Reviewerly/exHarmony huggingface dataset:
RuleKit: A comprehensive suite for rule-based learning

Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induction facilitates verifying hypotheses concerning data dependencies which are expected or of interest. The powerful and flexible experimental environment allows straightforward investigation of different induction schemes. The analysis can be performed in batch mode, through RapidMiner plug-in, or R package. A documented Java API is also provided for convenience. The software is publicly available at GitHub under GNU AGPL-3.0 license.
Efficient Private Comparison Queries Over Encrypted Databases Using Fully Homomorphic Encryption With Finite Fields

To achieve security and privacy for data stored on the cloud, we need the ability to secure data in compute. Equality comparisons, “ <inline-formula><tex-math notation="LaTeX">$x=y, x\ne y$</tex-math></inline-formula> ”, have been widely studied with many proposals but there is much room for improvement for order comparisons, “ <inline-formula><tex-math notation="LaTeX">$x &lt; y,~x \leq y,~x &gt; y \text{ and } x \geq y$</tex-math></inline-formula> ”. Most protocols for order comparisons have some limitation, either leaking some information about the data or requiring several rounds of communication between client and server. In addition, little work has been done on retrieving with compound conditions, mixing several equality and order comparisons. Fully homomorphic encryption (FHE) promises the ability to compute arbitrary functions on encrypted data without sacrificing privacy and without communication, but its potential has yet to be fulfilled. Particularly, private comparisons for database queries using FHE are expensive to compute. In this article, we design an efficient private database query (PDQ) protocol which supports compound conditions with equality and order comparisons. To this end, we first present a private comparison algorithm on encrypted integers using FHE, which scales efficiently for the length of input integers, by applying techniques from finite field theory. Then, we consider a scenario for PDQ protocols, querying for values based on a conjunction of one order and four equality conditions on key columns. The proposed algorithm and protocol are implemented and tested to determine their performance in practice. The proposed comparison algorithm takes about <inline-formula><tex-math notation="LaTeX">$25.259$</tex-math></inline-formula> seconds to compare 697 pairs of 64-bit integers using Brakerski-Gentry-Vaikuntanathan's leveled FHE scheme with single instruction multiple data (SIMD) techniques at more than 138 bits of security. This yields an amortized rate of just 36 milliseconds per comparison. On top of that, we show that our techniques achieve an efficient PDQ protocol for one order and four equality comparisons, achieving an amortized time and communication cost of 57 milliseconds and 448 bytes per database element.
Fast Hybrid PSO-APF Algorithm for Path Planning in Obstacle Rich Environment

The present study aims to evolve a new hybrid algorithm for fast path planning in an obstacle-rich environment using the particle swarm optimization (PSO) and artificial potential field (APF). The proposed methodology combines the PSO with APF to improve the speed of determining a feasible and minimum cost path in an environment which has a high obstacle density. The proposed hybridization generates the way-points to be followed, by employing a combination of PSO and APF and it is found that in the environment with different obstacle densities, the hybrid algorithm arrives at the minimum cost feasible path in significantly lesser time in comparison to the basic PSO. It is also shown that the proposed hybrid algorithm is significantly faster than PSO even when there are moving obstacles and hence has the potential to be employed in real-time applications.
Continual Prediction of Bug-Fix Time Using Deep Learning-Based Activity Stream Embedding

Predicting the fix time of a bug is important for managing the resources and release milestones of a software development project.However, it is considered non-trivial to achieve high accuracy when predicting bug-fix times.We view that such difficulties come from the lack of continuous or posterior estimation based on subsequent developers' activities after a bug is initially reported.In this paper, we formulate the problem of bug-fix time prediction into a continual update of estimates with more activities.Logging data of bug-related activities that are streamed to a bug tracking system change the bug reports, enabling us to recalculate predictions over time.To do so, we propose a deep learning-based two-staged activity stream embedding model, DASENet that employs (i) a merged network for extracting contextual features across different types of logs, and (ii) a sequence network for exploring temporal relations of the logs.Through experiments with bug tracking system datasets from open source projects including Firefox, Chromium, and Eclipse, we show that DASENet achieves stable performance, e.g., for the Firefox dataset, top-1 accuracy of 4.6 to 8.5 % higher than other state-of-the-art works.Our approach also provides a transferable structure, yielding robust performance with a small dataset for different tasks; the DASENet model trained with a small dataset of about 900 samples (2 % of a full dataset) can show competitive performance to the other models with a full dataset.To the best of our knowledge, we are the first to employ deep learning on log streams in the context of bug-fix time prediction.
Audio-first VR: New perspectives on musical experiences in virtual environments

This special issue of the Journal of New Music Research explores VR (Virtual Reality) through the lenses of music, art and technology, each focusing on foregrounded sonic expression – an audio-first VR, wherein sound is treated not only as an integral part of immersive virtual experiences but also as a critical point of departure for creative and technological work in this domain. In this article, we identify emerging challenges and opportunities in audio-first VR, and pose questions pertaining to both theoretical and practical aspects of this concept. We then discuss how each contribution to our special issue addresses these questions through research and artistic projects, giving us a glimpse into the future of audio in VR.
New Secure Block Cipher for Critical Applications: Design, Implementation, Speed and Security Analysis

Most of known methods for confidentiality and privacy ensuring don’t provide high-security level against cyberattacks based on linear and differential cryptanalysis and the required cryptographic data processing speed. In view of this, the cryptographic security method for critical infrastructure systems has been developed. On the basis of this method, the Luna-2k17 block cipher was designed. The specifications of this cipher are given in this paper. Also, high bound values of parameters characterizing its practical security against cyberattacks of mentioned categories of cryptanalysis are calculated. Under the same conditions, to evaluate the speed characteristics of ciphers experimental studies were conducted. Results of experiments showed that the Luna-2k17 cipher is faster than GOST 28147-89 cipher approximately in 3.11 times as well as in 1,27 times for the Kalyna and AES.
Automated Feature Enhancement for Predictive Modeling using External Knowledge

Supervised machine learning is the task of learning a function that maps features to a target. The strength of that function or the model depends directly on the features provided to the learning algorithm. Specifically, a crucial means of improving the model quality is to add new predictive features. This is often performed by domain specialists or data scientists. It is a hard and time-consuming task because the domain expert needs to identify data sources for new features, join them, and then select those that actually are relevant to the prediction. We present a new system called KAFE (Knowledge Aided Feature Engineering), an interactive predictive modeling system that automatically utilizes structured knowledge present on the web to perform feature addition to improve the accuracy of predictive models. In this proposal, we describe the key techniques such as feature inference and selection, relevant data indexing, and demonstrate its use through an interactive Jupyter notebook.
PVHybNet: a hybrid framework for predicting photovoltaic power generation using both weather forecast and observation data

Photovoltaics has gained popularity as a renewable energy source in recent decades. The main challenge for this energy source is the instability in the amount of generated energy owing to its strong dependency on the weather. Therefore, prediction of solar power generation is important for reliable and efficient operation. Popular data sources for predictors are largely divided into recent weather records and numerical weather predictions. This study proposes adequate deep neural networks that can utilise each data source or both. Focusing on a 24-hour-ahead prediction problem, the authors first design two deep neural networks for prediction: a deep feedforward network that uses the weather forecast data and a recurrent neural network that uses recent weather observations. Finally, a hybrid network, named PVHybNet, combines the both networks to enhance their prediction performance. In predicting the solar power generation by Yeongam power plant in South Korea, the final model yields an R-squared value of 92.7%. The results support the effectiveness of the combined network that utilises both weather forecasts and recent weather observations. The authors also demonstrate that the hybrid model outperforms several machine learning models.
Prediction of Horizontal Daily Global Solar Irradiation Using Artificial Neural Networks (ANNs) in the Castile and León Region, Spain

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.
Model of Multi-turn Dialogue in Emotional Chatbot

The intent recognition and natural language understanding of multi-turn dialogue is key for the commercialization of chatbots. Chatbots are mainly used for the processing of specific tasks, and can introduce products to customers or solve related problems, thus saving human resources. Text sentiment recognition enables a chatbot to know the user's emotional state and select the best response, which is important in medical care. In this study, we combined the multi-turn dialogue model and sentiment recognition model to develop a chatbot, that is designed for used in daily conversations rather than for specific tasks. Thus, the chatbot has the ability to provide the robot's emotions as feedback while talking with a user. Moreover, it can exhibit different emotional reactions based on the content of the user's conversation.
Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines.However, when graphs are used as input to machine learning models, this rich temporal information is frequently disregarded during the learning process, resulting in suboptimal performance on certain temporal inference tasks.To combat this, we introduce Temporal Neighbourhood Aggregation (TNA), a novel vertex representation model architecture designed to capture both topological and temporal information to directly predict future graph states.Our model exploits hierarchical recurrence at different depths within the graph to enable exploration of changes in temporal neighbourhoods, whilst requiring no additional features or labels to be present.The final vertex representations are created using variational sampling and are optimised to directly predict the next graph in the sequence.Our claims are supported by experimental evaluation on both real and synthetic benchmark datasets, where our approach demonstrates superior performance compared to competing methods, outperforming them at predicting new temporal edges by as much as 23% on real-world datasets, whilst also requiring fewer overall model parameters.
An Internet of Things Roaming Authentication Protocol Based on Heterogeneous Fusion Mechanism

With the increasing and diversified Internet of Things (IoT) devices, more IoT heterogeneous wireless networks have emerged, providing more network services for IoT devices, especially mobile phones and other roaming devices.However, there are also some malicious users who use different means to attack the security of the network, so that more users begin to pay attention to the identity authentication and privacy protection of the Internet of Things.This paper designs an IoT node roaming authentication model, which is used to enhance the security authentication capability of the Internet of Things to roaming devices.In order to effectively prevent malicious nodes from connecting to the network, this paper proposes a roaming authentication protocol based on heterogeneous fusion mechanism (HFM-IoT).The authentication protocol uses the remote authentication server in the local and remote areas to perform interactive authentication on the roaming device, which increases the difficulty of attacking or infecting multiple network areas by malicious nodes.According to the security analysis, the protocol can protect against multiple network attacks, and it can be seen from the experimental simulation results that the protocol has lower energy burden and authentication delay.
Seismic Hazard Assessment in Australia: Can Structured Expert Elicitation Achieve Consensus in the “Land of the Fair Go”?

Abstract The 2018 National Seismic Hazard Assessment of Australia incorporated 19 alternative seismic-source models developed by members of the Australian seismological community. The diversity of these models demonstrates the deep epistemic uncertainty that exists with regards to how best to characterize seismicity in stable continental regions. In the face of similarly high uncertainty, a diverse range of ground-motion models was proposed for use. A complex logic tree was developed to incorporate the alternative component models into a single hazard model. Expert opinion was drawn upon to weight the alternative logic-tree branches through a structured expert elicitation process. Expert elicitation aims to transparently and reproducibly characterize the community distribution of expert estimates for uncertain quantities and thereby quantify the epistemic uncertainty around estimates of seismic hazard in Australia. We achieve a multimodel rational consensus in which each model, and each expert, is, in accordance with the Australian cultural myth of egalitarianism, given a “fair go”—that is, judged on their merits rather than their status. Yet despite this process, we find that the results are not universally accepted. A key issue is a contested boundary between what is scientifically reducible and what remains epistemologically uncertain, with a particular focus on the earthquake catalog. Furthermore, a reduction, on average, of 72% for the 10% in 50 yr probability of exceedance peak ground acceleration levels compared with those underpinning existing building design standards, challenges the choice of metrics upon which design codes are based. Both quantification of the bounds of epistemic uncertainties through expert elicitation and reduction of epistemic uncertainties through scientific advances have changed our understanding of how the hazard behaves. Dialog between scientists, engineers, and policy makers is required to ensure that as our understanding of the hazard evolves, the hazard metrics used to underpin risk management decisions are re-evaluated to ensure societal aims are achieved.
Adoption of Blockchain Technology in the Australian Grains Trade: An Assessment of Potential Economic Effects

Recent analysis of Blockchain use has highlighted considerable potential productivity gains arising from lower transaction costs between buyers and sellers of goods. This has been shown by recent examples of Blockchain use in the Australian grains sector. In this paper, we have further developed and quantified this concept of productivity gain by undertaking several illustrative scenarios using a general equilibrium model of the global economy. Our analysis indicates that an assumed modest growth (five per cent) in productivity due to Blockchain use in the grains sector could raise output by eight per cent over the medium term. If this is accompanied by Blockchain use in the Australian finance sector, grains output could reach ten per cent. This reflects the effect of reduction in transaction costs due to the use of Blockchain technology as a “distributed ledger technology” in grain trading. Further, it is anticipated that the wider effects of Blockchain‐driven productivity enhancement of the Australian finance sector could contribute to approximately 2.5 per cent increase in GDP in the medium term, relative to what would otherwise be.
Collaborative Agent Communities for Resilient Service Composition in Mobile Environments

Automatic planning, with dynamic binding and adaptive composition recovery, has been used to tackle complex service provisioning in mobile environments, but given frequent network topology changes, and services with time-dependent QoS, finding composites that can functionally and non-functionally satisfy a user's request remains difficult. Many service composition mechanisms either require a centralised perspective of the environment, or use optimisation mechanisms that trade off computational efficiency for optimality. Stigmergy-based approaches have been used to model decentralised service interactions between service providers, using a community of mobile software agents that share the same goal to approximate the set of QoS-optimal service compositions. Inspired by this model, this article addresses computational efficiency concerns using a collaborative approach to engage multiple communities of agents for provisioning QoS-optimal service compositions in mobile environments. New compositions can emerge from local decisions and interactions with agents from diverse communities. We assess whether having multiple communities improves the diversity and optimality of solutions. We also measure the proposed approach’ efficiency in dealing with incomplete information. The results show that the proposed approach trades optimality for a more diverse set of solutions, at a cost of higher overhead.
Fine-grained recognition of error correcting codes based on 1-D convolutional neural network

Forward error correction (FEC) codes are the most commonly used method in channel coding, and it is capable of automatically detecting and correcting errors at the receiving end during transmission. At present, the channel coding semi-blind recognition given known FEC codes type has been extensively studied. However, in cognitive radio and non-cooperative systems, the types of channel coding are unknown at the receiving end. Therefore, the ability to identify the type of error correcting codes without any a priori knowledge is essential. In this paper, we proposed a novel method based on convolutional neural network that can achieve fine-grained type recognition for error correcting codes in non-cooperative systems. The proposed algorithm classifies the incoming data symbols among hamming codes, Bose-Chaudhuri-Hocquenghem (BCH) codes, Reed-Solomon (RS) codes, Low-density Parity-check (LDPC) codes, turbo codes, polar codes, and convolutional codes. Further, to train a well-behaved CNN model, we constructed a deep fusion model based on 1-D convolutional layer and modified 1-D inception architecture that can achieve end-to-end extraction of features. Experimental results show that the proposed model earns an average recognition accuracy of roughly 99% under the condition of signal-to-noise ratio (SNR) ranging from 6 dB to 20 dB. In addition, we conduct a comprehensively and thoroughly investigation on the performance of convolutional neural network based code recognition for digital communications.
On the Effectiveness of Tools to Support Infrastructure as Code: Model-Driven Versus Code-Centric

Infrastructure as Code (IaC) is an approach for infrastructure automation that is based on software development practices.The IaC approach supports code-centric tools that use scripts to specify the creation, updating and execution of cloud infrastructure resources.Since each cloud provider offers a different type of infrastructure, the definition of an infrastructure resource (e.g., a virtual machine) implies writing several lines of code that greatly depend on the target cloud provider.Model-driven tools, meanwhile, abstract the complexity of using IaC scripts through the high-level modeling of the cloud infrastructure.In a previous work, we presented an infrastructure modeling approach and tool (Argon) for cloud provisioning that leverages model-driven engineering and supports the IaC approach.The objective of the present work is to compare a model-driven tool (Argon) with a well-known code-centric tool (Ansible) in order to provide empirical evidence of their effectiveness when defining the cloud infrastructure, and the participants' perceptions when using these tools.We, therefore, conducted a family of three experiments involving 67 Computer Science students in order to compare Argon with Ansible as regards their effectiveness, efficiency, perceived ease of use, perceived usefulness, and intention to use.We used the AB/BA crossover design to configure the individual experiments and the linear mixed model to statistically analyze the data collected and subsequently obtain empirical findings.The results of the individual experiments and metaanalysis indicate that Argon is more effective as regards supporting the IaC approach in terms of defining the cloud infrastructure.The participants also perceived that Argon is easier to use and more useful for specifying the infrastructure resources.Our findings suggest that Argon accelerates the provisioning process by modeling the cloud infrastructure and automating the generation of scripts for different DevOps tools when compared to Ansible, which is a code-centric tool that is greatly used in practice.