Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Using Fuzzy-Deep Learning

Sentiment Analysis also termed as opinion mining is a classification process which is used to determine the polarity associated with user reviews in the form of text or images or speech. Due to the rapid growth in usage of social media such as forums, social networks, micro blogs etc., the need for sentiment analysis has simultaneously increased. Sentiment analysis can be helpful in improving marketing strategy for a product or customer services, providing information about in general public sentiment for a political party or candidate etc. Over the years, various techniques have been developed to provide user with better sentiment classification. These techniques have evolved from lexicon based to machine learning and now to deep learning. But there is an inherent uncertainty in natural language which could not be handled even by the most advanced deep learning techniques. Deep learning networks perform automatic feature extraction from given data. But, fuzzy logic helps us to deal with this uncertainty by providing us with decision making capabilities in the presence of ambiguity. Our aim is to improve sentiment analysis prediction for textual data by incorporating fuzziness with deep learning. So, in this paper we have combined the learning capabilities of deep learning and uncertainty handling abilities of fuzzy logic to provide more appropriate sentiment prediction to the user. We have used LSTM, a type of Recurrent Neural Network (RNN) for sentiment prediction. These networks have helped us to improve prediction accuracy as they are capable of dealing with long-term dependencies in the data.
Learning Local Forward Models on Unforgiving Games

This paper examines learning approaches for forward models based on local cell transition functions. We provide a formal definition of local forward models for which we propose two basic learning approaches. Our analysis is based on the game Sokoban, where a wrong action can lead to an unsolvable game state. Therefore, an accurate prediction of an action’s resulting state is necessary to avoid this scenario.In contrast to learning the complete state transition function, local forward models allow extracting multiple training examples from a single state transition. In this way, the Hash Set model, as well as the Decision Tree model, quickly learn to predict upcoming state transitions of both the training and the test set. Applying the model using a statistical forward planner showed that the best models can be used to satisfying degree even in cases in which the test levels have not yet been seen.Our evaluation includes an analysis of various local neighbourhood patterns and sizes to test the learners’ capabilities in case too few or too many attributes are extracted, of which the latter has shown do degrade the performance of the model learner.
Faster Homomorphic Encryption is not Enough: Improved Heuristic for Multiplicative Depth Minimization of Boolean Circuits

In somewhat homomorphic encryption schemes (e.g. B/FV, BGV) the size of ciphertexts and the execution performance of homomorphic operations depends heavily on the multiplicative depth. The multiplicative depth is the maximal number of consecutive multiplications for which the homomorphic encryption scheme was parameterized. In this work we improve a heuristic for multiplicative depth minimization of Boolean circuits found in the literature. In particular, a new circuit rewriting operator is introduced, the so called cone rewrite operator. The results we obtain using the new method are relevant in terms of accuracy and performance. The multiplicative depths for a benchmark of Boolean circuits is highly improved and the execution time of the new heuristic is significantly lower. The proposed rewrite operator and heuristic are not limited to Boolean circuits, but can also be used for arithmetic circuits.
Multi-Robot Cooperative Task Allocation With Definite Path-Conflict-Free Handling

Modeling and solving multi-robot task allocation with definite path-conflict-free handling is an important research, especially in real working environments.Some of the research lines are unable to obtain definite path-conflict-free solutions for multi-robot task allocations, such as using the penalty-term method in the fitness function to restrict the survival probabilities of the solutions with path conflicts.In some cases, these solutions are only able to satisfy the objective of minimizing task time.We formulate this problem based on grid maps, while focusing on the frequently used cooperative task allocation.In our model, two subtasks of each cooperative task must be executed by two robots, simultaneously.We propose vitality-driven genetic task allocation algorithm (VGTA), which is able to simultaneously minimize task time and realize definite conflict-free path planning.VGTA consists of local operators, such as random mutations, greedy crossovers, and vitality selection.Meanwhile, VGTA includes schedule conflict and path conflict handling strategies.In path conflict handling strategy, we not only consider the common path conflicts in a grid cell, but also focus on the path conflicts between robots when exchanging positions in the adjacent grid cells.Besides, we construct our benchmarks based on real working environments, such as factory, powerhouse, and airport environments.Experimental results indicate that VGTA's search capability and computation cost are satisfactory.Meanwhile, its solutions are able to be really executed.
LA-ICP-MS trace element analysis of arsenopyrite from the Samgwang gold deposit, South Korea, and its genetic implications

The Samgwang deposit, previously classified as an orogenic gold deposit, consists of several structurally controlled auriferous quartz veins hosted by metasedimentary rocks of the Precambrian Gyeonggi massif, South Korea. The gold at Samgwang occurs as electrum commonly associated with arsenopyrite. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), coupled with ore microscopy, was conducted on Au and trace elements distributed in arsenopyrite grains contained in quartz vein and wall rock samples. Resulting profiles for Au, Co, Ni, and Sb were relatively flat and showed little variability, indicating that the elements were relatively homogeneously distributed and exchanged via substitution in arsenopyrite, while variability and noisy depth profiles inferred that Pb was present as tiny inclusions or discrete particles. Furthermore, measured gold concentrations were typically higher in arsenopyrite grains from laminated quartz veins (avg. 2.07 ppm) than in grains from massive quartz veins (avg. 0.26 ppm) and wall rock samples (avg. 0.33 ppm). In the case of wall rock and massive quartz vein samples, arsenopyrite grains of euhedral shape illustrate an element distribution pattern whereby Au concentrations tend to decrease towards the rims of the analyzed grains. In contrast, the rims of arsenopyrite grains in laminated quartz vein samples recorded much higher Au concentrations of up to 20.1 ppm, with increasing Pb, Bi, and Ag, particularly within domains of fractured or recrystallized arsenopyrite grains. The newly acquired LA-ICP-MS data, along with textural evidence, may be interpreted in terms of the release and remobilization of invisible gold that was originally locked up in the arsenopyrite. This release is linked to late deformation and hydrothermal event during which the remobilized invisible gold was redeposited as electrum. In addition, there is evidence for the introduction of additional gold and other precious and base metals by the late-stage hydrothermal fluids. Based on the above and in contrast to previous genetic models, we propose a more complex metallogenic concept for Samgwang in which the economically important gold enrichment process was genetically related to intrusive activity during the waning stages of Jurassic to Early Cretaceous Daebo orogenesis.
Surrounding-aware correlation filter for UAV tracking with selective spatial regularization

The great advance of visual object tracking has provided unmanned aerial vehicle (UAV) with intriguing capability for various practical applications. With promising performance and efficiency, discriminative correlation filter-based trackers have drawn great attention and undergone remarkable progress. However, background interference and boundary effect remain two thorny problems. In this paper, a surrounding-aware tracker with selective spatial regularization (SASR) is presented. SASR tracker extracts surrounding samples according to the size and shape of the object in order to utilize context and maintain the integrality of the object. Additionally, a selective spatial regularizer is introduced to address boundary effect. Central coefficients in the filter are evenly regularized to preserve valid information from the object. While the others are penalized according to their spatial location. Under the framework of SASR tracker, surrounding information and selective spatial regularization prove to be complementary to each other, which actually did not draw much attention before. They managed to improve not only the robustness against various distractions in the surrounding but also the flexibility to catch up with frequent appearance change of the object. Qualitative evaluation and quantitative experiments on challenging UAV tracking sequences have shown that SASR tracker has performed favorably against 23 state-of-the-art trackers.
Deep Learning and One-class SVM based Anomalous Crowd Detection

Anomalous event detection in videos is an important and challenging task. This paper proposes a deep representation approach to the problem, which extracts and represents features in an unsupervised way. This algorithm can detect anomalous activity like standing statically and loitering among a crowd of people. Our proposed framework is a two-channel scheme by using feature channels extracted from the appearance and foreground of the original video. Two hybrid deep learning architectures SDAE-DBN-PSVM (a four-layer Stacked Denoising Auto-encoder with three-layer Deep Belief Nets and Plane-based one class SVM) are implemented for these two channels to learn the high-level feature representation automatically and produce two anomaly scores. Finally, a fusion scheme is proposed for combining anomaly scores and detecting anomalous events. Experimental results on a large real-world dataset (MCG) and two benchmark datasets (UCSD and Subway) demonstrate the effectiveness of this approach. Furthermore, quantitative analyses of the effects of the amount of training data and the illumination conditions of the video on the accuracy of anomaly detection are presented.
Assessing privacy and quality of synthetic health data

This paper builds on the results of the ESANN 2019 conference paper "Privacy Preserving Synthetic Health Data" [16], which develops metrics for assessing privacy and utility of synthetic data and models. The metrics laid out in the initial paper show that utility can still be achieved in synthetic data while maintaining both privacy of the model and the data being generated. Specifically, we focused on the success of the Wasserstein GAN method, renamed HealthGAN, in comparison to other data generating methods.
Adversarial Noise Attacks of Deep Learning Architectures: Stability Analysis via Sparse-Modeled Signals

Despite their impressive performance, deep convolutional neural networks (CNN) have been shown to be sensitive to small adversarial perturbations. These nuisances, which one can barely notice, are powerful enough to fool sophisticated and well performing classifiers, leading to ridiculous misclassification results. In this paper, we analyze the stability of state-of-the-art deep learning classification machines to adversarial perturbations, where we assume that the signals belong to the (possibly multilayer) sparse representation model. We start with convolutional sparsity and then proceed to its multilayered version, which is tightly connected to CNN. Our analysis links between the stability of the classification to noise and the underlying structure of the signal, quantified by the sparsity of its representation under a fixed dictionary. In addition, we offer similar stability theorems for two practical pursuit algorithms, which are posed as two different deep learning architectures—the layered thresholding and the layered basis pursuit. Our analysis establishes the better robustness of the later to adversarial attacks. We corroborate these theoretical results by numerical experiments on three datasets: MNIST, CIFAR-10 and CIFAR-100.
Multi-task Learning with Bidirectional Language Models for Text Classification

Multi-task learning is an effective approach to extract task-invariant features by leveraging potential information among related tasks, which improves the performance of a single task. Most existing work simply divides the whole model into shared and private spaces. Unfortunately, there is no explicit mechanism to prevent the two spaces from merging information from each other. As a result, the shared space may be mixed with task-specific features, while the private space may extract some task-invariant features. To alleviate the problem mentioned, in this paper, we propose a bidirectional language models based multi-task learning method for text classification. More specifically, we add language modelling as an auxiliary task to the private part, aiming to enhance its ability to extract task-specific features. In addition, to promote the shared part to learn common features, a loss constraint via uniform label distribution is introduced to the shared part. Finally, put task-specific features and taskinvariant features together in a weighted addition way to form the final representation, and it is then fed to the corresponding softmax layer. We do experiments on the FDU-MTL dataset which consists of 16 different text classification tasks. The experimental results show that our approach outperforms other typical methods.
Analysing causal relationships between delay factors in construction projects

Purpose Analyzing factors of delays in construction projects and determining their impact on project performance is necessary to better manage and control projects. Identification of root factors which may lead to project delay and increased cost is vital at the early or planning stage. Better identification of delay factors at the early stage can help the practitioners to reduce their impacts over the long run. Hence, the purpose of this paper is to propose an intelligent method to analyze causal relationships between delay factors in construction projects. The proposed approach is further validated by a real case study of the construction projects in West Azerbaijan province in Iran. Design/methodology/approach During the first phase, the fuzzy cognitive map (FCM) is drawn to indicate the causal relationships between the delay factors and the evaluation factors. For this purpose, the causal relationships between 20 delay factors and four evaluation factors are considered. Afterward, the effect of each factor on management goals is evaluated by using a hybrid learning algorithm. Delay factors are further prioritized by applying fuzzy data envelopment analysis (FDEA). In the second phase, an interpretive structural modeling (ISM) is employed to determine the root causes of delay factors. Findings Results of the first phase show that “supervision technical weaknesses for overcoming technical and executive workshop problems” and “Inaccurate estimation of workload, required equipment and project completion time” are the most significant delay factors. In contrary, “non-use of new engineering contracts” has the lowest impact on the management goals. Meanwhile, the results of the second phase conclude that factors like “Inaccurate estimation of workload, required equipment and project completion time” “weakness of laws and regulations related to job responsibilities” and “lack of foreseen of fines and encouragements in the contracts” are the most significant root factors of delay in construction projects. Originality/value This paper integrates three methods including FCM method, FDEA and ISM. In the first phase, FCM is drawn according to the experts’ opinions and concerning management goals and delay factors. Later, these factors are prioritized according to the results of running the algorithm and using the FDEA model. The second phase, the seven-step in the ISM methodology, is done to identify the root factors. To ensure that the root factors of the delay are at a lower level of hierarchical structure, delay factors are partitioned by drawing the ISM model.
Towards a Conceptual Framework Highlighting Mobile Learning Challenges

Over the last decade, there has been much interest in mobile technologies in teaching and learning as emerging and innovative tools. Despite this focus, mobile learning (m-Learning) implementation is facing many challenges. This study presents a tentative conceptual framework that consolidates existing research related to mobile learning implementation barriers. The study adopted a systematic review of the literature on challenges to mobile learning. A total of 125 papers published between 2007 and 2017 were extracted from established peer reviewed journals. A qualitative content analysis was used to define 24 barriers that have been grouped into four conceptual categories: Technological, Learner, Pedagogical and Facilitating Conditions. The proposed framework acts as guide for educators, systems developers, policy makers, researchers and stakeholders interested in implementing mobile learning programs.Request access from your librarian to read this article's full text.
Private Reader

Reading in public spaces can often be tricky if we wish to keep the contents away from the prying eye. We propose Private Reader, an eye-tracking approach towards maintaining privacy while reading by rendering only the portion of text that is gazed by the reader. We conducted a user study by evaluating for both the reader and observer in terms of privacy, reading comfort, and reading speed for three reading modes; normal, underscored, and scrambled text. "Scrambled" performs best in terms of perceived effort and frustration for the shoulder surfer. Our contribution is threefold; we developed a system to preserve privacy by rendering only the text at gaze-point of the reader, we conducted a user study to evaluate user preferences and subjective task load, and we suggested several scenarios where Private Reader is useful in public spaces.
Fuzzy Model Identification and Self Learning with Smooth Compositions

This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum values of the parameters on a smooth surface would facilitate further improvements in the application of other derivative based optimization control algorithms such as MPC or robust control algorithms to achieve a combined modeling-control scheme. Compared to the earlier works on the smooth fuzzy modeling structures, we could reach a desired trade-off between the model optimality and the computational load. The proposed method has been evaluated on a test problem as well as the non-linear dynamic of a chemical process.
Hybrid simulation system for testing artificial intelligence algorithms used in smart homes

Smart home automation systems allow the user to control and track lighting, security, ventilation and temperature systems more easily. While these systems provided easy control of the house depending on the commands only given by the user in the early period, today with artificial intelligence algorithms developed for energy saving, home security, control etc., these systems have begun to perform many operations autonomously. Many researchers and R&D companies work in this field and develop new algorithms every day to create new products. Artificial intelligence algorithms must be continuously tested and improved under different conditions in order to function properly and perform the desired function. Performing the algorithm tests in real environments involves many problems and reduces the efficiency in terms of studies. In similar situations in smart homes and different disciplines, algorithm development and improvement studies are performed in simulated environments. However, it may not be possible to include all the variables of real life in simulation environments. Considering all these situations, in this study, a hybrid smart home simulation that runs real and virtual smart homes together is developed to test the artificial intelligence algorithms developed for use in smart homes. A real smart home system is first developed and installed in a room for hybrid simulation. Then, a simulated house is developed with the desired number of rooms, desired number of smart home components with different tasks for this house, virtual individuals who use this house, and weekly life scenarios for these individuals. The room in which the real smart home system works is converted into a room in the virtual house by changing the simulation codes. Likewise, a real person who uses the real smart home becomes an inhabitant living in a virtual home. In addition, virtual inhabitants are created to use the real smart home system. Thus, the real and the virtual smart home and real and virtual inhabitants live together in a hybrid simulation. Finally, the hybrid simulation is operated smoothly under different conditions for two months with different artificial intelligence algorithms.
Cryptomining Cannot Change Its Spots: Detecting Covert Cryptomining Using Magnetic Side-Channel

With new cryptocurrencies being frequently introduced to the market, the demand for cryptomining - a fundamental operation associated with most of the cryptocurrencies - has initiated a new stream of earning financial gains. The cost associated with the lucrative cryptomining has driven general masses to unethically mine cryptocurrencies using “plundered” resources in the public organizations (e.g., universities) as well as in the corporate sector that follows Bring Your Own Device (BYOD) culture. Such exploitation of the resources causes financial detriment to the affected organizations, which often discover the abuse when the damage has already been done. In this paper, we present a novel approach that leverages magnetic side-channel to detect covert cryptomining. Our proposed approach works even when the examiner does not have login-access or root-privileges on the suspect device. It merely requires the physical proximity of the examiner and a magnetic sensor, which is often available on smartphones. The fundamental idea of our approach is to profile the magnetic field emission of a processor for the set of available mining algorithms. We built a complete implementation of our system using advanced machine learning techniques. In our experiments, we included all the cryptocurrencies supported by the top-10 mining pools, which collectively comprise the largest share (84% during Q3 2018) of the cryptomining market. Moreover, we tested our methodology primarily on two different laptops. By using the data recorded from the magnetometer of an ordinary smartphone, our classifier achieved an average precision of over 88% and an average F1 score of 87%. Apart from our primary goal - which is to identify covert cryptomining - we also performed four additional experiments to further evaluate our approach. We found that due to its underlying design, our system is future-ready and can readily adapt even to zero-day cryptocurrencies.