Miracle-dz/UniFAR-FTUs huggingface dataset:
Timing Patterns and Correlations in Spontaneous SCADA Traffic for Anomaly Detection

SCADA traffic is known to have stable traffic patterns with request-response communications triggered by a polling mechanism and believed to be suitable for anomaly detection. In such a communication mode, a SCADA master sends requests to the field devices and receives a response accordingly. However, modern SCADA protocols such as IEC-60780-5-104 also allow non-requested communications. In the non-requested communication mode, the field devices can initiate messages spontaneously. Due to the lack of periodicity and cyclic sequences caused by a polling mechanism, many anomaly detection methodologies designed for request-response communications cannot be applied to traffic generated by modern SCADA protocols. New approaches to model spontaneous traffic need to be explored.
Weakly Supervised Cross-lingual Semantic Relation Classification via Knowledge Distillation

Words in different languages rarely cover the exact same semantic space. This work characterizes differences in meaning between words across languages using semantic relations that have been used to relate the meaning of English words. However, because of translation ambiguity, semantic relations are not always preserved by translation. We introduce a cross-lingual relation classifier trained only with English examples and a bilingual dictionary. Our classifier relies on a novel attention-based distillation approach to account for translation ambiguity when transferring knowledge from English to cross-lingual settings. On new English-Chinese and English-Hindi test sets, the resulting models largely outperform baselines that more naively rely on bilingual embeddings or dictionaries for cross-lingual transfer, and approach the performance of fully supervised systems on English tasks.
Cross-Cultural Transfer Learning for Text Classification

Large training datasets are required to achieve competitive performance in most natural language tasks. The acquisition process for these datasets is labor intensive, expensive, and time consuming. This process is also prone to human errors. In this work, we show that cross-cultural differences can be harnessed for natural language text classification. We present a transfer-learning framework that leverages widely-available unaligned bilingual corpora for classification tasks, using no task-specific data. Our empirical evaluation on two tasks – formality classification and sarcasm detection – shows that the cross-cultural difference between German and American English, as manifested in product review text, can be applied to achieve good performance for formality classification, while the difference between Japanese and American English can be applied to achieve good performance for sarcasm detection – both without any task-specific labeled data.
Learning to Infer Implicit Surfaces without 3D Supervision

Recent advances in 3D deep learning have shown that it is possible to train highly effective deep models for 3D shape generation, directly from 2D images. This is particularly interesting since the availability of 3D models is still limited compared to the massive amount of accessible 2D images, which is invaluable for training. The representation of 3D surfaces itself is a key factor for the quality and resolution of the 3D output. While explicit representations, such as point clouds and voxels, can span a wide range of shape variations, their resolutions are often limited. Mesh-based representations are more efficient but are limited by their ability to handle varying topologies. Implicit surfaces, however, can robustly handle complex shapes, topologies, and also provide flexible resolution control. We address the fundamental problem of learning implicit surfaces for shape inference without the need of 3D supervision. Despite their advantages, it remains nontrivial to (1) formulate a differentiable connection between implicit surfaces and their 2D renderings, which is needed for image-based supervision; and (2) ensure precise geometric properties and control, such as local smoothness. In particular, sampling implicit surfaces densely is also known to be a computationally demanding and very slow operation. To this end, we propose a novel ray-based field probing technique for efficient image-to-field supervision, as well as a general geometric regularizer for implicit surfaces, which provides natural shape priors in unconstrained regions. We demonstrate the effectiveness of our framework on the task of single-view image-based 3D shape digitization and show how we outperform state-of-the-art techniques both quantitatively and qualitatively.
Towards Denial-of-Service Memory Vulnerabilities

We address the problem of verifying a program to be free of Denial-of-Service memory vulnerabilities. More specifically, we define a program to be safe from DoS attacks if its memory usage at any time during execution is linear to sizes of its inputs. We design an analysis algorithm that verifies if a program satisfies this definition, and reports code snippets in the program that may cause a nonlinear amount of memory usage in case the verification fails. We also formally prove the correctness of our algorithm w.r. t. the above definition. Our experimental results indicate that the analysis algorithm is both effective and efficient.
Reliable and Effectual Solitude Conformable Data Ownership in Cloud Computing

: Cloud computing is a paradigm which provide secure and pliable infrastructure enabling the data owners to store their data and they can access from cloud servers. This paradigm reduces storage and cost of maintenance. The user loses the physical control and ownership of data which leads to certainty risks. So to label these difficulty this work proposes a reliable and effectual solitude conformable data ownership in cloud computing. Further this scheme is extended to brace multiple owners’ data dynamics and batch verification.
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

Extreme multi-label text classification (XMTC) is an important problem in the era of {\it big data}, for tagging a given text with the most relevant multiple labels from an extremely large-scale label set. XMTC can be found in many applications, such as item categorization, web page tagging, and news annotation. Traditionally most methods used bag-of-words (BOW) as inputs, ignoring word context as well as deep semantic information. Recent attempts to overcome the problems of BOW by deep learning still suffer from 1) failing to capture the important subtext for each label and 2) lack of scalability against the huge number of labels. We propose a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: 1) a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label; and 2) a shallow and wide probabilistic label tree (PLT), which allows to handle millions of labels, especially for "tail labels". We empirically compared the performance of AttentionXML with those of eight state-of-the-art methods over six benchmark datasets, including Amazon-3M with around 3 million labels. AttentionXML outperformed all competing methods under all experimental settings. Experimental results also show that AttentionXML achieved the best performance against tail labels among label tree-based methods. The code and datasets are available at \url{http://github.com/yourh/AttentionXML} .
How the brain encodes meaning: Comparing word embedding and computer vision models to predict fMRI data during visual word recognition

The brain representational spaces of conceptual knowledge remain unclear. We addressed this question in a functional MRI study in which 27 participants were required to either read visual words or think about the concepts that words represented. To examine the properties of the semantic representations in the brain, we tested different encoding models based on word embeddings models -FastText (Bojanowski, Grave, Joulin, & Mikolov, 2017), GloVe (Pennington, Socher, & Manning, 2014), word2vec (Mikolov, Sutskever, Chen, Corrado, & Dean, 2013)-, and, image vision models -VGG19 (Simonyan & Zisserman, 2014), MobileNetV2 (Howard et al., 2017), DenseNet121 (Huang, Liu, Van Der Maaten, & Weinberger, 2017)fitted with the image referents of the words. These models were used to predict BOLD responses in putative substrates of the semantic network. We fitted and predicted the brain response using the feature representations extracted from the word embedding and computer vision models. Our results showed that computer vision models outperformed word embedding models in explaining brain responses during semantic processing tasks. Intriguingly, this pattern occurred independently of the task demand (reading vs thinking about the words). The results indicated that the abstract representations from the embedding layer of computer vision models provide a better semantic model of how the brain encodes word meaning. https://tinyurl.com/y5davcs6.
Non-Malleable Commitments using Goldreich-Levin List Decoding

We give the first construction of three-round non-malleable commitments from the almost minimal assumption of injective one-way functions. Combined with the lower bound of Pass (TCC 2013), our result is almost the best possible w.r. t. standard polynomial-time hardness assumptions (at least w.r.t. black-box reductions). Our results rely on a novel technique which we call 'bidirectional Goldreich-Levin extraction'. Along the way, we also obtain the first rewind secure delayed-input witness indistinguishable (WI) proofs from only injective one-way functions. We also obtain the first construction of an epsilon-extractable commitment scheme from injective one-way functions. We believe both of these to be of independent interest. In particular, as a direct corollary of our rewind secure WI construction, we are able to obtain a construction of 3-round promise zero-knowledge from only injective one-way functions.
Modeling in Neuroscience as a Decision Process

: Formal models play a crucial role in advancing knowledge about brain function. However, there are many incoherencies and questions typically asked in the field: what are good model goals? Which modeling tools are appropriate for a given question? When can we compare models? How can we know if model choices are appropriate and justified? This highlights some of the complexity of the modeling enterprise and suggests that modeling should be regarded as a high-dimensional decision process. Unfortunately, there is currently a lack of structure, principles and guidelines regarding this decision process. Here, we propose an organization of the problem, model and outcome spaces by describing modeling as a decision process that links model space to model utility via a crucial action space, i.e. model selection. We then provide decision heuristics and guidelines for best practices to help the modeler in the decision process. We hope this will help both modelers and reader better appreciate different models as an exercise of fruitful complementarity rather than a battle over what’s the “right” model.
Toolbox for the Reinforcement Learning Drift Diffusion Model

The continuous development of computational models drives understanding of cognitive mechanisms and their neurobiological underpinnings. Here we extend HDDM, an open source python toolbox for Bayesian hierarchical parameter estimation of the drift diffusion model, to also support reinforcement learning (RL). Moreover, our extension affords the ability to model instrumental learning paradigms in which the choice rule is replaced with the DDM (RLDDM), thus account for evolution of both choices and RT distributions with learning. RLDDM simultaneously estimates parameters of learning and dynamic decision processes by assuming decisions are made by accumulating evidence of the difference in expected rewards between choice options until reaching a decision threshold. Here we validate the model with a parameter recovery test and illustrate the usability of the toolbox, with posterior predictive checks, by fitting precollected data on an instrumental learning task.
Preventive Cardiology: A practical manual

BACKGROUND AND MULTIDISCIPLINARY FAMILY BASED CARDIOVASCULAR ASSESSMENT 1. Rationale for preventive cardiology programmes 2. The nurse-led multidisciplinary team 3. Identification and recruitment 4. Comprehensive multidisciplinary family based cardiovascular assessment APPLICATION OF THE ASSESSMENT FINDINGS TO MANAGE LIFESTYLE AND CARDIOVASCULAR RISK FACTORS 5. Changing lifestyles 6. Smoking cessation 7. Principles of dietary intervention 8. Physical activity and exercise 9. Principles of managing weight loss 10. Blood pressure, cholesterol and glucose 11. Cardioprotective drug therapies 12. Erectile dysfunction 13. The Health Promotion Workshop Programme 14. Data management, quality assurance and audit
A Fingerprint Image Enhancement Algorithm Based on Stretching Transfer Function

In this paper, a fingerprint image enhancement algorithm based on the stretching transfer function is proposed. The Otsu method is used to generate the threshold, which divides the fingerprint image grayscale roughly into two parts. The width of grayscale distribution can be controlled by adjusting the parameter of the stretching transfer function. The contrast of the input fingerprint image will be enhanced by the function, highlight image layers and make the ridge and valley lines clearer. The function could also retain the fingerprint image information in transformation process, thus lays a solid foundation for the accuracy and reliability of the fingerprint characteristic value extraction in the next step. The treatment effect of the algorithm is obviously better than that of histogram equalization algorithm; the superiority also lies in the smaller amount of calculation and the faster processing speed which effectively avoids manual intervention. It can be used for the occasion with limited computing resources and high real-time requirements. Besides, the ability to control the contrast of output image by only changing a single parameter provides users with regulations and choices in biometric.
Machine Translation for Machines: the Sentiment Classification Use Case

We propose a neural machine translation (NMT) approach that, instead of pursuing adequacy and fluency (“human-oriented” quality criteria), aims to generate translations that are best suited as input to a natural language processing component designed for a specific downstream task (a “machine-oriented” criterion). Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback. Experiments in sentiment classification of Twitter data in German and Italian show that feeding an English classifier with “machine-oriented” translations significantly improves its performance. Classification results outperform those obtained with translations produced by general-purpose NMT models as well as by an approach based on reinforcement learning. Moreover, our results on both languages approximate the classification accuracy computed on gold standard English tweets.
Multi-scale Feature Mapping Method Based on Clustering Convolution

In recent years, convolutional neural networks have attracted much attention due to their powerful image feature learning capabilities. However, in many cases, the network exhibits shortcomings such as difficulty in training and adjustment, and time-consuming training. This paper combines clustering and constrained Boltzmann machines to propose a multi-scale feature mapping method based on clustering convolution. Experiments were carried out on Cifar-10 and lung X-ray images. This method not only shortened the time but also improved the accuracy. Therefore, the proposed method is superior to the traditional feature extraction method.
Investigating TYPE constraint for frequent pattern mining

Abstract Frequent pattern mining is one of the hottest fields of research in data mining. It has proven quite valuable for a variety of pattern analyses. However, a major limitation of existing or traditional frequent pattern mining techniques is the extraction of a huge number of patterns, which complicates the analysis performed by the users. To reduce these overwhelming patterns; a refined pattern mining technique that extracts only useful and to-the-point patterns for the users, is required. Finding such patterns of user’s interest from large databases is a challenging research issue, which can be mitigated via constraint-based data mining. In this scenario, a pattern mining technique that incorporates specific interests (constraints) of the user could result in a substantial reduction in the number of patterns generated. In this paper, a similar technique has been proposed and investigated. In this regard, firstly all the well-defined and categorized constraints are surveyed. From the survey, it is concluded that an important constraint “type” is referred to in the literature but not categorized based on its characteristics. One of the contributions of this paper is categorization of this important constraint “type”. Further, in this paper, the “type” constraint is divided into four modes for the sake of characterization and algorithms to employ the “type” constraints are also proposed. Moreover, the amalgamated constraints are proposed and characterized. Finally; comparative study of the proposed work with the existing techniques is presented and it is found that the proposed technique is promising in terms of reduced computational complexity.
NOVEL LEARNING STRATEGY BASED ON GENETIC PROGRAMMING FOR CREDIT CARD FRAUD DETECTION IN BIG DATA

Due to the growing volume of monetary transactions on the internet, credit card fraud poses many challenging issues for banks and financial institutions, thus forcing them to continuously improve their fraud detection systems. However, current fraud detection techniques are far from accurate and fail to minimize the false alarm rates, which can cause inconvenience and dissatisfaction for customers in e-banking system. Furthermore, there are several factors that decrease the performance of Fraud Detection Systems (FDS), such as skewed distribution, concept drift, supports real time detection, large amount of data etc. In this paper, we address the highly imbalanced class issue faced by a FDS when working with big data and propose a novel method of generation of data set's minority class based on K-means clustering method and genetic algorithm to improve classification performance in credit card fraud detection. In our experiments, we apply the proposed approach to synthetic unbalanced credit card fraud data set to demonstrate its effectiveness by means of the most appropriate performance measures for fraud detection purposes.
An Energy-Efficient Sparse Deep-Neural-Network Learning Accelerator With Fine-Grained Mixed Precision of FP8–FP16

Recently, several hardware have been reported for deep-neural-network (DNN) acceleration, however, they focused on only inference rather than DNN learning that is crucial ingredient for user adaptation at the edge-device as well as transfer learning with domain-specific data. However, DNN learning requires much heavier floating-point (FP) computation and memory access than DNN inference, thus, dedicated DNN learning hardware is essential. In this letter, we present an energy-efficient DNN learning accelerator core supporting CNN and FC learning as well as inference with following three key features: 1) fine-grained mixed precision (FGMP); 2) compressed sparse DNN learning/inference; and 3) input load balancer. As a result, energy efficiency is improved <inline-formula> <tex-math notation="LaTeX">$1.76\times $ </tex-math></inline-formula> compared to sparse FP16 operation without any degradation of learning accuracy. The energy efficiency is <inline-formula> <tex-math notation="LaTeX">$4.9\times $ </tex-math></inline-formula> higher than NVIDIA V100 GPU and its normalized peak performance is <inline-formula> <tex-math notation="LaTeX">$3.47\times $ </tex-math></inline-formula> higher than previous DNN learning processor.
Curvilinear Distance Metric Learning

Distance Metric Learning aims to learn an appropriate metric that faithfully measures the distance between two data points. Traditional metric learning methods usually calculate the pairwise distance with fixed distance functions (\emph{e.g.,}\ Euclidean distance) in the projected feature spaces. However, they fail to learn the underlying geometries of the sample space, and thus cannot exactly predict the intrinsic distances between data points. To address this issue, we first reveal that the traditional linear distance metric is equivalent to the cumulative arc length between the data pair's nearest points on the learned straight measurer lines. After that, by extending such straight lines to general curved forms, we propose a Curvilinear Distance Metric Learning (CDML) method, which adaptively learns the nonlinear geometries of the training data. By virtue of Weierstrass theorem, the proposed CDML is equivalently parameterized with a 3-order tensor, and the optimization algorithm is designed to learn the tensor parameter. Theoretical analysis is derived to guarantee the effectiveness and soundness of CDML. Extensive experiments on the synthetic and real-world datasets validate the superiority of our method over the state-of-the-art metric learning models.
Time-Series Clustering in R Using the dtwclust Package

Most clustering strategies have not changed considerably since their initial definition. The common improvements are either related to the distance measure used to assess dissimilarity, or the function used to calculate prototypes. Time-series clustering is no exception, with the Dynamic Time Warping distance being particularly popular in that context. This distance is computationally expensive, so many related optimizations have been developed over the years. Since no single clustering algorithm can be said to perform best on all datasets, different strategies must be tested and compared, so a common infrastructure can be advantageous. In this manuscript, a general overview of shape-based time-series clustering is given, including many specifics related to Dynamic Time Warping and associated techniques. At the same time, a description of the dtwclust package for the R statistical software is provided, showcasing how it can be used to evaluate many different time-series clustering procedures.
A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing

We argue that external commonsense knowledge and linguistic constraints need to be incorporated into neural network models for mitigating data sparsity issues and further improving the performance of discourse parsing. Realizing that external knowledge and linguistic constraints may not always apply in understanding a particular context, we propose a regularization approach that tightly integrates these constraints with contexts for deriving word representations. Meanwhile, it balances attentions over contexts and constraints through adding a regularization term into the objective function. Experiments show that our knowledge regularization approach outperforms all previous systems on the benchmark dataset PDTB for discourse parsing.