Reviewerly/exHarmony huggingface dataset:
SOTagRec

Stack Overflow is one of the most popular online programming question and answer websites for developers around the world. Generally, developers need to provide tags for their posting. High-quality tags are expected to facilitate correct classification and efficient search. Unfortunately, tagging process is distributed and uncoordinated due to developers' understanding of their postings, English skills and preferences. Automatic tag recommendation becomes increasingly important for these information sites. In this paper, we propose SOTagRec, a novel tag recommendation approach combing convolutional neural network model and collaborative filtering method. By learning historical postings and their tags from existing information, SOTagRec can accurately infer tags for new postings. We have evaluated SOTagRec on Stackoverflow and compare with the state-of-the-art methods. Experiments Results show that SOTagRec achieves 81.7% and 88.7% respectively for [email protected] and [email protected], which outperforms the previous relevant methods.
DiagonalNet: Confidence diagonal lines for the UAV detection

Due to the maneuverability and small size of the Unmanned aerial vehicles (UAVs), the traditional object detection method cannot meet the requirements of detection accuracy and real‐time performance. To address this dilemma, we propose an object detection method by using the improved hourglass network as its backbone network generating the confidence diagonal lines as detection result, and then getting the bounding box through this diagonal line. As a one‐stage method, it has a good real‐time performance. At the same time, due to the good performance of our improved network, its mean average precision performs well. Through experiments with the UAV dataset, compared with faster‐regions‐with‐convolutional‐neural‐network and You only look once (YOLO), we show that the proposed framework can quickly and accurately detect objects. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Empirical likelihood estimation in multivariate mixture models with repeated measurements

Multivariate mixtures are encountered in situations where the data are repeated or clustered measurements in the presence of heterogeneity among the observations with unknown proportions. In such situations, the main interest may be not only in estimating the component parameters, but also in obtaining reliable estimates of the mixing proportions. In this paper, we propose an empirical likelihood approach combined with a novel dimension reduction procedure for estimating parameters of a two-component multivariate mixture model. The performance of the new method is compared to fully parametric as well as almost nonparametric methods used in the literature.
Hypothesis Testing in Presence of Adversaries

We present an extension to the classical problem of hypothesis testing by incorporating actions of an adversary who intends to mislead the decision-maker and attain a certain benefit. After presenting the general problem within an adversarial statistical decision theory framework, we consider the cases of adversaries who can either perturb the data received or modify the underlying data-generating process parametrically. Supplemental materials for this article are available online.
Deep anticipation: lightweight intelligent mobile sensing for unmanned vehicles in IoT by recurrent architecture

Integrating Internet of things (IoT) techniques into automated vehicles has been a vision in intelligent transportation system, there is however seldom researches addressing it. To this end, we envision a scenario: short-range on-board sensor perception system attached to individual mobile applications such as vehicles are connected via IoT and transferred to long-range mobile-sensing perception system, which can be used as part of a more extensive intelligent system surveilling the environment. However, the mobile sensing perception brings new challenges for how to efficiently analyse and intelligently interpret the deluge of IoT data in mission-critical services. Among these challenges, one bottelneck is the quality of service of IoT communication. In this article, we model the communication challenge as latency, packet delay variation and measurement noise which severely deteriorate the reliability and quality of IoT data. We propose a novel architecture that leverages recurrent neural networks and Kalman filtering to anticipate motions and interactions between objects. The model learns to develop a biased belief between prediction and measurement in different situations. We validate our neural architecture with synthetic and real-world datasets with noise that mimics the challenges of IoT communications. The proposed neural architecture outperforms state-of-the-art work in both computation time and model complexity.
Deep Learning Based Attack On Social Authentication System

As the rapid development of the Internet, social networks sites, such as Twitter and Facebook, are become more and more popular. These sites require users to provide various kinds of personal data to personalize their services. However, unintentionally disclosed personal information utilized by hackers may pose many security problems. In this paper, we present a deep learning based attack on the social authentication in the Facebook site. Social authentication, known as the fourth-factor authentication, utilizes social interaction information to achieve the identification of users. In the process of account recovery and remote login, the social authentication which requires users to identify names of friends displayed in photos is used as a part of the authentication module. After collecting public information of friends' photos through web crawler, the attach system uses deep learning networks based on face_recognition library and convolutional neural network (CNN for short) to train the model. The attack is achieved through the successful identification of friends' names using the trained deep learning model.
Human Improvised Theatre Augmented with Artificial Intelligence

Improvisational theatre (improv) has been proposed as a grand challenge for general artificial intelligence (AI)~\citemartin2016improvisational. Current state-of-the-art conversational intelligence models lack proper grounding, language understanding, and generate meaningless meandering responses~\citedziri2018augmenting. Utilizing them as improvised comedy partners (improvisors) is doomed to fail - curiously, this limitation makes their use particularly appealing. Improv theatre celebrates risk taking and failure by inviting performers to express themselves without hesitation or fear of being judged~\citejohnstone1979impro. Our installation is an interactive improv workshop for a group of interested participants, culminating in a live public performance. Attendees are invited to observe and interact with AI-based improvisational theatre technology. The workshop is facilitated by two improv theatre professionals with a combined 30 years of experience in teaching, training, and touring. The performance features various AI tools for augmented creativity.
Relevant Parameters for the Classification of Reading Books Depending on the Degree of Textual Readability in Primary and Compulsory Secondary Education (CSE) Students

This paper tries to establish the most important parameters of readability when it comes to choosing reading books for students in the second and third stages of primary and compulsory secondary education (CSE). A computational system is postulated, through data-mining techniques, capable of automatically classify reading texts by virtue of syntactic, lexical, semantics, and topological parameters regarding their overall content. The validity of this process is ensured through a careful selection by editorial experts used to accomplish the same validation task among different texts. In this paper, the results in the automatic classification of the degree of readability reached are promising; they allow to continue with a deeper investigation in this field of analysis with the purpose of progressing in the age rank studied, as well as in the text themes defining the corpus.
Multi-Agent Modelling of Fairness for Users and Miners in Blockchains

Due to the open and dynamic nature of blockchain systems, the participants (users and miners) have to take into accounts uncertain constraints (e.g., the transaction confirmation times, the delays in the network, and the topology of the network) during their decision-making processes for carefully balancing their objectives. One of these objectives is to operate in a fair environment. This is important since participants may decide to leave the system if they cannot satisfy this objective, which may imply reduced security and sustainability of the system. Yet, existing approaches to modelling fairness are based on formalisms that do not capture the open and complex nature of the blockchain systems. In this paper, we discuss the current status of modelling of fairness based on a high-level description of blockchains and we exploit multi-agent modelling of fairness for users and miners.
An annotation scheme for references to research artefacts in scientific publications

The extraction of mentions of research artefacts from scientific papers is a necessary precursor for multiple applications ranging from simple search for literature based on particular research artefacts to semantic analyses of the investigations described in the literature. Techniques of natural language processing like named entity and relation extraction allow to establish detailed knowledge about such artefacts. The application of supervised classifiers relies on annotated datasets in order to provide a basis for training and evaluation. In this work, we present an annotation scheme for research artefacts in scientific literature which not only distinguishes between different types of artefacts like datasets, software and materials but also allows for the annotation of more detailed information such as amount or concentration of materials. Furthermore, we present first preliminary results in terms of inter-rater reliability.
An evaluation of the role of fuzzy cognitive maps and Bayesian belief networks in the development of causal knowledge systems

Fuzzy cognitive maps (FCM) and Bayesian belief networks (BBN) are two of the most frequently used causal knowledge frameworks for modelling, representing and reasoning about causal knowledge. In this paper, an evaluation of their different roles in the engineering process of developing causal knowl edge systems is conducted, based on their inherent features. The evaluation criteria adopted in this research are understandability, usability, modularity, scalability, expressiveness, inferential capability, rigour, formality and preciseness. All of these are commonly used to evaluate the strengths and weaknesses of traditional knowledge representation frameworks. These criteria are used to reveal the fundamental characteristics of FCM and BBN. The findings of this study show that FCM is more appropriate for use in modelling causal knowledge, whereas BBN is more superior in model representation and inference. This study deepens the understanding of the role of FCM and BBN in the development of causal knowledge systems.
Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

Face attributes prediction has an increasing amount of applications in human–computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes. Multi-task learning architecture can build a synergy among the correlated tasks by parameter sharing in the shared layers. However, the dependencies between the tasks have been ignored in the task-specific layers of most multi-task learning architectures. Thus, how to further boost the performance of individual tasks by using task dependencies among face attributes is quite challenging. In this paper, we propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. The designed attention modules in task-specific layers of our proposed architecture are used for learning task-dependent disentangled representations. The experimental results demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on Faces of the world (FotW) and Labeled faces in the wild-a (LFWA) datasets.
Challenges of Data Mining Classification Techniques in Mammograms

Breast cancer is the growth of a malignant tumor in the breast. The incidence of this disease in women has increased significantly in recent years. Currently, early detection is an important factor in cancer treatment. The most effective method for early detection is mammography. The computer aided diagnosis (CAD) systems are essential to help searching for suspicious signs, or classifying lesions in benign or malignant types. In this paper, we want to discuss masses as one of the most important indicators of breast cancer. To classify and detect masses in mammograms, different data mining techniques have been used. On the other hand, the problem of classifying masses in mammogram images has placed many challenges for researchers. Before implementing a mass classification system, there is a need to be aware of the available problems and challenges in order to select the appropriate methods and classifiers. Hence, we first review the methods of mining and classifying masses in mammograms. Techniques that are commonly used can be categorized into four groups: Function based, probability based, similarity based and rule based techniques that are briefly discussed in this article. Then, we propose a categorization of existing challenges in the problem of mammographic mass classification. These challenges are divided into two parts: Data related and technique related challenges. Finally, the presented techniques are evaluated and analyzed according to the involved challenges.
Imbalanced Data Classification Method Based on Ensemble Learning

Imbalanced data classification is one of the problems that emerged when classifier learning algorithms used in the worlds of business and industry. This paper proposes the methodology to improve the performance of imbalanced data classification. We balance data sets by using synthetic minority oversampling technique (SMOTE); noise generated by new data sets is eliminated by Tomek links (T-Links), support vector machine (SVM), k-nearest neighbor (KNN), and logistic regression (LR) which are selected as the base classifiers to improve classification by using stacked generalization, and the final result is generated by weighted voting. In the experiments, six UCI datasets are tested, and the experimental results show that the method is highly representative and can effectively improve the classification ability.
Resilient Learning of Computational Models With Noisy Labels

The resistance of computational models against label noise offers promising potential for the correction of erroneous labels. One intuitive way is to re-label the data samples based on the model's prediction when the original label error rate is relatively high. However, directly flipping labels to the model's prediction may not improve the quality of datasets due to the concentration of noisy labels at small regions, i.e., increasing noise condensity, in the process of label flipping. Given the same label accuracy, datasets with condensed noise lead to (much) worse learning models than those without condensed noise. Hence, the quality of dataset may not benefit from this correction process. Moreover, iteratively flipping the label typically leads to the decreasing of label accuracy rather than a stabilized error rate around which the error rate slowly oscillates at each subsequent iteration. In this paper, we propose a novel method that simultaneously reduces the label error rate and improves the quality of datasets (via reduction of noise condensity). In contrast to the existing methods that either involve humans in the label correction process or construct multiple models to obtain a consensus opinion, our proposed method is simple and can automatically improve the quality of datasets. Specifically, we propose the use of a small clean dataset to evaluate overfitting caused by the concentration of label noise. Once the noise condensity issue is detected, the label flipping process will be modified by adding a statistical probability into the flipping procedure. The proposed method is verified on noisy MNIST and CIFAR-10 datasets. Label correction results are presented and the prediction accuracies of the neural network model trained on the corrected datasets are compared with results from the methods that target for learning from noisy labels.
Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network

In order to obtain the distances between the surrounding objects and the vehicle in the traffic scene in front of the vehicle, a monocular visual depth estimation method based on the depthwise separable convolutional neural network is proposed in this study. First, features containing shallow depth information were extracted from the RGB images using the convolution layers and maximum pooling layers. Subsampling operations were also performed on these images. Subsequently, features containing advanced depth information were extracted using a block based on an ensemble of convolution layers and a block based on depth separable convolution layers. The output from all different blocks is combined afterwards. Finally, transposed convolution layers were used for upsampling the feature maps to the same size with the original RGB image. During the upsampling process, skip connections were used to merge the features containing shallow depth information that was obtained from the convolution operation through the depthwise separable convolution layers. The depthwise separable convolution layers can provide more accurate depth information features for estimating the monocular visual depth. At the same time, they require reduced computational cost and fewer parameter numbers while providing a similar level (or slightly better) computing performance. Integrating multiple simple convolutions into a block not only increases the overall depth of the neural network but also enables a more accurate extraction of the advanced features in the neural network. Combining the output from multiple blocks can prevent the loss of features containing important depth information. The testing results show that the depthwise separable convolutional neural network provides a superior performance than the other monocular visual depth estimation methods. Therefore, applying depthwise separable convolution layers in the neural network is a more effective and accurate approach for estimating the visual depth.
Black-Box Optimization in an Extended Search Space for SAT Solving

The Divide-and-Conquer approach is often used to solve hard instances of the Boolean satisfiability problem (SAT). In particular, it implies splitting an original SAT instance into a series of simpler subproblems. If this split satisfies certain conditions, then it is possible to use a stochastic pseudo-Boolean black-box function to estimate the time required for solving an original SAT instance with the chosen decomposition. One can use black-box optimization methods to minimize the function over the space of all possible decompositions. In the present study, we make use of peculiar features which stem from the NP-completeness of the Boolean satisfiability problem to improve this general approach. In particular, we show that the search space over which the black-box function is minimized can be extended by adding solver parameters and the SAT encoding parameters into it. In the computational experiments, we use the SMAC algorithm to optimize such black-box functions for hard SAT instances encoding the problems of cryptanalysis of several stream ciphers. The results show that the proposed approach outperforms the competition.
Scheduling strategy for computational-intensive data flow in generalized cluster environments

A classical problem in the field of distributed computation and parallel processing concerns reasonable allocation of resources among computational-intensive data flows. We introduce the generalized cluster in this paper for processing large-scale scientific computations and to further explore a consecutive cooperation game-based dynamic scheduling strategy. We construct an abstract generalized cluster environment and summarize the types of data flows. We then convert the multi-objective scheduling problem into a multi-objective expectation function-based continuous cooperation game model and discuss its strategy and a solution for its kernel. We also propose a dynamic scheduling mechanism to address the instability of generalized clusters to ensure a reasonable, real-time adjusting allocation scheme by monitoring and compensating appropriately. Finally, we apply our method to a real world application to demonstrate our successful scheduling strategy that achieves superior results for overall cost, cost-performance index, and mean run time when compared with other methods.
A Case for Two-stage Inference with Knowledge Caching

Real-world intelligent services employing deep learning technology typically take a two-tier system architecture -- a dumb front-end device and smart back-end cloud servers. The front-end device simply forwards a human query while the back-end servers run a complex deep model to resolve the query and respond to the front-end device. While simple and effective, the current architecture not only increases the load at servers but also runs the risk of harming user privacy. In this paper, we present knowledge caching, which exploits the front-end device as a smart cache of a generalized deep model. The cache locally resolves a subset of popular or privacy-sensitive queries while it forwards the rest of them to back-end cloud servers. We discuss the feasibility of knowledge caching as well as technical challenges around deep model specialization and compression. We show our prototype two-stage inference system that populates a front-end cache with 10 voice commands out of 35 commands. We demonstrate that our specialization and compression techniques reduce the cached model size by 17.4x from the original model with 1.8x improvement on the inference accuracy.
On the Role of Linear, Mel and Inverse-Mel Filterbank in the Context of Automatic Speech Recognition

In the context of automatic speech recognition (ASR), the power spectrum is generally warped to the Mel-scale during front-end speech parameterization. This is motivated by the fact that, human perception of sound is nonlinear. The Mel-filterbank provide better resolution for low-frequency contents while a greater degree of averaging happens in the high-frequency range. The work presented in this paper aims at studying the role of linear, Mel and inverse-Mel filterbanks in the context of speech recognition. It is well known that, when speech data is from high-pitched speakers like children, there is a significant amount of relevant information in the high-frequency region. Hence, down-sampling the information in that range through Mel-filterbank reduces the recognition performance. On the other hand, employing inverse-Mel or linear-filterbanks are expected to be more effective in such cases. The same has been experimentally validated in this work. To do so, an ASR system is developed on adults' speech and tested using data from adult as well as child speakers. Significantly improved recognition rates are noted for children's as well adult females' speech when linear or inverse-Mel filterbank is used. The use of linear filters results in a relative improvement of 21% over the baseline.