Reviewerly/exHarmony huggingface dataset:
Automated temporal equilibrium analysis: Verification and synthesis of multi-player games

In the context of multi-agent systems, the rational verification problem is concerned with checking which temporal logic properties will hold in a system when its constituent agents are assumed to behave rationally and strategically in pursuit of individual objectives. Typically, those objectives are expressed as temporal logic formulae which the relevant agent desires to see satisfied. Unfortunately, rational verification is computationally complex, and requires specialised techniques in order to obtain practically useable implementations. In this paper, we present such a technique. This technique relies on a reduction of the rational verification problem to the solution of a collection of parity games. Our approach has been implemented in the Equilibrium Verification Environment (EVE) system. The EVE system takes as input a model of a concurrent/multi-agent system represented using the Simple Reactive Modules Language (SRML), where agent goals are represented as Linear Temporal Logic () formulae, together with a claim about the equilibrium behaviour of the system, also expressed as an formula. EVE can then check whether the claim holds on some (or every) computation of the system that could arise through agents choosing Nash equilibrium strategies; it can also check whether a system has a Nash equilibrium, and synthesise individual strategies for players in the multi-player game. After presenting our basic framework, we describe our new technique and prove its correctness. We then describe our implementation in the EVE system, and present experimental results which show that EVE performs favourably in comparison to other existing tools that support rational verification.
Toward ontology‐based risk management framework for software projects: An empirical study

Abstract Software risk management is a proactive decision‐making practice with processes, methods, and tools for managing risks in a software project. Many existing techniques for software project risk management are textual documentation with varying perspectives that are nonreusable and cannot be shared. In this paper, a life‐cycle approach to ontology‐based risk management framework for software projects is presented. A dataset from literature, domain experts, and practitioners is used. The identified risks are refined by 19 software experts; risks are conceptualized, modeled, and developed using Protégé. The risks are qualitatively analyzed and prioritized, and aversion methods are provided. The framework is adopted in real‐life software projects. Precision recall and F‐measure metrics are used to validate the performance of the extraction tool while performance and perception evaluation are carried out using the performance appraisal form and technology acceptance model, respectively. Mean scores from performance and perception evaluation are compared with evaluation concept scale. Results showed that cost is reduced, high‐quality projects are delivered on time, and software developers found this framework a potent tool needed for their day‐to‐day activities in software development.
Recurrent convolutional networks for session-based recommendations

Traditional recommender systems help users find items of interest by modeling long-term user profiles, which consist of items the users interacted with in the past. In many real-life applications, however, informative user profiles are not available. Instead, the system recommends by relying on the current user activities within an ongoing session, leading to the emergence of session-based recommendation methods. Among techniques used in such situations, recurrent neural networks (RNNs) present a natural choice thanks to their ability to model the order of session events and capture long-term dependencies. Recently, methods based on convolutional neural networks (CNNs) have also shown their potential in modeling session data, especially in extracting complex local patterns that are predictive of the user target. In this work, we propose a recurrent convolutional architecture that takes the advantages of both complex local features extracted by CNNs and long-term dependencies learned by RNNs from session sequences. Our model has two main layers: the lower layer consists of convolutional filters applied over consecutive session event embeddings, and the upper layer is a gated recurrent unit (GRU) RNN that takes as input the CNN’s output. This hybrid approach provides a flexible and unified network architecture for modeling various important features of session sequences. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed model over pure RNNs and CNNs models as well as state-of-the-art session-based recommendation methods.
A Cyberattacks Detection Technique Based on Evolutionary Algorithms

An article presents an approach for cyberattack detection based on genetic algorithms is presented. The method allows detecting both known and unknown cyberattacks. The method has the heuristic nature and is based on the collected data about the cyberattacks. It makes it possible to give an answer about the cyberattacks’ existence in the computer networks and its hosts. Developed attack detection approach consists of training and detection stages. The mechanism of attack detection system is based on the cyberattacks’ features gathering from network or hosts, extracting the subset of acquired set and generation the attacks’ detection rules. Genetic algorithms are used for the minimization of the feature set, which allows effective using of the system resources for attacks detection. In order to detect the attacks, the proposed technique involves the rule generation. The attacks’ features are described by the set of sub-rules. It is suggested to use the feature with the smallest domain for generating the minimal set for rules. It is possible to select the optimal feature after all selected features which were discovered while applying the genetic algorithm. The sub-rule set is used with the aim to reduce false positive rate.
Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study

In this work, we assess how pre-training strategy affects deep learning performance for the task of distinguishing false-recall from malignancy and normal (benign) findings in digital mammography images. A cohort of 1303 breast cancer screening patients (4935 digital mammogram images in total) was retrospectively analyzed as the target dataset for this study. We assessed six different convolutional neural network model structures utilizing four different imaging datasets (total > 1.4 million images (including ImageNet); medical images different in terms of scale, modality, organ, and source) for pre-training on six classification tasks to assess how the performance of CNN models varies based on training strategy. Representative pre-training strategies included transfer learning with medical and non-medical datasets, layer freezing, varied network structure, and multi-view input for both binary and triple-class classification of mammogram images. The area under the receiver operating characteristic curve (AUC) was used as the model performance metric. The best performing model out of all experimental settings was an AlexNet model incrementally pre-trained on ImageNet and a large Breast Density dataset. The AUC for the six classification tasks using this model ranged from 0.68 to 0.77. In the case of distinguishing recalled-benign mammograms from others, four out of five pre-training strategies tested produced significant performance differences from the baseline model. This study suggests that pre-training strategy influences significant performance differences, especially in the case of distinguishing recalled- benign from malignant and benign screening patients.
Image Captioning with a Joint Attention Mechanism by Visual Concept Samples

The attention mechanism has been established as an effective method for generating caption words in image captioning; it explores one noticed subregion in an image to predict a related caption word. However, even though the attention mechanism could offer accurate subregions to train a model, the learned captioner may predict wrong, especially for visual concept words, which are the most important parts to understand an image. To tackle the preceding problem, in this article we propose Visual Concept Enhanced Captioner, which employs a joint attention mechanism with visual concept samples to strengthen prediction abilities for visual concepts in image captioning. Different from traditional attention approaches that adopt one LSTM to explore one noticed subregion each time, Visual Concept Enhanced Captioner introduces multiple virtual LSTMs in parallel to simultaneously receive multiple subregions from visual concept samples. Then, the model could update parameters by jointly exploring these subregions according to a composite loss function. Technically, this joint learning is helpful in finding the common characters of a visual concept, and thus it enhances the prediction accuracy for visual concepts. Moreover, by integrating diverse visual concept samples from different domains, our model can be extended to bridge visual bias in cross-domain learning for image captioning, which saves the cost for labeling captions. Extensive experiments have been conducted on two image datasets (MSCOCO and Flickr30K), and superior results are reported when comparing to state-of-the-art approaches. It is impressive that our approach could significantly increase BLUE-1 and F1 scores, which demonstrates an accuracy improvement for visual concepts in image captioning.
Towards a Decentralized and Distributed Framework for Open Educational Resources based on IPFS and Blockchain

Open Educational Resources (OER) play a pivotal role in the traditional teaching learning processes. So far, there are several problems in the conventional OER such as tracking and tracing of OER, proper attribution, sustainability, publishing time and single point of failure due to its centralized nature. This paper proposes a Ethereum blockchain based decentralized and distributed framework to alleviate the problems existing in present OER. In addition Interplanetary File System (IPFS) has been included in this proposed system for storing the hypermedia as distributed way. It reduces the storage time. In this paper, we design distributed and decentralized infrastructure for OER system that remedies all of these drawbacks through the use of the Ethereum blockchain platform.
Kernel Attention Network for Single Image Super-Resolution

Recently, attention mechanisms have shown a developing tendency toward convolutional neural network (CNN), and some representative attention mechanisms, i.e., channel attention (CA) and spatial attention (SA) have been fully applied to single image super-resolution (SISR) tasks. However, the existing architectures directly apply these attention mechanisms to SISR without much consideration of the nature characteristic, resulting in less strong representational power. In this article, we propose a novel kernel attention module (KAM) for SISR, which enables the network to adjust its receptive field size corresponding to various scales of input by dynamically selecting the appropriate kernel. Based on this, we stack multiple kernel attention modules with group and residual connection to constitute a novel architecture for SISR, which enables our network to learn more distinguishing representations through filtering the information under different receptive fields. Thus, our network is more sensitive to multi-scale features, which enables our single network to deal with multi-scale SR task by predefining the upscaling modules. Besides, other attention mechanisms in super-resolution are also investigated and illustrated in detail in this article. Thanks to the kernel attention mechanism, the extensive benchmark evaluation shows that our method outperforms the other state-of-the-art methods.
Intelligent Learning based Opinion Mining Model for Governmental Decision Making

Government in a country is the foremost legislative body responsible for taking decisive steps, planning schemes and implementing them with zero margins of error. These schemes and policies directly or indirectly affect the population of the country and direct the rate of social and economic growth. Effective policy framing and implementations have been the primary aim of all governments. But for good governance with long term sustainability taking opinions of the general public becomes indispensable. Twitter is one such open platform for a new type of social interaction where people come forward and express their views not only on products, movies and celebrities but also those critical policies and schemes designed by the government with aim of the overall development. These opinions have a lot more weight and convey a major message to the policymakers if evaluated correctly. This paper elucidates one such framework which mines opinion of general users tweeting on twitter about government policies and classifies them into three different polarities i.e. positive, negative and neutral. Machine Learning and Deep Learning method along with Natural Language Processing techniques has been utilized to extract the sentiments of the tweet and perform analysis on its polarity. The results of this detailed analysis can act as feedback to the governing bodies which can give them a better idea of the demography of the public’s opinion in an effective manner. Thus, this research works presents a technology-based solution for smart governance and interactive policy framing.
Automated Short-Answer Grading Using Deep Neural Networks and Item Response Theory

Automated short-answer grading (ASAG) methods using deep neural networks (DNN) have achieved state-of-the-art accuracy. However, further improvement is required for high-stakes and large-scale examinations because even a small scoring error will affect many test-takers. To improve scoring accuracy, we propose a new ASAG method that combines a conventional DNN-ASAG model and an item response theory (IRT) model. Our method uses an IRT model to estimate the test-taker’s ability from his/her true-false responses to objective questions that are offered with a target short-answer question in the same test. Then, the target short-answer score is predicted by jointly using the ability value and a distributed short-answer representation, which is obtained from an intermediate layer of a DNN-ASAG model.
Parallel Machine Learning Algorithm Using Fine-Grained-Mode Spark on a Mesos Big Data Cloud Computing Software Framework for Mobile Robotic Intelligent Fault Recognition

An accurate and efficient intelligent fault diagnosis of mobile robotic roller bearings can significantly enhance the reliability and safety of mechanical systems.To improve the efficiency of intelligent fault classification of mobile robotic roller bearings, this paper proposes a parallel machine learning algorithm using fine-grained-mode Spark on a Mesos big data cloud computing software framework.Through the segmentation of datasets and the support of a parallel framework, the parallel processing technology Spark is combined with a support vector machine (SVM), and a parallel single-SVM algorithm is realized using Scala language.In this approach, empirical mode decomposition (EMD) is applied to extract the energy of the acceleration vibration signal in different frequency bands as features.The parallel EMD-SVM approach is applied to detect faults in mobile robotic roller bearings from fault vibration signals.The experimental results show that it can accurately and effectively identify the faults, and it outperforms existing methods based on parallel deep belief network (DBN) and parallel radial basis function neural network under different training set sizes.Fault classification tests are conducted on outer-race and inner-race faults: in both cases, the proposed parallel EMD-SVM outperforms the serial EMD-SVM in terms of the classification accuracy and classification time under different test sizes.For a small number of nodes, the processing time of the proposed Spark model is less than that of Hadoop but close to that of Storm.For 17 slave nodes, the average precision, average recall, and average F1 score of Spark on Mesos in the fine-grained mode reach 97.3, 97.8, and 97.9%, respectively.The parallel EMD-SVM algorithm in the big data Spark cloud computing framework can improve the accuracy of intelligent fault classification, albeit by a small margin, with higher processing speed and learning convergence rate.
Round Optimal Secure Multisignature Schemes from Lattice with Public Key Aggregation and Signature Compression

This paper presents the first construction for an efficient multisignature (MS) in the lattice setting, achieving signature compression and public key aggregation simultaneously with single round signature generation. The multisignature size in our construction is the same as that of a single signature. The verification of a multisignature can be performed with the aggregated public key and the verifier gets convinced that the message has been signed by all the signers. More positively, our aggregated public key size is also the same as that of a single signer. Additionally, we extend our multisignature to an accountable subgroup multisignature (ASM) that permits any subset of potential signers to sign a common message with the property that the signature reveals the identities of the signers to any verifier. Our ASM scheme enjoys the same efficiency as that of our MS scheme without incurring any loss in the security reduction. We design our schemes in the plain public key model where there is no need to verify individual public keys. Our constructions are built in the standard lattice and are proven to be secure under the hardness of the short integer solution (SIS) problem in the random oracle model.
PrefixSpan Based Pattern Mining Using Time Sliding Weight From Streaming Data

This study proposes the prefixSpan based pattern mining using time sliding weight from streaming data.To discover sequential patterns, it applies a time sliding weight to create a structure of projected DB Tree.For the time sliding weight, a time window is applied to the sequential data to calculate the label and support of the window.When a projected DB Tree is designed, the time weight calculated for each pattern is inserted in a table.At this time, the tree is updated by deleting the node whose time weight is less than the reference value.For this reason, whenever data is updated, the tree is sorted again.The reordering process removes the pattern of less influence by applying time weights.Therefore, it is possible to construct a projected DB Tree that can extract influential patterns.The performance of the proposed method is evaluated in three aspects.Firstly, the conventional PrefixSpan algorithm is compared with the proposed PrefixSpan algorithm based on time sliding weight in terms of the pattern generation time according to size of data.Secondly, the fitness of the proposed algorithm is evaluated through cross-validation.Thirdly, GSP, SPADE, and prefixSpan algorithms are compared with the proposed algorithm through F-measure.As a result of the evaluation, the proposed algorithm improves accuracy 75% more than PrefixSpan algorithm and the sequential pattern algorithms of GSP and SPADE.Regarding the comparison of F-measure based on precision and recall, the proposed one improves its performance about 83%.
A privacy-preserving public integrity check scheme for outsourced EHRs

Cloud storage as an emerging technology has already drawn people's attention and hospitals and medical institutions increasingly begin to outsource their huge electronic health records (EHRs) to cloud. Since EHRs always involve lots of sensitive privacy, it is necessary to protect the confidentiality and privacy of EHRs. Meanwhile, existing works do not consider that combining classified storage with special data structures to improve the efficiency of integrity check for outsourced EHRs. In this paper, we propose an efficient integrity check scheme for classified EHRs. First, our scheme not only protects the privacy of category information for EHRs, but also ensures the EHRs themselves. Second, we improve the performance of lookup and data updating dramatically by deploying adjustable-capacity cuckoo filter with a linked list, which is a container storing the EHRs' ciphertexts and verification metadata. Third, we implement our scheme in a more practical way by extending batch auditing of EHRs from single category to multi-category. The provable update for the cloud service provider and the third-party auditor after updating EHRs is also guaranteed. Finally, the security of our scheme is proven, and numerical analyses and simulation experiments show that our scheme is efficient.
Supervised learning for the detection of negation and of its scope in French and Brazilian Portuguese biomedical corpora

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.
Double Graph Regularized Double Dictionary Learning for Image Classification

In this paper, we present a novel double graph regularized double dictionary learning (DGRDDL) method for image classification. The proposed method jointly constructs a number of class-specific sub-dictionaries to capture the most discriminative features (class-specific information) of each class, and a class-shared dictionary to model the common patterns (class-shared information) shared by the images from different classes. A novel double graph regularization is proposed to correctly represent and differentiate these two types of information. Specifically, an intra-class similarity graph constraint is imposed on the representation coefficients over the class-specific dictionaries, and an inter-class similarity graph constraint is applied on the representation coefficients over the class-shared dictionary. In this way, the representations learned by the proposed DGRDDL method can correctly model the local similarity relationships of the class-specific and the class-shared information in images, respectively. Moreover, due to the differences between the intra-class and inter-class similarity graphs, the two types of information can be appropriately separated and captured by the learned dictionaries. We evaluate the performance of the proposed method on six public datasets and compared against those of seven benchmark methods. The experimental results demonstrate the effectiveness and superiority of the proposed method in image classification over the benchmark dictionary learning methods.