Reviewerly/exHarmony huggingface dataset:
Quorums enable optimal pooling of independent judgements in biological systems

Collective decision-making is ubiquitous, and majority-voting and the Condorcet Jury Theorem pervade thinking about collective decision-making. Thus, it is typically assumed that majority-voting is the best possible decision mechanism, and that scenarios exist where individually-weak decision-makers should not pool information. Condorcet and its applications implicitly assume that only one kind of error can be made, yet signal detection theory shows two kinds of errors exist, 'false positives' and 'false negatives'. We apply signal detection theory to collective decision-making to show that majority voting is frequently sub-optimal, and can be optimally replaced by quorum decision-making. While quorums have been proposed to resolve within-group conflicts, or manage speed-accuracy trade-offs, our analysis applies to groups with aligned interests undertaking single-shot decisions. Our results help explain the ubiquity of quorum decision-making in nature, relate the use of sub- and super-majority quorums to decision ecology, and may inform the design of artificial decision-making systems.This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
HOMPer: A new hybrid system for opinion mining in the Persian language

Opinion mining is a subfield of data mining and natural language processing that concerns with extracting users’ opinion and attitude towards products or services from their comments on the Web. Persian opinion mining, in contrast to its counterpart in English, is a totally new field of study and hence, it has not received the attention it deserves. Existing methods for opinion mining in the Persian language may be classified into machine learning– and lexicon-based approaches. These methods have been proposed and successfully used for polarity-detection problem. However, when they should be used for more complex tasks like rating prediction, their results are not desirable. In this study, first an exhaustive investigation of machine learning– and lexicon-based methods is performed. Then, a new hybrid method is proposed for rating-prediction problem in the Persian language. Finally, the effect of machine learning component, feature-selection method, normalisation method and combination level are investigated. The experimental results on a large data set containing 16,000 Persian customers’ review show that this proposed system achieves higher performance in comparison to Naïve Bayes algorithm and a pure lexicon-based method. Moreover, results demonstrate that this proposed method may also be successfully used for polarity detection.
Evolving neural networks using bird swarm algorithm for data classification and regression applications

This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets.
Retrospective model-based inference guides model-free credit assignment

An extensive reinforcement learning literature shows that organisms assign credit efficiently, even under conditions of state uncertainty. However, little is known about credit-assignment when state uncertainty is subsequently resolved. Here, we address this problem within the framework of an interaction between model-free (MF) and model-based (MB) control systems. We present and support experimentally a theory of MB retrospective-inference. Within this framework, a MB system resolves uncertainty that prevailed when actions were taken thus guiding an MF credit-assignment. Using a task in which there was initial uncertainty about the lotteries that were chosen, we found that when participants' momentary uncertainty about which lottery had generated an outcome was resolved by provision of subsequent information, participants preferentially assigned credit within a MF system to the lottery they retrospectively inferred was responsible for this outcome. These findings extend our knowledge about the range of MB functions and the scope of system interactions.
Modality-Invariant Image-Text Embedding for Image-Sentence Matching

Performing direct matching among different modalities (like image and text) can benefit many tasks in computer vision, multimedia, information retrieval, and information fusion. Most of existing works focus on class-level image-text matching, called cross-modal retrieval , which attempts to propose a uniform model for matching images with all types of texts, for example, tags, sentences, and articles (long texts). Although cross-model retrieval alleviates the heterogeneous gap among visual and textual information, it can provide only a rough correspondence between two modalities. In this article, we propose a more precise image-text embedding method, image-sentence matching, which can provide heterogeneous matching in the instance level. The key issue for image-text embedding is how to make the distributions of the two modalities consistent in the embedding space. To address this problem, some previous works on the cross-model retrieval task have attempted to pull close their distributions by employing adversarial learning. However, the effectiveness of adversarial learning on image-sentence matching has not been proved and there is still not an effective method. Inspired by previous works, we propose to learn a modality-invariant image-text embedding for image-sentence matching by involving adversarial learning. On top of the triplet loss--based baseline, we design a modality classification network with an adversarial loss, which classifies an embedding into either the image or text modality. In addition, the multi-stage training procedure is carefully designed so that the proposed network not only imposes the image-text similarity constraints by ground-truth labels, but also enforces the image and text embedding distributions to be similar by adversarial learning. Experiments on two public datasets (Flickr30k and MSCOCO) demonstrate that our method yields stable accuracy improvement over the baseline model and that our results compare favorably to the state-of-the-art methods.
A Heuristic Statistical Testing Based Approach for Encrypted Network Traffic Identification

In recent years, malware with strong concealment uses encrypted protocol to evade detection. Thus, encrypted traffic identification can help security analysts to be more effective in narrowing down those encrypted network traffic. Existing methods are protocol independent, such as statistical-based and machine-learning-based approaches. Statistical-based approaches, however, are confined to payload length and machine-learning-based approaches have a low recognition rate for encrypted traffic using undisclosed protocols. In this paper, we proposed a heuristic statistical testing (HST) approach that combines both statistics and machine learning and has been proved to alleviate their respective deficiencies. We manually selected four randomness tests to extract small payload features for machine learning to improve real-time performances. We also proposed a simple handshake skipping method called HST-R to increase the classification accuracy. We compared our approach with other identification approaches on a testing dataset consisting of traffic that uses two known, two undisclosed, and one custom cryptographic protocols. Experimental results showed that HST-R performs better than other traditional coding-based, entropy-based, and ML-based approaches. We also showed that our handshake skipping method could generalize better for unknown cryptographic protocols. Finally, we also conducted experimental comparisons among different classification algorithms. The results showed that C4.5, with our method, has the highest identification accuracy for secure sockets layer and secure shell traffic.
TableNet: An Approach for Determining Fine-grained Relations for Wikipedia Tables

We focus on the problem of interlinking Wikipedia tables with fine-grained table relations: equivalent and subPartOf. Such relations allow us to harness semantically related information by accessing related tables or facts therein. Determining the type of a relation is not trivial. Relations are dependent on the schemas, the cell-values, and the semantic overlap of the cell values in tables.
Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery

Heterogeneous networks are widely used to model real-world semi-structured data. The key challenge of learning over such networks is the modeling of node similarity under both network structures and contents. To deal with network structures, most existing works assume a given or enumerable set of meta-paths and then leverage them for the computation of meta-path-based proximities or network embeddings. However, expert knowledge for given meta-paths is not always available, and as the length of considered meta-paths increases, the number of possible paths grows exponentially, which makes the path searching process very costly. On the other hand, while there are often rich contents around network nodes, they have hardly been leveraged to further improve similarity modeling. In this work, to properly model node similarity in content-rich heterogeneous networks, we propose to automatically discover useful paths for pairs of nodes under both structural and content information. To this end, we combine continuous reinforcement learning and deep content embedding into a novel semi-supervised joint learning framework. Specifically, the supervised reinforcement learning component explores useful paths between a small set of example similar pairs of nodes, while the unsupervised deep embedding component captures node contents and enables inductive learning on the whole network. The two components are jointly trained in a closed loop to mutually enhance each other. Extensive experiments on three real-world heterogeneous networks demonstrate the supreme advantages of our algorithm. Code related to this paper is available at: https://github.com/yangji9181/AutoPath .
An intelligent water drops-based workflow scheduling for IaaS cloud

Cloud computing is an emerging technology in a distributed environment with a collection of large-scale heterogeneous systems. One of the challenging issues in the cloud data center is to select the minimum number of virtual machine (VM) instances to execute the tasks of a workflow within a time limit. The objectives of such a strategy are to minimize the total execution time of a workflow and improve resource utilization. However, the existing algorithms do not guarantee to achieve high resource utilization although they have abilities to achieve high execution efficiency. The higher resource utilization depends on the reusability of VM instances. In this work, we propose a new intelligent water drops based workflow scheduling algorithm for Infrastructure-as-a-Service (IaaS) cloud. The objectives of the proposed algorithm are to achieve higher resource utilization and minimize the makespan within the given deadline and budget constraints. The first contribution of the algorithm is to find multiple partial critical paths (PCPs) of a workflow which helps in finding suitable VM instances. The second contribution is a scheduling strategy for PCP-VM assignment for assigning the VM instances. The proposed algorithm is evaluated through various simulation runs using synthetic datasets and various performance metrics. Through comparison, we show the superior performance of the proposed algorithm over the existing ones.
Advanced tone rendition technique for a painting robot

One of the unresolved problems in the field of computer creativity is developing a robot capable of creating full-color images with artistic paints in a human-like manner. While several advanced painting machines have been presented up to date, high-grade color rendition remains one of the main bottlenecks in robotic painting. In this paper, we present a robotic setup for realistic grayscale painting. The key feature of our robot is a special paint mixing device aimed at improving tone rendition in comparison to previously reported approaches. We describe the main algorithmic and hardware solutions implemented in our robot as well as the first experimental results. The robot represents a 3-DoF CNC machine equipped with a brush, the paint mixing device and a syringe pump block for paint supplement. In our study, we focus on monochrome painting with black and white acrylic paints. A mathematical model of primary paint mixing is described. Two realistic artworks have been created during test runs, and their reproductions together with the source images are given. The accuracy of tone rendition was experimentally tested. Further research will be aimed at high-grade color rendition and creating full-color paintings.
Fuzzy Multi-task Learning for Hate Speech Type Identification

In traditional machine learning, classifiers training is typically undertaken in the setting of single-task learning, so the trained classifier can discriminate between different classes. However, this must be based on the assumption that different classes are mutually exclusive. In real applications, the above assumption does not always hold. For example, the same book may belong to multiple subjects. From this point of view, researchers were motivated to formulate multi-label learning problems. In this context, each instance can be assigned multiple labels but the classifiers training is still typically undertaken in the setting of single-task learning. When probabilistic approaches are adopted for classifiers training, multi-task learning can be enabled through transformation of a multi-labelled data set into several binary data sets. The above data transformation could usually result in the class imbalance issue. Without the above data transformation, multi-labelling of data results in an exponential increase of the number of classes, leading to fewer instances for each class and a higher difficulty for identifying each class. In addition, multi-labelling of data is very time consuming and expensive in some application areas, such as hate speech detection. In this paper, we introduce a novel formulation of the hate speech type identification problem in the setting of multi-task learning through our proposed fuzzy ensemble approach. In this setting, single-labelled data can be used for semi-supervised multi-label learning and two new metrics (detection rate and irrelevance rate) are thus proposed to measure more effectively the performance for this kind of learning tasks. We report an experimental study on identification of four types of hate speech, namely: religion, race, disability and sexual orientation. The experimental results show that our proposed fuzzy ensemble approach outperforms other popular probabilistic approaches, with an overall detection rate of 0.93.
Fractal modeling and fry analysis of the relationship between structures and Cu mineralization in Saveh region, Central Iran

The Saveh region in the middle part of the Urumieh-Dokhtar Magmatic Belt (UDMB), Central Iran, contains several Cu mineralization and deposits that may have been localized by structural controls in the region. Along the major thrust fault, the Saveh region comprises intrusive bodies associated with various Cu occurrences and deposits. The NW-SE major faults, especially the Khalkhab and Koshk-e-Nosrat faults on the Iranian plates, created a sigmoid space for the emplacement of plutons during middle Eocene. The Cu occurrences/deposits are either vein- or manto-style, with vein-style being dominant. The lineaments and faults in the Saveh region were mapped using precise local data including remote sensing (Landsat ETM+), digital elevation models (DEM), geomagnetic airborne images, geological maps and field observations. The relationship between Cu occurrences/deposits and geological structures (the latter being described by a lineament factor (LF) or fracture density) is important for focusing of ore-forming fluids and mineralization. This was analyzed by concentration-area (C-A) fractal modeling and by Fry analysis. The results of the C-A fractal modeling and Fry analysis indicate that the vein-style Cu mineralization is associated with major NW-SE-trending faults, especially the Khalkhab and Koshk-e-Nosrat faults. Manto-style Cu mineralization in the Saveh region is associated with minor NE-SW-trending faults.
Cyber-Attacks Against the Autonomous Ship

Autonomous ships transferring valuable cargoes and humans in a more efficient and cost effective manner will soon be state of the art technology. Yet, their ICT system architecture and operations have not been defined in full detail. Moreover, multiple cyber security issues remain open and should be addressed. No study to date has analyzed fully the architecture of the autonomous ship, even less so have potential cyber threats and cyber attacks been identified. In this paper we identify and categorize systems that make up an autonomous ship, we propose a generic system architecture, and we analyze the cyber security of the ship by leveraging the STRIDE threat modeling methodology to identify potential cyber attacks, and to analyze the accordant risk. The results will support ship designers and industry towards improving the autonomous ship system architecture and making ship operations more secure.
Neural Multimodal Belief Tracker with Adaptive Attention for Dialogue Systems

Multimodal dialogue systems are attracting increasing attention with a more natural and informative way for human-computer interaction. As one of its core components, the belief tracker estimates the user's goal at each step of the dialogue and provides a direct way to validate the ability of dialogue understanding. However, existing studies on belief trackers are largely limited to textual modality, which cannot be easily extended to capture the rich semantics in multimodal systems such as those with product images. For example, in fashion domain, the visual appearance of clothes play a crucial role in understanding the user's intention. In this case, the existing belief trackers may fail to generate accurate belief states for a multimodal dialogue system.
Privacy-preserving Data Mining in Industry

Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple's differential privacy deployment for iOS / macOS, Google's RAPPOR, LinkedIn Salary, and Microsoft's differential privacy deployment for collecting Windows telemetry. We will conclude with open problems and challenges for the data mining / machine learning community, based on our experiences in industry.
Forecasting by designing Mamdani general type-2 fuzzy logic systems optimized with quantum particle swarm optimization algorithms

Much more attention has been focused on studying and applying general type-2 fuzzy logic systems (GT2 FLSs) in recent years. The paper designs a type of Mamdani GT2 FLS for studying forecasting problems based on the data of permanent magnetic drive (PMD) loss. During the system design process, we choose the primary membership functions (MFs) of antecedent, consequent and input measurement general type-2 fuzzy sets (GT2 FSs) as Gaussian type MFs with uncertain standard deviations. The corresponding vertical slices (secondary MFs) are chosen as the triangle MFs. All the parameters of Mamdani GT2 FLSs are optimized by the quantum particle swarm optimization (QPSO) algorithms. Noisy data of PMD loss are adopted for both training and testing the proposed FLSs forecasting approaches. Simulation studies and convergence analysis are employed to show the effectiveness and feasibility of the proposed GT2 FLSs forecasting methods compared with their T1 and IT2 counterparts.
Designing a pseudorandom bit generator based on LFSRs and a discrete chaotic map

A new method for the generation of pseudorandom numbers, based on two linear feedback shift registers (LFSRs) and a discrete-space chaotic map is presented. The proposed method consists of multiple stages; in stage one a pseudorandom sequence is generated. In stage two a selection of output bits is conducted in order to produce sequences with high randomness. Because many previous methods based on LFSRs and XOR operations are not able to resist chosen and known plaintext attack, stage one of the proposed method is designed to be resistant to this problem. Relations between two LFSRs and the parameters of the chaotic map are established in such a way that based on known output bits of a proposed generator it is impossible to obtain any part of the secret key without guessing the whole secret key. The randomness of generated sequences is confirmed using NIST, TestU01, and the DIEHARD test. The advantages of the proposed method are virtually unlimited key space and resistance to digital degradation.
Comfortable Immersive Analytics With the VirtualDesk Metaphor

The VirtualDesk metaphor is an opportunity for more comfortable and efficient immersive data exploration, using tangible interaction with the analyst's physical work desk and embodied manipulation of mid-air data representations. In this paper, we present an extended discussion of its underlying concepts, and review and compare two previous case studies where promising results were obtained in terms of user comfort, engagement, and usability. We also discuss findings of a novel study conducted with geovisualization experts, pointing directions for improvement and future research.