Reviewerly/exHarmony huggingface dataset:
Description for Smart Grid: Towards the Ontological Approach

Smart grids facilitate the use of distributed and renewable resources on the supply side and providing consumers with a range of tailored services on the consumption side. The introduction of energy smart grid in Malaysia allows other stakeholders to reap the potential benefits it offers. However, for the stakeholders to share knowledge and collaborate, a common language that can act as intermediary that allows any application to communicate with each other is needed. Ontologies have been successful in integrating the knowledge required for solving complex problems such as energy management problems. Ontology has become a promising future direction towards devising a context-aware middleware platform for the smart grid. Hence, we propose to develop ontology for energy smart grid to support information integration for existing smart grid applications and to produce an efficient, semantically aware and operationally accurate software environment for managing flexibility in Malaysian energy distribution networks.
Modeling Survival in model-based Reinforcement Learning

Although recent model-free reinforcement learning algorithms have been shown to be capable of mastering complicated decision-making tasks, the sample complexity of these methods has remained a hurdle to utilizing them in many real-world applications. In this regard, model-based reinforcement learning proposes some remedies. Yet, inherently, model-based methods are more computationally expensive and susceptible to sub-optimality. One reason is that model-generated data are always less accurate than real data, and this often leads to inaccurate transition and reward function models. With the aim to mitigate this problem, this work presents the notion of survival by discussing cases in which the agent's goal is to survive and its analogy to maximizing the expected rewards. To that end, a substitute model for the reward function approximator is introduced that learns to avoid terminal states rather than to maximize accumulated rewards from safe states. Focusing on terminal states, as a small fraction of state-space, reduces the training effort drastically. Next, a model-based reinforcement learning method is proposed (Survive) to train an agent to avoid dangerous states through a safety map model built upon temporal credit assignment in the vicinity of terminal states. Finally, the performance of the presented algorithm is investigated, along with a comparison between the proposed and current methods.
Improving the Security of Wireless Network Through Cross-project Security Issue Prediction

To guarantee the security of the wireless network, effort must be paid to software security as software plays a more and more important role in the development of wireless network intelligentization. Identifying security bug reports (SBRs) from bug repository matters much for reducing security risk of wireless network. Cross-project SBR prediction, which uses a prediction model trained with labeled data from one project to predict another project, has been proposed to eliminate SBRs of software products. While reviewing the previous work focused on cross-project SBR prediction, we find the performance (e.g., Recall, F1-score) of cross-project SBR prediction is too low to the production application. This paper proposes a hybrid sampling approach based on text similarity and uncertainty-sampling. We conduct experiments on ten publicly available datasets. The results show our approach could significantly improve the performance of cross-project SBR prediction. On average, the performance of the classification model can be improved by 34%, 64%, 48%, and 11% in terms of Recall, Precision, F1-score, and AUC, respectively.
Understanding the Behavior of Reinforcement Learning Agents

Reinforcement Learning (RL) is the process of training agents to solve specific tasks, based on measures of reward. Understanding the behavior of an agent in its environment can be crucial. For instance, if users understand why specific agents fail at a task, they might be able to define better reward functions, to steer the agents’ development in the right direction. Understandability also empowers decisions for agent deployment. If we know why the controller of an autonomous car fails or excels in specific traffic situations, we can make better decisions on whether/when to use them in practice. We aim to facilitate the understandability of RL. To that end, we investigate and observe the behavioral space: the set of actions of an agent observed for a set of input states. Consecutively, we develop measures of distance or similarity in that space and analyze how agents compare in their behavior. Moreover, we investigate which states and actions are critical for a task, and determine the correlation between reward and behavior. We utilize two basic RL environments to investigate our measures. The results showcase the high potential of inspecting an agents’ behavior and comparing their distance in behavior space.
A Study of BlockChain Technologies and health Care Systems

The demand and encouragement to use digital platforms in various technologies like big data and Machine Learning has proved to be efficient in various applicable areas and industries. Blockchain (BC) technology has been started by S antoshi Nakamoto in the year 2008; this was developed in a sole purpose to serve crypto currency bit-coin. The BC cannot be compared to any existing system or platform. BC can be used in a place where both end users, which creates trust between them. BC has gained its popularity in the area of decentralized approaches to create management value. Many Governmental sectors and nongovernmental sector has introduced the concept of BC because of it advantages provided by the technology, it provides some of the important services like security, scalability and improvement in efficiency. The major advantage of BC is tracking of error because of block nature of the application. The BC has slowly induced into various sectors, thus by providing the ease of work in day to day life. This technology is very useful in a country like India, because people here urge for new techniques, and there is a possibility that many industry adopt this technique to reach their heights in their domains. In this survey paper we discuss in detail the impact of BC in various applicable areas.
Dynamical Lamb effect in a superconducting circuit

The dynamical Lamb effect is predicted to arise in superconducting circuits when the coupling of a superconducting qubit with a resonator is periodically switched "on" and "off" nonadiabatically. We show that by using a superconducting circuit which allows to switch between longitudinal and transverse coupling of a qubit to a resonator, it is possible of to observe the dynamical Lamb effect. {The switching between longitudinal and transverse coupling can be achieved by modulating the magnetic flux through the circuit loops.} By solving the Schr\"{o}dinger equation for a qubit coupled to a resonator, we calculate the time evolution of the probability of excitation of the qubit and the creation of $n$ photons in the resonator due to the dynamical Lamb effect. The probability is maximum when the coupling is periodically switched between longitudinal and transverse using a square-wave or sinusoidal modulation of the magnetic flux with frequency equal to the sum of the average qubit and photon transition frequencies.
Research on financial fraud identification of listed companies based on text data mining

Identifying the financial fraud behavior of listed companies in a timely manner and helping investors avoid risks is a key measure to promote the healthy development of the capital market. Aiming at the lag of traditional fraud identification method, a text classification model suitable for Chinese capital market is constructed based on user generated content (UGC) on social media platform. To detect fraudulent companies,TF-IDF features, topic features and explosive news quantity features are extracted guided by systemic functional linguistics (SFL) theory. Empirical analysis is conducted by crawling the news and comments of 124 companies from Eastmoney and JRJ.com.The empirical analysis results show that the model constructed by the article can effectively extract the implicit information in unstructured data and improve the timeliness of screening fraudulent behaviors of listed companies. The topic and keyword features in text comments play an important role in distinguishing the fraud of listed companies. On the one hand, it helps individual and institutional investors avoid investment traps, on the other hand, it helps regulators detect companies with fraudulent potential in time to prevent market risks.
Research on a Distribution-Outlier Detection Algorithm Based on Logistics Distribution Data

Abstract In this paper, we propose an outlier detection algorithm, which improves the existing algorithm from three aspects: the time outlier, the outlier detection of logistics distribution path, the time complexity. Aiming at the time outlier, this paper proposes an abnormal objective function based on the average speed of the logistics distribution process and realizes the detection of the outlier time of logistics distribution. In the aspect of the outlier detection of logistics distribution path, this paper combines the distance- and the angle-based outlier detection algorithms to realize the detection of the local abnormal situation of the logistics distribution trajectory. At the same time, the high time complexity of the algorithm is reduced by only retrieving the path sub-sections in the neighborhood of the proper radius. Experimental results indicate that the efficiency and speed of the proposed algorithm are better than those of the trajectory outlier detection (TRAOD) algorithm, and the proposed algorithm is suitable for outlier detection of logistics distribution.
Towards on Blockchain Data Privacy Protection with Cryptography and Software Architecture Approach

The essence of blockchain is to solve trust problems and realize value transfer. The traditional centralized processing method adopts centralized transmission and storage of users’ data privacy, which improves the security and reliability of processing. The non central blockchain technology uses distributed ledger technology to realize the characteristics of disintermediation, data tampering, traceability, work audit, etc. encryption algorithm is used to encrypt the data, and consensus mechanism makes the data sharing of blockchain system more fair and stable. The current passive data privacy schemes are basically based on cryptography. On the premise of satisfying the constraint mechanism of blockchain, this paper studies the technical framework, encryption mechanism and empirical analysis of blockchain, and discusses the information privacy protection method of hiding the original big data as much as possible, so as to improve the system performance and protect the security and privacy of user data. At the same time, we study data privacy protection from the perspective of software architecture, and propose a data privacy protection scheme through algorithm decomposition multi center collaboration method, which provides guidance for big data sharing and transaction based on blockchain.
Towards AI-based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones

To cope with the vastly growing number of Multimedia Assets on Smartphones and Social Media, an integrated approach for Semantic Indexing and Retrieval is required. This paper introduces an approach towards a generic framework to fuse existing image and video analysis tools and algorithms into a Unified Semantic Annotation, Indexing and Retrieval Model resulting in a Multimedia Feature Vector Graph representing various levels of Media Content, Media Structures and Media Features. Utilizing Artificial Intelligence and Machine Learning, these Feature Representations can be used to provide accurate Semantic Indexing and Retrieval. This paper provides an overview of the Generic Multimedia Analysis Framework (GMAF) as well as the definition of a Multimedia Feature Vector Graph Framework (MMFVGF). As a third contribution we introduce AI4MMACCESS to detect differences, enhance Semantics and refine weights in the Feature Vector Graph. Combining this, we describe a solution for highly flexible Semantic Indexing and Retrieval that offers unseen possibilities for applications like Social Media or local Apps on Smartphones.
Mīmāṃsā deontic reasoning using specificity: a proof theoretic approach

Abstract Over the course of more than two millennia the philosophical school of Mīmāṃsā has thoroughly analyzed normative statements. In this paper we approach a formalization of the deontic system which is applied but never explicitly discussed in Mīmāṃsā to resolve conflicts between deontic statements by giving preference to the more specific ones. We first extend with prohibitions and recommendations the non-normal deontic logic extracted in Ciabattoni et al. (in: TABLEAUX 2015, volume 9323 of LNCS, Springer, 2015) from Mīmāṃsā texts, obtaining a multimodal dyadic version of the deontic logic $$\mathsf {MD}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>MD</mml:mi></mml:math> . Sequent calculus is then used to close a set of prima-facie injunctions under a restricted form of monotonicity, using specificity to avoid conflicts. We establish decidability and complexity results, and investigate the potential use of the resulting system for Mīmāṃsā philosophy and, more generally, for the formal interpretation of normative statements.
The Influence of LWE/RLWE Parameters on the Stochastic Dependence of Decryption Failures

Learning with Errors (LWE) and Ring-LWE (RLWE) problems allow the construction of efficient key exchange and public-key encryption schemes. However, while improving the security through the use of error distributions with large standard deviations, the decryption failure rate increases as well. Currently, the independence of individual coefficient failures is assumed to estimate the overall decryption failure rate of many LWE/RLWE schemes. However, previous work has shown that this assumption is not correct. This assumption leads to wrong estimates of the decryption failure probability and consequently of the security level of the LWE/RLWE cryptosystem. An exploration of the influence of the LWE/RLWE parameters on the stochastic dependence among the coefficients is still missing. In this paper, we propose a method to analyze the stochastic dependence between decryption failures in LWE/RLWE cryptosystems. We present two main contributions. First, we use statistical methods to analyze the influence of fixing the norm of the error distribution on the stochastic dependence among decryption failures. The results have shown that fixing the norm of the error distribution indeed reduces the stochastic dependence of decryption failures. Therefore, the independence assumption gives a very close approximation to the true behavior of the cryptosystem. Second, we analyze and explore the influence of the LWE/RLWE parameters on the stochastic dependence. This exploration gives designers of LWE/RLWE based schemes the opportunity to compare different schemes with respect to the inaccuracy made by using the independence assumption. This work shows that the stochastic dependence depends on three LWE/RLWE parameters in different ways: i) it increases with higher lattice dimensions (n) and higher standard deviations of the error distribution ( $$\sqrt{k/2}$$ ); and ii) it decreases with higher modulus (q).
Novel Watermarking Scheme Using Boundary Pixels Least Significant Qubit Steganography

Image watermarking has become a popular area of study in quantum information processing. In this paper, we propose a new watermarking method using boundary pixels of a quantum image. The quantum image is firstly scrambled into a meaningless image and expanded to an appropriate size. Then the expanded and scrambled watermark is embedded into the horizontal direction of boundary pixels of the quantum cover images. The watermark can be extracted using some inverse quantum operations. Supported by the simulation and analysis, the proposal has outperformed some similar quantum schemes in terms of the robustness and PSNR value.
Learning to Adapt to Unseen Abnormal Activities Under Weak Supervision

We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available. Our work is motivated by the fact that existing methods suffer from poor generalization to diverse unseen examples. We claim that an anomaly detector equipped with a meta-learning scheme alleviates the limitation by leading the model to an initialization point for better optimization. We evaluate the performance of our framework on two challenging datasets, UCF-Crime and ShanghaiTech. The experimental results demonstrate that our algorithm boosts the capability to localize unseen abnormal events in a weakly supervised setting. Besides the technical contributions, we perform the annotation of missing labels in the UCF-Crime dataset and make our task evaluated effectively.
An Ontology Based Matchmaking Technique for Cloud Service Discovery and Selection Using Aneka PaaS

As we are moving ahead with cloud computing, preference to cloud services is getting increased day by day. These services mostly seem to be significantly identical in their functionality except their key attributes like storage, computational power, price etc. As of now there is no uniform specification for defining a service in cloud domain. In order for specification of the identical operations and publication of the services on the websites, different cloud service providers tend to use completely different vocabulary. The process of requesting for a cloud service becomes merely challenging task as a result of increasing number of selection parameters and QoS constraints. Hence, a reasoning mechanism is very much required for service discovery that could resolve the resemblance appearing across different services by inferring with the respective cloud ontology. In this paper, an ontology based matchmaking technique has been proposed for cloud service discovery and selection using a distributed cloud platform called Aneka Platform as a Service (PaaS).
Faster Self-adaptive Deep Stereo

Fueled by the power of deep learning, stereo vision has made unprecedented advances in recent years. Existing deep stereo models, however, can be hardly deployed to real-world scenarios where the data comes on-the-fly without any ground-truth information, and the data distribution continuously changes over time. Recently, Tonioni et al. proposed the first real-time self-adaptive deep stereo system (MADNet) to address this problem, which, however, still runs at a relatively low speed with not so satisfactory performance. In this paper, we significantly upgrade their work in both speed and accuracy by incorporating two key components. First, instead of adopting only the image reconstruction loss as the proxy supervision, a second more powerful supervision is proposed, termed Knowledge Reverse Distillation (KRD), to guide the learning of deep stereo models. Second, we introduce a straightforward yet surprisingly effective Adapt-or-Hold (AoH) mechanism to automatically determine whether or not to fine-tune the stereo model in the online environment. Both components are lightweight and can be integrated into MADNet with only a few lines of code. Experiments demonstrate that the two proposed components improve the system by a large margin in both speed and accuracy. Our final system is twice as fast as MADNet, meanwhile attains considerable superior performance on the popular benchmark datasets KITTI.
Provenance-based Hierarchical Encryption for Fine-grained Access Control in Cloud Computing

In the big data era, files stored at cloud are usually supplemented, such as health record managing systems. Access policies on different sections of a file have similarities as well as differences. Hence, an efficient approach to constructing hierarchical access structures for different sections of a file not only achieves flexible fine-grained access control but also provides well-preserved confidentiality of data based on the preferences of each data producers. In this paper, we introduce an idea on the provenance-based access control policy along with its applicable cloud system. Then we propose the provenance-based hierarchical encryption to implement it.
Building A Multi-class XGBoost Model for Arabic Figurative Language

In the Natural Language Proc essing (NLP) field, the text classification becomes a task on which many scholars and researchers concentrate. Rhetorical methods in the Arabic language are among the means of linguistic expression that express opinions and feelings through written or spoken texts. It is essential to pay attention to this specialized research point in the Arabic language and in particular in the so-called Arabic rhetoric sciences that are concerned with figurative devices (i.e. simile, hyperbole and sarcasm). In this paper, we build the eXtreme Gradient Boosting (XGBoost) classifier to classify the multi-class Arabic figurative texts. The XGBoost is quite efficient for its speed and performance. The XGBoost classifier was developed, trained, and tested on this Arabic Figurative Corpus (AFC). The performance of the XGBoost classifier obtained as Fl-score is 88%.
Load Balancing and Resource Utilization Approach in Cloud Computing Using Honey Bee-Inspired Algorithm

Cloud security strategies have been utilized for privacy, protection, and reliability in cloud environments. In the existing honey bee-inspired algorithm, the load balancing is performed with the help of a random selection of virtual machines. This paper proposes an efficient strategy for the load balancing of the usage of honey bee-inspired algorithm on the basis of excessive throughputs. Therefore, we can reduce the overall response time of the proposed algorithm by shifting load to the most environment-friendly virtual machine. Improved load balancing strategy is implemented in the CloudSim simulator. CloudSim simulator is designed by means of using Java technology. Our proposed approach reduces load impact of about 10% to 15%. Obtained results show that the proposed approach minimizes makespan as well as the response time of virtual machines.
Easy-But-Effective Domain Sub-Similarity Learning for Transfer Regression

Transfer covariance function, which can model domain similarity and adaptively control the knowledge transfer across domains, is widely used in transfer learning.In this paper, we concentrate on Gaussian process (GP) models using a transfer covariance function for regression problems in a black-box learning scenario.Precisely, we investigate a family of rather general transfer covariance functions, T * , that can model the heterogeneous sub-similarities of domains through multiple kernel learning.A necessary and sufficient condition to obtain valid GPs using T * (GP T * ) for any data is given.This condition becomes specially handy for practical applications as (i) it enables semantic interpretations of the sub-similarities and (ii) it can readily be used for model learning.In particular, we propose a computationally inexpensive model learning rule that can explicitly capture different sub-similarities of domains.We propose two instantiations of GP T * , one with a set of predefined constant base kernels and one with a set of learnable parametric base kernels.Extensive experiments on 36 synthetic transfer tasks and 12 real-world transfer tasks demonstrate the effectiveness of GP T * on the sub-similarity capture and the transfer performance.