The system can autonomously adapt its behavior with no need of platform reconfiguration nor user supervision. Experimental results are presented and compared with state-of-the-art accelerators.