Reviewerly/exHarmony huggingface dataset:
CIExplore

Learning in a sparse-reward setting is a well-known challenge in RL (Reinforcement Learning). In the single-agent domain, this challenge can be addressed by introducing exploration bonuses driven by intrinsic motivation to encourage agents to visit unseen states. However, naively applying these methods in MARL (Multi-Agent Reinforcement Learning) cooperative settings with sparse rewards results in some inevitable problems: misunderstanding environmental knowledge and lack of collaboration among agents, etc. Based on this, in this paper, we propose the Curiosity and Influence-based Explore (CIExplore) method, which includes a new form of intrinsic reward and an internal counterfactual advantage function. Concretely, the intrinsic reward is a combination of joint curiosity reward and influence reward. The former is the variance of outputs across an ensemble of prediction models that take joint observations and actions of all agents as inputs to predict the next time's joint observations. And the latter quantifies the influence of one agent's behavior on other agents' state-value functions. Given that the joint curiosity reward is shared by all agents, we compute an internal counterfactual advantage function to address this intrinsic reward assignment problem. We demonstrate the efficacy of CIExplore in the multi-agent grid-world environments and show that it is compatible with both on-policy and off-policy MARL algorithms and be scalable to complex settings where agents' number or environment randomness increases.
Exploration and Implementation of RSA-KEM Algorithm

Cryptography is used to protect information. Encryption and decryption can confirm the confidentiality, integrity of information and protect information from tampering, forgery and counterfeiting. RSA (Rivest, Shamir, Adleman) uses two keys: private key and public key. RSA-KEM (key encapsulation mechanism) is a hybrid encryption algorithm that uses RSA trapdoor permutation along with a key derivation function. RSA is more powerful than other symmetric key algorithms, and its keys are two large randomly generated prime numbers. It is hard to factor a large prime number which is a product of two prime numbers. This paper presents an implementation of a RSA-KEM encryption decryption based on RSA public key algorithm and KEM. RSA contains three functions: key generation, encryption and decryption. In RSA-KEM, password-based key derivation function is used for key stretching. Password, salt and iteration are used for generation of derived key. Advanced encryption standard is used for key-wrapping scheme. AES is a vigorous security algorithm and can be implemented in both hardware and software. It is resilient to hacking attempts due to long key lengths of 128,192 and 256 bits. RSA-KEM creates a random integer ‘r’, manages symmetric encryption key by ‘r’ along key derivation function (KDF) and then encodes ‘r’ with RSA.
Discovering Time-invariant Causal Structure from Temporal Data

Discovering causal structure from temporal data is an important problem in many fields in science. Existing methods usually suffer from several limitations such as assuming linear dependencies among features, limiting to discrete time series, and/or assuming stationarity, i.e., causal dependencies are repeated with the same time lag and strength at all time points. In this paper, we propose an algorithm called the μ-PC that addresses these limitations. It is based on the theory of μ-separation and extends the well-known PC algorithm to the time domain. To be applicable to both discrete and continuous time series, we develop a conditional independence testing technique for time series by leveraging the Recurrent Marked Temporal Point Process (RMTPP) model. Experiments using both synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm.
Emoji-Based Co-Attention Network for Microblog Sentiment Analysis

Emojis are widely used in online social networks to express emotions, attitudes, and opinions. As emotional-oriented characters, emojis can be modeled as important features of emotions towards the recipient or subject for sentiment analysis. However, existing methods mainly take emojis as heuristic information that fails to resolve the problem of ambiguity noise. Recent researches have utilized emojis as an independent input to classify text sentiment but they ignore the emotional impact of the interaction between text and emojis. It results that the emotional semantics of emojis cannot be fully explored. In this paper, we propose an emoji-based co-attention network that learns the mutual emotional semantics between text and emojis on microblogs. Our model adopts the co-attention mechanism based on bidirectional long short-term memory incorporating the text and emojis, and integrates a squeeze-and-excitation block in a convolutional neural network classifier to increase its sensitivity to emotional semantic features. Experimental results show that the proposed method can significantly outperform several baselines for sentiment analysis on short texts of social media.
Modelling Disruptive APTs targeting Critical Infrastructure using Military Theory

Disruptive Advanced Persistent Threats (D-APTs) are a new sophisticated class of cyberattacks targeting critical infrastructures. Whereas regular APTs are well-described in the literature, no existing APT kill chain model incorporates the disruptive actions of D-APTs and can be used to represent DAPTs in data. To this aim, the contribution of this paper is twofold: first, we review the evolution of existing APT kill chain models. Second, we present a novel D-APT model based on existing ATP models and military theory. The model describes the strategic objective setting, the operational kill chain and the tactics of the attacker, as well as the defender’s critical infrastructure, processes and societal function.
Anti-Counterfeit Handwritten Signature via DCGAN with SGPD Network

In recent years, the growth of machine learning makes the computer can learn many things by using artificial intelligence. One method that is feared nowadays is the computer's capability to imitate something. This capability is called deep-fake. Deep-fake is the capability of the computer to imitate human characteristics such as voice, images, and video through artificial intelligence. Deep-fake is used to combine put the consisted image and video to another source of images and video using machine learning which is known as a generative adversarial network. With these capabilities, deep-fake is already used to make a counterfeit video, signature, voice signature, and much fake news. This paper is about to combine the capabilities of deep learning and the Generative Adversarial Network (GAN) to deal with detecting the fraud in the handwritten signature. We will focus on several types of ways to sign with the characters. The system will recommend if the hand signature of the user is fake or genuine. This is under the capabilities of GAN to synthesize the signature, it can make the computer automatically generate hand signature by using a machine. Many researchers called this capability is deep-fake. This research aims to learn the hand signature to do fraud detection. We propose an architecture to build the anti-counterfeiting hand signature which is utilized deep learning with a self-growing probabilistic method.
Integrated Plant Growth and Disease Monitoring with IoT and Deep Learning Technology

At present, the time, labor, and inaccuracies in plant and seedling care make feasibility a major concern in large-scale agricultural operations. Developments in Internet of Things (IoT) technology and image classification by deep learning have made it possible to monitor various aspects of plant conditions, but an integrated solution that combines IoT sensor data, high-resolution imagery, and manual intervention data in a synchronized time-series database environment has not yet been brought to market. In this paper, we propose such an integrated solution. The overall system architecture is outlined, as well as the individual components including sensors, drone imagery, image processing, database framework, and alerting mechanism. These components are brought together and synchronized in a time-series database. By synchronizing all the variables, this solution presents a comprehensive view and better means for intervention. Finally, opportunities for research and specific component improvements are identified.
Specifying Autonomous Driving Scenarios

Defects in Autonomous driving systems (ADSs) might result in catastrophic losses of lives and properties. To avoid such defects, we need to first ensure high quality requirements, which highly possibly would lead to the delivery of high-quality ADSs. Specifying requirements for ADSs, a method needs to have terms/notations specific to ADSs such as complex traffic environments (e.g., pedestrians, roads). Use case modeling is commonly practiced in industry for requirements specification and modeling. In this paper, we propose a novel use case modeling methodology, named RUCM4ADS, which specializes the Restricted Use Case Modeling (RUCM). RUCM4ADS aims to specify ADS scenarios by integrating elements from both the autonomous driving domain and Operational World Model (OWM) Ontology. Accompanied with RUCM4ADS, we also develop an editor for it. To evaluate RUCM4ADS, we conducted one real-world case study with 10 use cases. We also conducted a preliminary controlled experiment, in a laboratory setting, to evaluate the applicability of RUCM4ADS. Results show that RUCM4ADS can be used for modeling ADS scenarios and has the potential to improve the overall applicability for specifying ADS scenarios as use case models.
Developing Question Answering System Based on Ontology in Indonesian Traditional Medicine Plants

Indonesia has a variety of local and traditional knowledge in utilizing medicinal plants. The development of the system that can summarize knowledge related to medicinal plants is needed. The system that can summarize medicinal plant knowledge is the Question Answering System. This paper develop the system that named Tanaman Obat Tradisional Indonesia (TOTI). This system uses rule-based method for exploring the answer based on the question and ontology as knowledge of Indonesian traditional medicinal plants. This system was developed in four stages, namely data collection with ontology, analysis and design in three processes (Question Processing, Document Processing, and Answer Processing), implementation, and testing. The result of the Question Answering System based on ontology can provide answers correctly according to the context of the question. The accuracy evaluation of the system based on the test reaches 95,96%. The average response time of the system is 701,67 ms, with the percentage of response time is 84,02 %. This study succeeded in developing a Question Answering System with knowledge-based ontology using the rule-based method.
Relational Models for the Lambek Calculus with Intersection and Unit

We consider the Lambek calculus extended with intersection (meet) operation. For its variant which does not allow empty antecedents, Andréka and Mikulás (1994) prove strong completeness w.r.t. relational models (R-models). Without the antecedent non-emptiness restriction, however, only weak completeness w.r.t. R-models (so-called square ones) holds (Mikulás 2015). Our goals are as follows. First, we extend the calculus with the unit constant, introduce a class of non-standard R-models for it, and prove completeness. This gives a simpler proof of Mikulás’ result. Second, we prove that strong completeness does not hold. Third, we extend our weak completeness proof to the infinitary setting, to so-called iterative divisions (Kleene star under division).
New Tight Relaxations of Rank Minimization for Multi-Task Learning

Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace. It means learning multiple tasks jointly is better than learning them independently. In this paper, we propose two novel multi-task learning formulations based on two regularization terms, which can learn the optimal shared latent subspace by minimizing the exactly k minimal singular values. The proposed regularization terms are the more tight approximations of rank minimization than trace norm. But it's an NP-hard problem to solve the exact rank minimization problem. Therefore, we design a novel re-weighted based iterative strategy to solve our models, which can tactically handle the exact rank minimization problem by setting a large penalizing parameter. Experimental results on benchmark datasets demonstrate that our methods can correctly recover the low-rank structure shared across tasks, and outperform related multi-task learning methods.
Deformable Registration of Brain MR Images via a Hybrid Loss

Unsupervised learning strategy is widely adopted by the deformable registration models due to the lack of ground truth of deformation fields. These models typically depend on the intensity-based similarity loss to obtain the learning convergence. Despite the success, such dependence is insufficient. For the deformable registration of mono-modality image, well-aligned two images not only have indistinguishable intensity differences, but also are close in the statistical distribution and the boundary areas. Considering that well-designed loss functions can facilitate a learning model into a desirable convergence, we learn a deformable registration model for T1-weighted MR images by integrating multiple image characteristics via a hybrid loss. Our method registers the OASIS dataset with high accuracy while preserving deformation smoothness.
Task-Agnostic Out-of-Distribution Detection Using Kernel Density Estimation

In the recent years, researchers proposed a number of successful methods to perform out-of-distribution (OOD) detection in deep neural networks (DNNs). So far the scope of the highly accurate methods has been limited to image level classification tasks. However, attempts for generally applicable methods beyond classification did not attain similar performance. In this paper, we address this limitation by proposing a simple yet effective task-agnostic OOD detection method. We estimate the probability density functions (pdfs) of intermediate features of a pre-trained DNN by performing kernel density estimation (KDE) on the training dataset. As direct application of KDE to feature maps is hindered by their high dimensionality, we use a set of lower-dimensional marginalized KDE models instead of a single high-dimensional one. At test time, we evaluate the pdfs on a test sample and produce a confidence score that indicates the sample is OOD. The use of KDE eliminates the need for making simplifying assumptions about the underlying feature pdfs and makes the proposed method task-agnostic. We perform experiments on classification task using computer vision benchmark datasets. Additionally, we perform experiments on medical image segmentation task using brain MRI datasets. The results demonstrate that the proposed method consistently achieves high OOD detection performance in both classification and segmentation tasks and improves state-of-the-art in almost all cases. Our code is available at https://github.com/eerdil/task_agnostic_ood. Longer version of the paper and supplementary materials can be found as preprint in [].
Semantic Based Greedy Levy Gradient Boosting Algorithm for Phishing Detection

The detection of phishing and legitimate websites is considered a great challenge for web service providers because the users of such websites are indistinguishable.Phishing websites also create traffic in the entire network.Another phishing issue is the broadening malware of the entire network, thus highlighting the demand for their detection while massive datasets (i.e., big data) are processed.Despite the application of boosting mechanisms in phishing detection, these methods are prone to significant errors in their output, specifically due to the combination of all website features in the training state.The upcoming big data system requires MapReduce, a popular parallel programming, to process massive datasets.To address these issues, a probabilistic latent semantic and greedy levy gradient boosting (PLS-GLGB) algorithm for website phishing detection using MapReduce is proposed.A feature selection-based model is provided using a probabilistic intersective latent semantic preprocessing model to minimize errors in website phishing detection.Here, the missing data in each URL are identified and discarded for further processing to ensure data quality.Subsequently, with the preprocessed features (URLs), feature vectors are updated by the greedy levy divergence gradient (model) that selects the optimal features in the URL and accurately detects the websites.Thus, greedy levy efficiently differentiates between phishing websites and legitimate websites.Experiments are conducted using one of the largest public corpora of a website phish tank dataset.Results show that the PLS-GLGB algorithm for website phishing detection outperforms stateof-the-art phishing detection methods.Significant amounts of phishing detection time and errors are also saved during the detection of website phishing.
Artificial intelligence voice assistant implementation possibilities in interactive toy for preschool age children

This paper presents some implementation possibilities of a speech recognition system for the languages Hungarian and Serbian. The system is integrated in an interactive toy for preschool age children that uses voice command. The development of this system is currently at its initial stages, but it shows some potential for application as a speech recognition system for Hungarian and Serbian. The system uses neural networks. The system is especially promising as the toy is aimed at young learners who benefit from interactive learning.
The present and future of mixed reality in China

research-article Share on The present and future of mixed reality in China Authors: Guofeng Zhang Zhejiang University, Hangzhou, China Zhejiang University, Hangzhou, ChinaView Profile , Xiaowei Zhou Zhejiang University, Hangzhou, China Zhejiang University, Hangzhou, ChinaView Profile , Feng Tian Chinese Academy of Sciences, Beijing, China Chinese Academy of Sciences, Beijing, ChinaView Profile , Hongbin Zha Peking University, Beijing, China Peking University, Beijing, ChinaView Profile , Yongtian Wang Beijing Institute of Technology, Beijing, China Beijing Institute of Technology, Beijing, ChinaView Profile , Hujun Bao Zhejiang University, Hangzhou, China Zhejiang University, Hangzhou, ChinaView Profile Authors Info & Claims Communications of the ACMVolume 64Issue 11November 2021 pp 64–69https://doi.org/10.1145/3481619Published:25 October 2021Publication History 0citation4,706DownloadsMetricsTotal Citations0Total Downloads4,706Last 12 Months1,140Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Synchronization of Measurement Data with the Reference System

Abstract Today, most devices are equipped with sensors that enable the collection of process data that influences the control parameters. Sensors are to replace human senses and provide information from the environment to the control algorithm. In many cases, the sensors used are subject to reference measurements from an external diagnostic system, which is to eliminate possible errors in the installation of sensors or the operation of the control algorithm. For this purpose, external systems are most often used that do not duplicate any errors from the sensors used. Most often these are vision systems that are based on the measurement of markers mounted on the tested object. The most important effect of the reference system is the verification of sensor data or the correction of the control algorithm that processes sensor data. Often, when comparing two measurement systems, there is a problem with unequal sampling rates. The main problem is comparing the two graphs as the number of data / arguments is different. In order to compare two graphs, many methods are used to reduce or extend the number of arguments. The use of data expansion or reduction methods may result in an additional error. In this article, methods of reducing measurement data from IMU sensors and encoders connected to servo motors will be presented. There is a significant difference in sampling time for the two measurement systems shown. At the beginning, the methods and applications of algorithms in the literature will be discussed. Then, the measurement data will be compared with the data processed by the algorithms. The final point of work will be the combination of the data processed by the algorithm with the measurements. Two measurements were also compared in order to estimate the measurement error and a comparative algorithm was proposed.
Understanding patient experience in the emergency room using multiple methods

This paper discusses a multi-method approach of gathering data in order to understand how variables within a high stress environment, such as a hospital emergency room, can affect user experience. The challenges facing emergency departments in Alberta can be summarized as a lack of understanding of what happens in an emergency room, inadequate communication between staff and the public, and challenges with the physical design of these departments. Through this study we were able to understand the patient experience and to identify gaps in service, gain insights into issues that are negatively impacting patients and suggests artefacts and processes in order to better deliver patient care.
Multi-scale underwater object tracking by adaptive feature fusion

Different from object tacking on the ground, underwater object tracking is challenging due to the image attenuation and distortion. Also, challenges are increased by the high-freedom motion of targets under water. Target rotation, scale change, and occlusion significantly degenerate the performance of various tracking methods. Aiming to solve above problems, this paper proposes a multi-scale underwater object tracking method by adaptive feature fusion. The gray, HOG (Histogram of Oriented Gradient) and CN (Color Names) features are adaptively fused in the background-aware correlation filter (BACF) model. Moreover, a novel scale estimation method and a high-confidence model update strategy are proposed to comprehensively solve the problems caused by the scale changes and background noise influences. Experimental results demonstrate that the success ratio of the AUC criterion is 64.1% that is better than classic BACF and other methods, especially in challenging conditions.