Reviewerly/exHarmony huggingface dataset:
Learning classifier systems

Biological nervous systems can learn knowledge from simple and small-scale problems and then apply it to resolve more complex and large-scale problems in similar and related domains. However, the rudimentary attempts to apply this transfer learning in artificial intelligence systems have struggled. This is maybe due to the homogeneous nature of their knowledge representation. It is believed that it is the lateral asymmetry of the brain, enabling modular learning at different levels of abstraction, which facilitates transfer between tasks. Learning classifier systems (LCSs) are a rule-based evolutionary computation technique that automatically clusters inputs into environmental niches, which makes it an ideal candidate for implementing lateralization.
Mining Multivariate Discrete Event Sequences for Knowledge Discovery and Anomaly Detection

Modern physical systems deploy large numbers of sensors to record at different time-stamps the status of different systems components via measurements such as temperature, pressure, speed, but also the component's categorical state. Depending on the measurement values, there are two kinds of sequences: continuous and discrete. For continuous sequences, there is a host of state-of-the-art algorithms for anomaly detection based on time-series analysis, but there is a lack of effective methodologies that are tailored specifically to discrete event sequences. This paper proposes an analytics framework for discrete event sequences for knowledge discovery and anomaly detection. During the training phase, the framework extracts pairwise relationships among discrete event sequences using a neural machine translation model by viewing each discrete event sequence as a "natural language". The relationship between sequences is quantified by how well one discrete event sequence is "translated" into another sequence. These pairwise relationships among sequences are aggregated into a multivariate relationship graph that clusters the structural knowledge of the underlying system and essentially discovers the hidden relationships among discrete sequences. This graph quantifies system behavior during normal operation. During testing, if one or more pairwise relationships are violated, an anomaly is detected. The proposed framework is evaluated on two real-world datasets: a proprietary dataset collected from a physical plant where it is shown to be effective in extracting sensor pairwise relationships for knowledge discovery and anomaly detection, and a public hard disk drive dataset where its ability to effectively predict upcoming disk failures is illustrated.
Autonomous Economic Agents as a Second Layer Technology for Blockchains: Framework Introduction and Use-Case Demonstration

The user experience of interacting with distributed ledger technologies (DLT) is fraught with excessive complexity, high risk and unintuitive processes. Moreover, smart contracts deployed in these systems are restricted to being reactive. These limitations have negative implications on user adoption and prevent DLTs from being general purpose. We introduce a framework for the development of Autonomous Economic Agents (AEAs), software agents that act autonomously and pursue an economic goal, and demonstrate how AEAs complement existing decentralised ledgers as a second layer technology. In particular, the framework enables a simplified user experience through automation, supports modularisation and reuse of complex decision making and machine learning capabilities, and allows for proactive behaviour facilitating autonomy. We demonstrate these gains in the context of a specific use-case, a multi-agent trading system modelling a Walrasian Exchange Economy populated by a number of agents trading a basket of tokens.
Quality control method of the transmission of fine image details in the JPEG2000

The article proposes a method for controlling the transmission quality of fine image details in the JPEG2000 standard based on an automatic adjustment of the quantization parameters of discrete wavelet transform (DWT) coefficients. An algorithm for setting up quantization scale parameters for different transformation sub-ranges of the DWT coefficients depending on given (permissible) distortions is described. For an objective assessment of image quality, numerical measures of fine detail distortion in a normalized N-CIELAB colorimetric system are used, according to which an analysis of their structural features is performed. Results of the experimental studies of the analysis of image quality and compression efficiency depending on the quantization parameters utilized in the developed adaptive compression algorithm are presented. Results of evaluating the algorithm performance that can be used for practical applications in multimedia applications are also presented.
Software Agent-Centric Semantic Social Network for Cyber-Physical Interaction and Collaboration

Considerable research has recently focused on integrating cyber-physical systems in a social context. However, several challenges remain concerning appropriate methodologies, frameworks and techniques for supporting socio-cyber-physical collaboration. Existing systems do not recognize how cyber-physical resources can be socially connected so that they interact in collaborative decision-making like humans. Furthermore, the lack of semantic representations for heterogeneous cyber-social-collaborative networks limits integration, interoperability and knowledge discovery from their underlying data sources. Semantic Web ontology models can help to overcome this limitation by semantically describing and interconnecting cyber-physical objects and human participants in a social space. This research addresses the establishment of both cyber-physical and human relationships and their interactions within a social-collaborative network. We discuss how nonhuman resources can be represented as socially connected nodes and utilized by software agents. A software agent-centric Semantic Social-Collaborative Network (SSCN) is then presented that provides functionality to represent and manage cyber-physical resources in a social network. It is supported by an extended ontology model for semantically describing human and nonhuman resources and their social interactions. A software agent has been implemented to perform some actions on behalf of the nonhuman resources to achieve cyber-physical collaboration. It is demonstrated within a real-world decision support system, GRiST (www.egrist.org), used by mental-health services in the UK.
Integrating DOTS With Blockchain Can Secure Massive IoT Sensors

This paper presents a novel approach to securing IoT devices by leveraging DDoS Open Threat Signaling (DOTS) architecture on a Blockchain framework. Like many areas of the information technology domain, IoT sensors are also prone to attacks but on a larger scale. There are millions of devices being connected to a central domain to provide different types of services. Since these low-powered IoT devices have constrained technical requirements with less computational capabilities, they lack the capacity to judge their behavior as benign or malignant. IoT relies heavily on the higher level of intelligent nodes to decide on their status. An IoT Controller/Edge server handles the registration and the limited management of devices. Since traditional security is unable to protect the IoT environment sufficiently, we present a Blockchain-based DDoS detection approach to secure and mitigate such attacks in the IoT environment. Our test setup includes dataset from four sensors over two months. These values were tested using a threshold calculation against the variation of temperature, humidity, pressure, and wind direction on that day to find out whether an IoT sensor is under a DDoS attack. Our results show how DOTS can help in detection of attack when mapped on IoT edge computing.
CVSS-based Multi-Factor Dynamic Risk Assessment Model for Network System

The risk assessment model of network systems is designed to provide quantifiable evidence to assist security administrators in choosing appropriate defend methods. Most models measure the overall risk by combining CVSS base scores of system vulnerabilities. However, they merely consider the impact of dynamic risk factors including attacker capability and evolutions of vulnerabilities. To address this issue, we propose a CVSS based Multi-Factor dynamic risk assessment Model, CMFM. It uses attack paths to model an attacker’s capability, which is thus used to estimate the successful probabilities about vulnerability exploitations. Besides, we exploit both static and time-variant factors of vulnerabilities to produce a better estimation result. The final system risk assessment can then be accessed via a Bayesian attack graph. We evaluate the proposed model in two scenarios, all of which demonstrate that CMFM outperforms the state-of-the-art models in assessing the dynamic risk status of network systems.
JS-son - A Lean, Extensible JavaScript Agent Programming Library

A multitude of agent-oriented software engineering frameworks exist, most of which are developed by the academic multi-agent systems community. However, these frameworks often impose programming paradigms on their users that are challenging to learn for engineers who are used to modern high-level programming languages such as JavaScript and Python. To show how the adoption of agent-oriented programming by the software engineering mainstream can be facilitated, we provide a lean JavaScript library prototype for implementing reasoning-loop agents. The library focuses on core agent programming concepts and refrains from imposing further restrictions on the programming approach. To illustrate its usefulness, we show how the library can be applied to multi-agent systems simulations on the web, deployed to cloud-hosted function-as-a-service environments, and embedded in Python-based data science tools.
A genetic algorithm to optimize SMOTE and GAN ratios in class imbalanced datasets

Class imbalance is one of the problem easily encountered in the fields of data analysis and machine learning. When there is an imbalance in learning dataset, machine learning models become biased and learn inaccurate classifiers. To resolve such data imbalance problems, a strategy that increases the volume of data of minority classes is often used by applying the synthetic minority oversampling technique (SMOTE). Furthermore, the use of generative adversarial networks (GANs) for data oversampling has recently become more common. This research used a genetic algorithm to search and optimize the combinations of oversampling ratios based on the SMOTE and GAN techniques. The case in which the proposed method was used was compared with the cases in which a single technique was used to train either the imbalanced data or oversampled data. From the results, it was established that the classifier that learned the oversampled data with the optimized ratio using the proposed method was superior in classification performance.
Semantic Service Search in IT Crowdsourcing Platform: A Knowledge Graph-Based Approach

Understanding user’s search intent in vertical websites like IT service crowdsourcing platform relies heavily on domain knowledge. Meanwhile, searching for services accurately on crowdsourcing platforms is still difficult, because these platforms do not contain enough information to support high-performance search. To solve these problems, we build and leverage a knowledge graph named ITServiceKG to enhance search performance of crowdsourcing IT services. The main ideas are to (1) build an IT service knowledge graph from Wikipedia, Baidupedia, CN-DBpedia, StuQ and data in IT service crowdsourcing platforms, (2) use properties and relations of entities in the knowledge graph to expand user query and service information, and (3) apply a listwise approach with relevance features and topic features to re-rank the search results. The results of our experiments indicate that our approach outperforms the traditional search approaches.
Improved Cuckoo Search with Artificial Bee Colony for Efficient Load Balancing in Cloud Computing Environment

Task scheduling and load balancing are major issues in cloud environments. Utilization of resource is affected directly by this. In research field of cloud, aspect of load balancing has to be considered. On front as well as back end, it has a serious impact. For balancing load, improved cuckoo search + artificial bee colony (ICSABC) algorithm is proposed in this work. It is bio-inspired load balancing scheduling algorithm. On VMs, load balance level is improved by enabling process of scheduling efficiently. In cloud computing, most important work is task scheduling. Users are paying based on resource usage time. Among resources of system, load must be distributed evenly. It increases the utilization of resource and execution time is reduced. Best task-to-virtual machine mapping is computed by proposed algorithm. Speed of VM processing and submitted workload's length has an effect on it. Best average VM load is achieved by proposed ICSABC as shown by results of experimentation. It also results in high accuracy and low complexity time, when compared with existing methods.
Transfer Learning of Genetic Programming Instruction Sets

The performance of a genetic programming system depends partially on the composition of the collection of elements out of which programs can be constructed, and by the relative probability of different instructions and constants being chosen for inclusion in randomly generated programs or for introduction by mutation. In this paper we develop a method for the transfer learning of instruction sets across different software synthesis problems. These instruction sets outperform unlearned instruction sets on a range of problems.
Illuminating super mario bros

We aim to build on the current understanding of how Quality-Diversity (QD) algorithms can be applied to Procedural Content Generation (PCG) for video games with our platform, Illuminated-Mario. The work presented here has two core aims: to explore the viability of our approach to PCG and to compare the performances of MAP-Elites and SHINE, two state of the art QD algorithms. Our experimental results suggest both that our approach to level generation is a viable foundation for future PCG systems, and that while the SHINE variants implemented do not significantly outperform MAP-Elites, they warrant further investigation.
Adversarial Learning for Multi-task Sequence Labeling with Attention Mechanism

With the requirements of natural language applications, multi-task sequence labeling methods have some immediate benefits over the single-task sequence labeling methods. Recently, many state-of-the-art multi-task sequence labeling methods were proposed, while still many issues to be resolved including (C1) exploring a more general relationship between tasks, (C2) extracting the task-shared knowledge purely and (C3) merging the task-shared knowledge for each task appropriately. To address the above challenges, we propose MTAA, a symmetric multi-task sequence labeling model, which performs an arbitrary number of tasks simultaneously. Furthermore, MTAA extracts the shared knowledge among tasks by adversarial learning and integrates the proposed multi-representation fusion attention mechanism for merging feature representations. We evaluate MTAA on two widely used data sets: CoNLL2003 and OntoNotes5.0. Experimental results show that our proposed model outperforms the latest methods on the named entity recognition and the syntactic chunking task by a large margin, and achieves state-of-the-art results on the part-of-speech tagging task.
Dark web Activity on Tor—Investigation Challenges and Retrieval of Memory Artifacts

Internet is a collection of various technologies which is becoming a platform for both positive and negative activities. Deep web is the section of the Internet which requires credentials to access the data, whereas in surface web, data is publicly available. Since data of deep web is not available for public analysis, there is technology available for doing illegal activity using deep web. Due to anonymity feature, dark web is becoming a challenge to investigator. Dark web is not accessible through standard browsers and is not indexed by search engines, which makes it anonymous. Web site over dark web may not cooperate with law enforcement agencies during investigation, thereby creating a challenge to investigator or forensics expert. Therefore, if we can retrieve some evidences from end devices through forensics analysis, it may give some evidence for investigator to proceed in the case.
Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System

Extreme Learning Machine improved the iterative procedures of adjusting weights by randomly selecting hidden neurons besides analytically determining the output weights. In this paper, the basic ELM neural network was enhanced with a simplified network structure to achieve regression performance. Next, to solve the pattern classification, a hybrid system was proposed which integrated the ELM neural network and MAS models. A MAS model is then designed with a novel trust measurement method to combine ELM neural networks. Firstly, ELM hybrid with Single Input Rule Module (SIRM-ELM) was designed. There was only a single input connected to the rules, where the rules were the hidden neurons of ELM and each represented a single input fuzzy rules. Results showed that the SIRM-ELM model was better than Support Vector Machine and traditional ELM. Secondly, an extreme learning machine based multi agent systems (ELM-MAS) was designed to improve ELM’s capability. Its first layer was made up of at least one ELM where ELM acted as an individual agent, whereas another layer was made up of a single ELM acting as the parent agent. Lastly, Certified Belief in Strength (CBS) method was applied to the ELM neural network to form ELM-MAS-CBS, using the reputation and strength of individual agents as the trust measurement. The assembly of strong elements related to the ELM agents formed the trust management that allowed the improvement of the performance in MAS using the CBS method. Both of the developed models were evaluated on its application on the power generation system. The test accuracy rate of both models for circulating water systems was shown to be comparable to other algorithms. In short, the developed models had been verified using benchmark datasets and applied in power generation, where the results were satisfactory.
Ephemeral Exit Bridges for Tor

This paper examines an existential threat to Tor---the increasing frequency at which websites apply discriminatory behavior to users who arrive via the anonymity network. Our main contribution is the introduction of Tor exit bridges. Exit bridges, constructed as short-lived virtual machines on cloud service providers, serve as alternative egress points for Tor and are designed to bypass server-side censorship. Due to the proliferation of managed cloud-based desktop services (e.g., Amazon Workspaces), there is already a surprisingly large fraction of web requests that originate in the cloud. Trivially disrupting exit bridges by blocking requests from the cloud would thus lead to significant collateral damage. Our experiments demonstrate that exit bridges effectively circumvent server-side blocking of Tor with low overhead. Additionally, we perform a cost-analysis of exit bridges and show that even a large-scale deployment can be done at low cost.
Round-Optimal Black-Box Commit-and-Prove with Succinct Communication

We give a four-round black-box construction of a commit-and-prove protocol with succinct communication. Our construction is WI and has constant soundness error, and it can be upgraded into a one that is ZK and has negligible soundness error by relying on a round-preserving transformation of Khurana et al. (TCC 2018). Our construction is obtained by combining the MPC-in-the-head technique of Ishai et al. (SICOMP 2009) with the two-round succinct argument of Kalai et al. (STOC 2014), and the main technical novelty lies in the analysis of the soundness—we show that, although the succinct argument of Kalai et al. does not necessarily provide soundness for $$\mathcal {NP}$$ statements, it can be used in the MPC-in-the-head technique for proving the consistency of committed MPC views. Our construction is based on sub-exponentially hard collision-resistant hash functions, two-round PIRs, and two-round OTs.
Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

It is difficult to obtain many labeled Link Establishment (LE) behavior signals sent by non-cooperative short-wave radio stations. We propose a novel unidimensional Auxiliary Classifier Generative Adversarial Network (ACGAN) to get more signals and then use unidimensional DenseNet to recognize LE behaviors. Firstly, a few real samples were randomly selected from many real signals as the training set of unidimensional ACGAN. Then, the new training set was formed by combining real samples with fake samples generated by the trained ACGAN. In addition, the unidimensional convolutional auto-coder was proposed to describe the reliability of these generated samples. Finally, different LE behaviors could be recognized without the communication protocol standard by using the new training set to train unidimensional DenseNet. Experimental results revealed that unidimensional ACGAN effectively augmented the training set, thus improving the performance of recognition algorithm. When the number of original training samples was 400, 700, 1000, or 1300, the recognition accuracy of unidimensional ACGAN+DenseNet was 1.92, 6.16, 4.63, and 3.06% higher, respectively, than that of unidimensional DenseNet.
ETCNet

As more and more Internet traffic is encrypted, classifying their flows for the usage in application-aware networking (AAN) and application-network integration (ANI) becomes increasingly important and challenging. Traditional deep packet inspection approaches are no longer capable of identifying the encrypted packet streams, and hence new traffic classification methods based on machine learning have recently been explored by several researchers. One major challenge of using machine learning to classify encrypted traffic is lacking real datasets. Collecting Internet traffic may leak users' sensitive information, which prohibits the network community from sharing the datasets they collected. In this poster, we propose ETCNet which is a model based on Siamese convolutional network to solve this issue. Our evaluation for the ETCNet shows that it can achieve high accuracy by only using 40 flows of each application to train it.
Smart mobile application for public transport schedules – data organization and program realization

Data organization and program realization of portal for smart mobile application for public transport schedules has been presented in this article. Realization is of PHP and DBSM (data base system management) - MySQL's.