Paper Group ANR 151
A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems. Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?. Deep Transfer Learning for Static Malware Classification. Unsupervised parameter selection for denoising with the elastic net. Experience, Imitation …
A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems
| Title | A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems |
| Authors | Maria-Yaneli Ameca-Alducin, Maryam Hasani-Shoreh, Wilson Blaikie, Frank Neumann, Efren Mezura-Montes |
| Abstract | Dynamic constrained optimization problems (DCOPs) have gained researchers attention in recent years because a vast majority of real world problems change over time. There are studies about the effect of constrained handling techniques in static optimization problems. However, there lacks any substantial study in the behavior of the most popular constraint handling techniques when dealing with DCOPs. In this paper we study the four most popular used constraint handling techniques and apply a simple Differential Evolution (DE) algorithm coupled with a change detection mechanism to observe the behavior of these techniques. These behaviors were analyzed using a common benchmark to determine which techniques are suitable for the most prevalent types of DCOPs. For the purpose of analysis, common measures in static environments were adapted to suit dynamic environments. While an overall superior technique could not be determined, certain techniques outperformed others in different aspects like rate of optimization or reliability of solutions. |
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| Published | 2018-02-16 |
| URL | http://arxiv.org/abs/1802.05825v1 |
| http://arxiv.org/pdf/1802.05825v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-comparison-of-constraint-handling |
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Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?
| Title | Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation? |
| Authors | Abhik Jana, Pawan Goyal |
| Abstract | Distributed representations of words learned from text have proved to be successful in various natural language processing tasks in recent times. While some methods represent words as vectors computed from text using predictive model (Word2vec) or dense count based model (GloVe), others attempt to represent these in a distributional thesaurus network structure where the neighborhood of a word is a set of words having adequate context overlap. Being motivated by recent surge of research in network embedding techniques (DeepWalk, LINE, node2vec etc.), we turn a distributional thesaurus network into dense word vectors and investigate the usefulness of distributional thesaurus embedding in improving overall word representation. This is the first attempt where we show that combining the proposed word representation obtained by distributional thesaurus embedding with the state-of-the-art word representations helps in improving the performance by a significant margin when evaluated against NLP tasks like word similarity and relatedness, synonym detection, analogy detection. Additionally, we show that even without using any handcrafted lexical resources we can come up with representations having comparable performance in the word similarity and relatedness tasks compared to the representations where a lexical resource has been used. |
| Tasks | Network Embedding |
| Published | 2018-02-17 |
| URL | http://arxiv.org/abs/1802.06196v1 |
| http://arxiv.org/pdf/1802.06196v1.pdf | |
| PWC | https://paperswithcode.com/paper/can-network-embedding-of-distributional |
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Deep Transfer Learning for Static Malware Classification
| Title | Deep Transfer Learning for Static Malware Classification |
| Authors | Li Chen |
| Abstract | We propose to apply deep transfer learning from computer vision to static malware classification. In the transfer learning scheme, we borrow knowledge from natural images or objects and apply to the target domain of static malware detection. As a result, training time of deep neural networks is accelerated while high classification performance is still maintained. We demonstrate the effectiveness of our approach on three experiments and show that our proposed method outperforms other classical machine learning methods measured in accuracy, false positive rate, true positive rate and $F_1$ score (in binary classification). We instrument an interpretation component to the algorithm and provide interpretable explanations to enhance security practitioners’ trust to the model. We further discuss a convex combination scheme of transfer learning and training from scratch for enhanced malware detection, and provide insights of the algorithmic interpretation of vision-based malware classification techniques. |
| Tasks | Malware Classification, Malware Detection, Transfer Learning |
| Published | 2018-12-18 |
| URL | http://arxiv.org/abs/1812.07606v1 |
| http://arxiv.org/pdf/1812.07606v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-transfer-learning-for-static-malware |
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Unsupervised parameter selection for denoising with the elastic net
| Title | Unsupervised parameter selection for denoising with the elastic net |
| Authors | Ernesto de Vito, Zeljko Kereta, Valeria Naumova |
| Abstract | Despite recent advances in regularisation theory, the issue of parameter selection still remains a challenge for most applications. In a recent work the framework of statistical learning was used to approximate the optimal Tikhonov regularisation parameter from noisy data. In this work, we improve their results and extend the analysis to the elastic net regularisation, providing explicit error bounds on the accuracy of the approximated parameter and the corresponding regularisation solution in a simplified case. Furthermore, in the general case we design a data-driven, automated algorithm for the computation of an approximate regularisation parameter. Our analysis combines statistical learning theory with insights from regularisation theory. We compare our approach with state-of-the-art parameter selection criteria and illustrate its superiority in terms of accuracy and computational time on simulated and real data sets. |
| Tasks | Denoising |
| Published | 2018-09-23 |
| URL | https://arxiv.org/abs/1809.08696v3 |
| https://arxiv.org/pdf/1809.08696v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-learning-theory-approach-to-a |
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Experience, Imitation and Reflection; Confucius’ Conjecture and Machine Learning
| Title | Experience, Imitation and Reflection; Confucius’ Conjecture and Machine Learning |
| Authors | Amir Ramezani Dooraki |
| Abstract | Artificial intelligence recently had a great advancements caused by the emergence of new processing power and machine learning methods. Having said that, the learning capability of artificial intelligence is still at its infancy comparing to the learning capability of human and many animals. Many of the current artificial intelligence applications can only operate in a very orchestrated, specific environments with an extensive training set that exactly describes the conditions that will occur during execution time. Having that in mind, and considering the several existing machine learning methods this question rises that ‘What are some of the best ways for a machine to learn?’ Regarding the learning methods of human, Confucius’ point of view is that they are by experience, imitation and reflection. This paper tries to explore and discuss regarding these three ways of learning and their implementations in machines by having a look at how they happen in minds. |
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| Published | 2018-08-01 |
| URL | http://arxiv.org/abs/1808.00222v1 |
| http://arxiv.org/pdf/1808.00222v1.pdf | |
| PWC | https://paperswithcode.com/paper/experience-imitation-and-reflection-confucius |
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Behavioral Malware Classification using Convolutional Recurrent Neural Networks
| Title | Behavioral Malware Classification using Convolutional Recurrent Neural Networks |
| Authors | Bander Alsulami, Spiros Mancoridis |
| Abstract | Behavioral malware detection aims to improve on the performance of static signature-based techniques used by anti-virus systems, which are less effective against modern polymorphic and metamorphic malware. Behavioral malware classification aims to go beyond the detection of malware by also identifying a malware’s family according to a naming scheme such as the ones used by anti-virus vendors. Behavioral malware classification techniques use run-time features, such as file system or network activities, to capture the behavioral characteristic of running processes. The increasing volume of malware samples, diversity of malware families, and the variety of naming schemes given to malware samples by anti-virus vendors present challenges to behavioral malware classifiers. We describe a behavioral classifier that uses a Convolutional Recurrent Neural Network and data from Microsoft Windows Prefetch files. We demonstrate the model’s improvement on the state-of-the-art using a large dataset of malware families and four major anti-virus vendor naming schemes. The model is effective in classifying malware samples that belong to common and rare malware families and can incrementally accommodate the introduction of new malware samples and families. |
| Tasks | Behavioral Malware Classification, Behavioral Malware Detection, Malware Classification, Malware Detection |
| Published | 2018-11-19 |
| URL | http://arxiv.org/abs/1811.07842v1 |
| http://arxiv.org/pdf/1811.07842v1.pdf | |
| PWC | https://paperswithcode.com/paper/behavioral-malware-classification-using |
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A Rank-Based Similarity Metric for Word Embeddings
| Title | A Rank-Based Similarity Metric for Word Embeddings |
| Authors | Enrico Santus, Hongmin Wang, Emmanuele Chersoni, Yue Zhang |
| Abstract | Word Embeddings have recently imposed themselves as a standard for representing word meaning in NLP. Semantic similarity between word pairs has become the most common evaluation benchmark for these representations, with vector cosine being typically used as the only similarity metric. In this paper, we report experiments with a rank-based metric for WE, which performs comparably to vector cosine in similarity estimation and outperforms it in the recently-introduced and challenging task of outlier detection, thus suggesting that rank-based measures can improve clustering quality. |
| Tasks | Outlier Detection, Semantic Similarity, Semantic Textual Similarity, Word Embeddings |
| Published | 2018-05-04 |
| URL | http://arxiv.org/abs/1805.01923v1 |
| http://arxiv.org/pdf/1805.01923v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-rank-based-similarity-metric-for-word |
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PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track
| Title | PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track |
| Authors | Takuya Akiba, Tommi Kerola, Yusuke Niitani, Toru Ogawa, Shotaro Sano, Shuji Suzuki |
| Abstract | We present a large-scale object detection system by team PFDet. Our system enables training with huge datasets using 512 GPUs, handles sparsely verified classes, and massive class imbalance. Using our method, we achieved 2nd place in the Google AI Open Images Object Detection Track 2018 on Kaggle. |
| Tasks | Object Detection |
| Published | 2018-09-04 |
| URL | http://arxiv.org/abs/1809.00778v1 |
| http://arxiv.org/pdf/1809.00778v1.pdf | |
| PWC | https://paperswithcode.com/paper/pfdet-2nd-place-solution-to-open-images |
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RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
| Title | RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses |
| Authors | Sean MacAvaney, Bart Desmet, Arman Cohan, Luca Soldaini, Andrew Yates, Ayah Zirikly, Nazli Goharian |
| Abstract | Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one’s mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging. |
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| Published | 2018-06-20 |
| URL | http://arxiv.org/abs/1806.07916v1 |
| http://arxiv.org/pdf/1806.07916v1.pdf | |
| PWC | https://paperswithcode.com/paper/rsdd-time-temporal-annotation-of-self |
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Context-Aware Sequence-to-Sequence Models for Conversational Systems
| Title | Context-Aware Sequence-to-Sequence Models for Conversational Systems |
| Authors | Silje Christensen, Simen Johnsrud, Massimiliano Ruocco, Heri Ramampiaro |
| Abstract | This work proposes a novel approach based on sequence-to-sequence (seq2seq) models for context-aware conversational systems. Exist- ing seq2seq models have been shown to be good for generating natural responses in a data-driven conversational system. However, they still lack mechanisms to incorporate previous conversation turns. We investigate RNN-based methods that efficiently integrate previous turns as a context for generating responses. Overall, our experimental results based on human judgment demonstrate the feasibility and effectiveness of the proposed approach. |
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| Published | 2018-05-22 |
| URL | http://arxiv.org/abs/1805.08455v1 |
| http://arxiv.org/pdf/1805.08455v1.pdf | |
| PWC | https://paperswithcode.com/paper/context-aware-sequence-to-sequence-models-for |
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One Formalization of Virtue Ethics via Learning
| Title | One Formalization of Virtue Ethics via Learning |
| Authors | Naveen Sundar Govindarajulu, Selmer Bringjsord, Rikhiya Ghosh |
| Abstract | Given that there exist many different formal and precise treatments of deontologi- cal and consequentialist ethics, we turn to virtue ethics and consider what could be a formalization of virtue ethics that makes it amenable to automation. We present an embroyonic formalization in a cognitive calculus (which subsumes a quantified first-order logic) that has been previously used to model robust ethical principles, in both the deontological and consequentialist traditions. |
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| Published | 2018-05-20 |
| URL | http://arxiv.org/abs/1805.07797v1 |
| http://arxiv.org/pdf/1805.07797v1.pdf | |
| PWC | https://paperswithcode.com/paper/one-formalization-of-virtue-ethics-via |
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Globalness Detection in Online Social Network
| Title | Globalness Detection in Online Social Network |
| Authors | Yu-Cheng Lin, Chun-Ming Lai, S. Felix Wu, George A. Barnett |
| Abstract | Classification problems have made significant progress due to the maturity of artificial intelligence (AI). However, differentiating items from categories without noticeable boundaries is still a huge challenge for machines – which is also crucial for machines to be intelligent. In order to study the fuzzy concept on classification, we define and propose a globalness detection with the four-stage operational flow. We then demonstrate our framework on Facebook public pages inter-like graph with their geo-location. Our prediction algorithm achieves high precision (89%) and recall (88%) of local pages. We evaluate the results on both states and countries level, finding that the global node ratios are relatively high in those states (NY, CA) having large and international cities. Several global nodes examples have also been shown and studied in this paper. It is our hope that our results unveil the perfect value from every classification problem and provide a better understanding of global and local nodes in Online Social Networks (OSNs). |
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| Published | 2018-12-18 |
| URL | http://arxiv.org/abs/1812.07135v1 |
| http://arxiv.org/pdf/1812.07135v1.pdf | |
| PWC | https://paperswithcode.com/paper/globalness-detection-in-online-social-network |
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A Generalized Representer Theorem for Hilbert Space - Valued Functions
| Title | A Generalized Representer Theorem for Hilbert Space - Valued Functions |
| Authors | Sanket Diwale, Colin Jones |
| Abstract | The necessary and sufficient conditions for existence of a generalized representer theorem are presented for learning Hilbert space-valued functions. Representer theorems involving explicit basis functions and Reproducing Kernels are a common occurrence in various machine learning algorithms like generalized least squares, support vector machines, Gaussian process regression and kernel based deep neural networks to name a few. Due to the more general structure of the underlying variational problems, the theory is also relevant to other application areas like optimal control, signal processing and decision making. We present the generalized representer as a unified view for supervised and semi-supervised learning methods, using the theory of linear operators and subspace valued maps. The implications of the theorem are presented with examples of multi input-multi output regression, kernel based deep neural networks, stochastic regression and sparsity learning problems as being special cases in this unified view. |
| Tasks | Decision Making |
| Published | 2018-09-19 |
| URL | http://arxiv.org/abs/1809.07347v1 |
| http://arxiv.org/pdf/1809.07347v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-generalized-representer-theorem-for-hilbert |
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UnibucKernel: A kernel-based learning method for complex word identification
| Title | UnibucKernel: A kernel-based learning method for complex word identification |
| Authors | Andrei M. Butnaru, Radu Tudor Ionescu |
| Abstract | In this paper, we present a kernel-based learning approach for the 2018 Complex Word Identification (CWI) Shared Task. Our approach is based on combining multiple low-level features, such as character n-grams, with high-level semantic features that are either automatically learned using word embeddings or extracted from a lexical knowledge base, namely WordNet. After feature extraction, we employ a kernel method for the learning phase. The feature matrix is first transformed into a normalized kernel matrix. For the binary classification task (simple versus complex), we employ Support Vector Machines. For the regression task, in which we have to predict the complexity level of a word (a word is more complex if it is labeled as complex by more annotators), we employ v-Support Vector Regression. We applied our approach only on the three English data sets containing documents from Wikipedia, WikiNews and News domains. Our best result during the competition was the third place on the English Wikipedia data set. However, in this paper, we also report better post-competition results. |
| Tasks | Complex Word Identification, Word Embeddings |
| Published | 2018-03-20 |
| URL | http://arxiv.org/abs/1803.07602v4 |
| http://arxiv.org/pdf/1803.07602v4.pdf | |
| PWC | https://paperswithcode.com/paper/unibuckernel-a-kernel-based-learning-method |
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Defending Against Universal Perturbations With Shared Adversarial Training
| Title | Defending Against Universal Perturbations With Shared Adversarial Training |
| Authors | Chaithanya Kumar Mummadi, Thomas Brox, Jan Hendrik Metzen |
| Abstract | Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space. While adversarial training improves the robustness of image classifiers against such adversarial perturbations, it leaves them sensitive to perturbations on a non-negligible fraction of the inputs. In this work, we show that adversarial training is more effective in preventing universal perturbations, where the same perturbation needs to fool a classifier on many inputs. Moreover, we investigate the trade-off between robustness against universal perturbations and performance on unperturbed data and propose an extension of adversarial training that handles this trade-off more gracefully. We present results for image classification and semantic segmentation to showcase that universal perturbations that fool a model hardened with adversarial training become clearly perceptible and show patterns of the target scene. |
| Tasks | Image Classification, Semantic Segmentation |
| Published | 2018-12-10 |
| URL | https://arxiv.org/abs/1812.03705v2 |
| https://arxiv.org/pdf/1812.03705v2.pdf | |
| PWC | https://paperswithcode.com/paper/defending-against-universal-perturbations |
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