Reviewerly/exHarmony huggingface dataset:
Multimedia Deep Learning

By achieving breakthrough results on domains such as speech recognition, natural language processing, and computer vision, it is no surprise that deep neural networks are receiving a lot of attention these days. Specifically in the field of multimedia data analysis, there is a tremendous amount of multimedia big data that is being generated every day. Deep learning has the potential to overcome the issue of multimedia data having massive and heterogeneous characteristics that make it a challenge to store and analyze the data. This can be accomplished by allowing computers to easily and automatically extract features from unstructured data without the need to rely on human intervention. Although recent multimedia deep learning methods have achieved some remarkable results, deep learning challenges such as interpretability and generalization still make it difficult to be fit for critical decision-making tasks from fields such as medicine and defense.
Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.
Smart solar tracking system based on fuzzy-Pi controller for maximizing the power of PV

Solar energy is a potential renewable energy to be developed as an alternative energy due to its abundant availability and environmentally friendly. Indonesia, as a tropical country has a very large intensity of solar irradiance and evenly distributed throughout region. However, the energy has not been optimally utilized because the existing technology today cannot significantly improve the efficiency of solar panels especially in terms of material technology. Nowaday, researchers have developed a technology capable of improving the performance of solar panels through solar tracking mechanisms. Solar tracker is a combination of electrical and mechanical systems that can move a solar panel to locate the position of the sun. The solar tracker system is expected to enhance the output power of solar panel (PV) because the intensity of solar radiation received is always maximum. Generally, solar tracking system has been operated on fix-based, so its usage has been limited to stationary objects. In this research has been proposed a smart solar tracking system-based PV panel on mobile object to investigate the enhanced power generated by PV. Meanwhile, this study implemented fuzzy logic system-based PI Controller. Fuzzy system used to tune desired PI gain in such a way that it will improve the performance of PI controller. There are two membership function (MF) schemes used, ie using 3 and 5 MF. The simulation results show that the best response of system was reached when fuzzy PI controller was 5 membership numbers with performance index parameters including the rise time (tr) 0.61 second, settling time (ts) 5.18 seconds, maximum overshoot 3.18% and steady state error (ess) 0.45%. The increased power generated by the PV panel based on smart solar tracking system placed on top of the car for a month is 52.37% as compared to fixed PV.
Automatic Translation of Spanish Natural Language Commands to Control Robot Comands Based on LSTM Neural Network

In this paper, we propose a high level layer able to translate motion commands in natural spanish language to a formal intermediate representation called Robot Control Language (RCL). The layer was built by using the seq2seq TensorFlow library, with a single forward LSTM for encoder and decoder respectively. We were able to achieve a 4.3e-08 loss employing a manually generated corpus in Spanish.
A CNN Model for Human Parsing Based on Capacity Optimization

Although a state-of-the-art performance has been achieved in pixel-specific tasks, such as saliency prediction and depth estimation, convolutional neural networks (CNNs) still perform unsatisfactorily in human parsing where semantic information of detailed regions needs to be perceived under the influences of variations in viewpoints, poses, and occlusions. In this paper, we propose to improve the robustness of human parsing modules by introducing a depth-estimation module. A novel scheme is proposed for the integration of a depth-estimation module and a human-parsing module. The robustness of the overall model is improved with the automatically obtained depth labels. As another major concern, the computational efficiency is also discussed. Our proposed human parsing module with 24 layers can achieve a similar performance as the baseline CNN model with over 100 layers. The number of parameters in the overall model is less than that in the baseline model. Furthermore, we propose to reduce the computational burden by replacing a conventional CNN layer with a stack of simplified sub-layers to further reduce the overall number of trainable parameters. Experimental results show that the integration of two modules contributes to the improvement of human parsing without additional human labeling. The proposed model outperforms the benchmark solutions and the capacity of our model is better matched to the complexity of the task.
A hybrid transfer learning algorithm incorporating TrSVM with GASEN

Traditional machine learning is generally committed to obtaining classifiers which are well-performed over unlabeled test data. This usually relies on two critical assumptions: firstly, sufficient labeled training data are available; secondly, training and testing data are drawn from the same distribution and the same feature space. Unfortunately, in most cases, the actual situation is difficult to meet the above conditions. Transfer learning scheme is naturally proposed to alleviate this problem. In order to get robust classifiers with relatively lower computational costs, we incorporate the rationale of Support Vector Machine (SVM) into transfer learning scheme and propose a novel SVM-based transfer learning model, abbreviated as TrSVM. In this method, support vector sets are extracted to represent the source domain. New training datasets are respectively constructed by combining each support vector set and target labeled dataset. On the basis of these training datasets, a number of new base classifiers can be acquired. Since performance of a classifiers ensemble is generally superior to that of individual classifiers, ensemble selection is utilized in our work. A hybrid transfer learning algorithm, integrating the Genetic Algorithm based Selective Ensemble (GASEN) with TrSVM, is proposed, and abbreviated as TrGASVM, naturally. GASEN is a genetic algorithm-based heuristic algorithm for solving combinatorial optimization problems. It can not only enhance the generalization ability of an ensemble, but also alleviate the local minimum problem of greedy ensemble pruning methods. Since TrGASVM is under frame of TrSVM and GASEN, it inevitably inherits the advantages of both algorithms. The reasonable incorporation of TrSVM with GASEN endows TrGASVM with favorable transfer learning capability, with its effectiveness being demonstrated by the experimental results on three real-world text classification datasets.
‘Intersemiotic translating’

Abstract Should transpositions between language and other sign systems be considered a kind of translation? The answer could be yes if the comparison is made to interlingual translating that features a high degree of variance. Here, however, the question will be whether there are any kinds of intersemiotic transposition that feature a high degree of in variance. Four criteria are suggested for defining invariance-oriented translation, and a variety of possible instances of invariant intersemiotic translation are considered, with special mention of transpositions to and from music.
Investigating agentive urban learning: an assembly of situated experiences for sustainable futures

In this article we explore the dynamic between the pedagogical and the urban, attending to ‘agentive urban learning’. By this we mean processes by which young people build agency in the urban context, in using the resources of the city to develop their own agency, and of developing agency to act within the city. By agency, we refer to the capacity to imagine and act to create individual and collective futures. Our interest is how young people develop such agentive urban learning themselves and how it might be enhanced pedagogically at school and university. Three case studies explore different facets—the first how young people themselves develop this agency in situated settings and the tools that they use to reflect upon the future; the second how digital tools might be used to enhance students’ understanding of the city as a site of change, in this instance, climate change; and the third how such agency might be developed collectively in partnership with other city dwellers. We conclude that a diversity of students’ engagement in urban contexts of learning offers ways from which to further investigate how identity, setting, and stakeholder relationships matter as part of potentially sustainable agentive learning futures.
Online Signature Verification Using Deep Learning and Feature Representation Using Legendre Polynomial Coefficients

Handwritten signing are one of the most popular behavioral biometrics. They are widely accepted for verification purposes, such as authenticating legal documents and financial contracts. In this paper, Legendre polynomials coefficients are used as features to model the signatures. The classifier used in this paper is deep feedforward neural network and the deep learning algorithm is stochastic gradient descent with momentum. The experimental results show better Equal Error Rate reduction and accuracy enhancement on SigComp2011 Dataset presented within ICDAR 2011 in comparison with state-of-the-art methods.
Affinity: A System for Latent User Similarity Comparison on Texting Data

In the field of social networking services, finding similar users based on profile data is common practice. Smartphones harbor sensor and personal context data that can be used for user profiling. Yet, one vast source of personal data, that is text messaging data, has hardly been studied for user profiling. We see three reasons for this: First, private text messaging data is not shared due to their intimate character. Second, the definition of an appropriate privacy-preserving similarity measure is nontrivial. Third, assessing the quality of a similarity measure on text messaging data representing a potentially infinite set of topics is non-trivial. In order to overcome these obstacles we propose affinity, a system that assesses the similarity between text messaging histories of users reliably and efficiently in a privacypreserving manner. Private texting data stays on user devices and data for comparison is compared in a latent format that neither allows to reconstruct the comparison words nor any original private plain text. We evaluate our approach by calculating similarities between Twitter histories of 60 US senators. The resulting similarity network reaches an average 85.0% accuracy on a political party classification task.
Skeleton-based automatic generation of Labanotation with neural networks

Labanotation is one of the most widely used notation systems and plays a powerful role in recording and archiving traditional folk dance. We propose an end-to-end method for generating Labanotation from motion capture data by identifying the movement of each part of the human body and by assigning corresponding Labanotation symbols. Our method is mainly highlighted in the following aspects: first, we design simple yet highly discriminative skeleton features that can accurately represent human movements; and second, for the recognition of upper limb movements, we adopt fast and efficient extreme-learning neural networks, and for the recognition of lower limb movements, we employ powerful long short-term memory networks. It is worth mentioning that this is the first time that neural networks have been applied to the field of Labanotation generation. Experimental results show that our approach achieves much better recognition accuracy than previous work.
An Analytical Model for Identifying Suspected Users on Twitter

With the use of identity resolution, both information leakage and identity hacking can be reduced to some extent. In this paper, a prototype has been developed to classify Twitter users as suspicious and nonsuspicious on the basis of features which identify user demographics and their tweeting activity using Twitter APIs. A model has been devised based upon user and tweet meta-data which is used to calculate user score and tweet score, and further aggregate the values generated by these scores to label suspicious and nonsuspicious users in the collected dataset of around 21,492 Twitter users. Further, support vector machine classifier has been used to classify the labeled data. Through this paper, our analysis about the role of features and the characteristics of dataset used for the categorization of users in Twitter has been reported. The experimental results illustrate that the proposed system can identify suspicious users with an accuracy of 94.1%.
On Calibration of Nested Dichotomies

Nested dichotomies (NDs) are used as a method of transforming a multiclass classification problem into a series of binary problems. A tree structure is induced that recursively splits the set of classes into subsets, and a binary classification model learns to discriminate between the two subsets of classes at each node. In this paper, we demonstrate that these NDs typically exhibit poor probability calibration, even when the binary base models are well-calibrated. We also show that this problem is exacerbated when the binary models are poorly calibrated. We discuss the effectiveness of different calibration strategies and show that accuracy and log-loss can be significantly improved by calibrating both the internal base models and the full ND structure, especially when the number of classes is high.
Jointly Sparse Convolutional Neural Networks in Dual Spatial-winograd Domains

We consider the optimization of deep convolutional neural networks (CNNs) such that they provide good performance while having reduced complexity if deployed on either conventional systems with spatial-domain convolution or lower-complexity systems designed for Winograd convolution. The proposed framework produces one compressed model whose convolutional filters can be made sparse either in the spatial domain or in the Winograd domain. Hence, the compressed model can be deployed universally on any platform, without need for re-training on the deployed platform. To get a better compression ratio, the sparse model is compressed in the spatial domain that has a fewer number of parameters. From our experiments, we obtain 24.2× and 47.7× compressed models for ResNet-18 and AlexNet trained on the ImageNet dataset, while their computational cost is also reduced by 4.5× and 5.1×, respectively.
DA-PMHT for Multistatic Passive Radar Multitarget Tracking in Dense Clutter Environment

Multistatic passive radar exploits illuminators of opportunity such as radio or television stations for tracking airborne targets.This paper focuses on broadcast signals in a single frequency network modulated according to the digital audio/video broadcasting (DAB/DVB) standards.The main challenge is that one has to solve the measurement-to-illuminator association ambiguity in addition to the conventional measurement-to-target association ambiguity, which increases data association complexity greatly.In this paper, to solve the additional measurement-to-illuminator data association ambiguity, a novel deterministic annealing probabilistic multi-hypothesis tracker based on the extended Kalman filter is proposed.The proposed algorithm works directly in the original three-dimensional Cartesian space, and can efficiently handle a large number of association hypotheses among the targets, measurements, and illuminators in a natural way, also it is computationally attractive.As the angular measurement is not available or often of extremely poor quality, this paper assumes measurements of bistatic range and range rate and does not process bearing information.The simulation results show that the proposed algorithm works reasonably well in dense clutter environment for multistatic passive radar system.
Attacks only Get Better: How to Break FF3 on Large Domains

We improve the attack of Durak and Vaudenay (CRYPTO'17) on NIST Format-Preserving Encryption standard FF3, reducing the running time from $$O(N^5)$$ to $$O(N^{17/6})$$ for domain $$\mathbb {Z}_N \times \mathbb {Z}_N$$ . Concretely, DV's attack needs about $$2^{50}$$ operations to recover encrypted 6-digit PINs, whereas ours only spends about $$2^{30}$$ operations. In realizing this goal, we provide a pedagogical example of how to use distinguishing attacks to speed up slide attacks. In addition, we improve the running time of DV's known-plaintext attack on 4-round Feistel of domain $$\mathbb {Z}_N \times \mathbb {Z}_N$$ from $$O(N^3)$$ time to just $$O(N^{5/3})$$ time. We also generalize our attacks to a general domain $$\mathbb {Z}_M \times \mathbb {Z}_N$$ , allowing one to recover encrypted SSNs using about $$2^{50}$$ operations. Finally, we provide some proof-of-concept implementations to empirically validate our results.
A multivariate statistical approach identifying the areas underlain by potential porphyry-style Cu mineralization, south-central British Columbia, Canada

The Bonaparte Lake area in southern part of the Quesnellia terrane of the Canadian Cordillera is proximal (<50 km) to several large porphyry CuAu and CuMo deposits. This area has potential for finding more porphyry type Cu deposits, but is overlain by a thick and nearly continuous cover of glacial sediments, which adds difficulty in finding mineral deposits in underlying bedrocks. This study uses geochemical data following an aqua regia leach on the clay fraction (<2 μm) of basal till (n = 726) to assess the mineral potential in the study area. Principal component analysis (PCA) captures four PCs containing large fractions of Cu variance: PC2 (31.2%), PC6 (17%), PC3 (16.8%) and PC16 (12.6%). Copper is associated with Au-Mo-Ag-As-Hg-Pb-Sb-Zn-Ni-Co-Mn in PC2; Au-Pb-Mn-W in PC6, Cr-V-Ti in PC3 and Pb-Se-Hg in PC16. The elemental assemblages in PC2, PC6 and PC16 may reflect the geochemical signature of underlying porphyry-style polymetallic mineralization, whereas the element association in PC3 may be originated from mafic volcanic rocks of the Chilcotin Group. Partitioning around medoids based on the dissimilarity matrix produced by unsupervised random forest using elements associated with Cu yields two groups, Group A and Group B, with minor overlap. Group A likely represents till samples with a composition influenced by a mineralized provenance because the contents of elements (Cu, Au, As, Mo, Pb and Zn) are much higher than samples in Group B. The Group A samples are mostly located in the areas underlain by the Nicola Group volcanic rocks and the northern boundary of the Thuya Batholith. Group A till samples are dominantly located close to or down-ice from known mineral occurrences, suggesting that more porphyry-Cu mineralization and other multi-elemental enrichments occur in these areas, such as Au, Mo, Pb and Zn. This study demonstrates the usefulness of multivariate analysis and unsupervised machine learning in detecting potential Cu mineralization in bedrocks based on the composition of basal till. The statistical methods used in this study may be applicable to large scale geochemical data of sediments to evaluate multi-element assemblages associated with a variety of mineralization in bedrocks.
Argumentation-based Intention Formation Process

In this work, we present an argumentation-based formalization for supporting the process of formation of intentions in practical agents. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more expressive and refined model than the BDI (Beliefs-Desires-Intentions) model. We focus on the progress of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use argumentation to support the transition of the goals from their initial state until the last one. Our proposal complies with both supporting relation properties defined by Castelfranchi and Paglieri, diachrony and synchrony. The former means that the support happens since the goal is a desire until it becomes an intention, and the latter that the support can be tracked, i.e. there is a memory of the cognitive path from the beginning of the process until the end. In this work, we present an argumentation-based formalization for supporting the process of formation of intentions in practical agents. This is based on the belief-based goal processing model proposed by Castelfranchi and Paglieri, which is a more expressive and refined model than the BDI (Beliefs-Desires-Intentions) model. We focus on the progress of goals since they are desires until they become intentions, including the conditions under which a goal can be cancelled. We use argumentation to support the transition of the goals from their initial state until the last one. Our proposal complies with both supporting relation properties defined by Castelfranchi and Paglieri, diachrony and synchrony. The former means that the support happens since the goal is a desire until it becomes an intention, and the latter that the support can be tracked, i.e. there is a memory of the cognitive path from the beginning of the process until the end.
Effort-Aware Tri-Training for Semi-supervised Just-in-Time Defect Prediction

In recent years, just-in-time (JIT) defect prediction has gained considerable interest as it enables developers to identify risky changes at check-in time. Previous studies tried to conduct research from both supervised and unsupervised perspectives. Since the label of change is hard to acquire, it would be more desirable for applications if a prediction model doesn't highly rely on the label information. However, the performance of the unsupervised models proposed by previous work isn't good in terms of precision and F1 due to the lack of supervised information. To overcome this weakness, we try to study the JIT defect prediction from the semi-supervised perspective, which only requires a few labeled data for training. In this paper, we propose an Effort-Aware Tri-Training (EATT) semi-supervised model for JIT defect prediction based on sample selection. We compare EATT with the state-of-the-art supervised and unsupervised models with respect to different labeled rates. The experimental results on six open-source projects demonstrate that EATT performs better than existing supervised and unsupervised models for effort-aware JIT defect prediction.