Miracle-dz/UniFAR-FTUs huggingface dataset:
isAnon: Flow-Based Anonymity Network Traffic Identification Using Extreme Gradient Boosting

The abuse of anonymous communication technology brings serious challenges to network supervision. The valid identification of anonymity network traffic is a prerequisite and fundamentally important for preventing the violence of such techniques. However, due to the distinct characteristics of flow from anonymity networks including Tor, I2P, and JonDonym, existing studies don’t take full advantage of these features, damaging the accuracy of identification. In this paper, we propose an effective anonymity network traffic identification model, called isAnon. Firstly, isAnon designs a novel hybrid feature selection algorithm by combining Modified Mutual Information and Random Forest (MMIRF) algorithm to filter out some irrelevant and redundant features quickly. Secondly, our proposed model applies a nested cross-validation scheme with an inner 5-fold cross-validation and an outer Monte Carlo cross-validation to prevent model overfitting. Finally, we use the Extreme Gradient Boosting (XGBoost) algorithm to identify Tor, I2P, and JonDonym networks for four scenarios. Comprehensive experimental results on several real-world anonymity network traffic datasets clearly show the effectiveness of our isAnon model compared with state-of-the-art baseline identification methods.
A Novel Deep Learning-Based Encoder-Decoder Model for Remaining Useful Life Prediction

A novel encoder-decoder model based on deep neural networks is proposed for the prediction of remaining useful life (RUL) in this work. The proposed model consists of an encoder and a decoder. In the encoder, the Bi-directional Long Short-Term Memory Networks (Bi-LSTM) and Convolutional Neural Networks (CNN) are used to capture the long-term temporal dependencies and important local features from the sequential data, respectively. Besides, single 1*1 convolution filter in the last convolutional layer is used for dimensionality reduction. In the decoder, the fully connected networks are employed to decode the feature information to predict RUL. In addition, the proposed data-driven method can achieve end-to-end prediction, which does not need feature engineering. To evaluate the proposed model, experimental verification is carried out on a commonly used aero-engine C-MAPSS dataset. Compared with other state-of-the-art approaches on the same dataset, the effectiveness and superiority of the proposed framework are demonstrated. For example, the scoring function value of the second subset is reduced by up to 64.99% compared with the best existing result.
Building and inferring knowledge bases using biomedical text mining

Biomedical researchers have the overwhelming task of keeping abreast of the latest research. This is especially true in the field of personalized cancer medicine where knowledge from different areas such as clinical trials, preclinical studies, and basic science research needs to be combined. We propose that automated text mining methods should become a commonplace tool for researchers to help them locate relevant research, assimilate it quickly and collate for hypothesis generation. To move towards this goal, we focus on extracting relations from published abstracts and full-text papers. We first explore the use of co-occurrences in sentences and develop a method for inferring new co-occurrences that can be used for hypothesis generation. We next explore more advanced relation extraction methods by developing a supervised learning method, VERSE, which won part of the BioNLP 2016 Shared Task. Our classical method outperforms a deep learning method showing its applicability to text mining problems with limited training data. We develop it further into the Kindred Python package which integrates with other biomedical text mining resources and is easily applied to other biomedical problems. Finally, we examine the applicability of these methods in personalized cancer research. The specific role of genes in different cancer types as drivers, oncogenes, and tumor suppressors is essential information when interpreting an individual cancer genome. We built CancerMine, a high-quality knowledgebase, using the Kindred classifier and annotations from a team of annotators. This allows for quantifiable comparisons of different cancer types based on the importance of different genes. The clinical relevance of cancer mutations is generally locked in the raw text of literature and was the focus of the CIViCmine project. As a collaboration with the Clinical Interpretation of Variants in Cancer (CIViC) project team, we built methods to prioritise relevant papers for curation. Through this work, we have focussed on different ways to extract structured knowledge from individual sentences in biomedical publications. The methods, guidelines, and results developed will aid biomedical text mining research and the personalized cancer treatment community.
Intranet User-Level Security Traffic Management with Deep Reinforcement Learning

Insider threats gradually exert great influence in cy-bersecurity, causing a significant loss to organizations or companies. However, whatever the form of the threat is, insiders have to conduct the unauthorized activities through the communication traffic, such as controlling the victim systems and unauthorizedly requesting the resources. Moreover, as one of the most fundamental intranet resources, bandwidth is frequently targeted by insider attackers for sabotage to traffic communication and service delivery of the network. In this paper, we present a user-level full-lifecycle security management scheme for intranet traffic from anomaly detection to mitigation execution in an online manner. This scheme dynamically monitors abnormal users that deviate from normal behavior patterns through bidirectional Gated Recurrent Unit (bi-GRU) based online unsupervised log parser, then adaptively adjusts the traffic scheduling policy according to the adequate consideration of network security, network performance and user requirements by using deep Reinforcement Learning (RL) method for online decision-making. Extensive experimental evaluations show that our scheme can stably maintain the high performance of traffic scheduling and effectively mitigate multifarious traffic threats. Our work is a valuable step towards designing self-adaptive intranets that learn to enhance security management by themselves with high scalability and deployability.
Condensed Convolution Neural Network by Attention over Self-attention for Stance Detection in Twitter

In the era of Web 2.0, people have become accustomed to expressing their attitudes and exchanging opinions on social media sites such as Twitter. It is critical for security and business related applications to make sense of public opinions implied in users’ texts. Stance detection aims to classify the stances users hold towards certain targets as FAVOR, AGAINST or NONE. In the literature, many efforts have been paid on neural network based stance detection to avoid hand-crafted features. As a widely used neural network structure, convolutional neural network (CNN) can mine and combine various local textual features for classifying stances with high training efficiency. However, global textual information is usually neglected in the convolution process. Besides, stance clues are often mixed with less informative words in noisy tweets, and it is hard for CNN to resolve and leverage long-distance semantic dependencies between words effectively. To address these issues, in this paper, we propose CCNN-ASA, the Condensed CNN by Attention over Self-Attention, to detect the stances of tweets. We first introduce self-attention into CNN to adaptively enhance word embeddings with global textual information. To make stance clues close to each other and thus more salient, we further introduce attention-based condensation module which identifies stance-indicative words to condense tweets. Experiments on a benchmark dataset show that CCNN-ASA outperforms state-of-the-art methods in stance detection of tweets.
Investigation of Neural Networks Using Synapse Arrays Based on Gated Schottky Diodes

A synapse device array based on the gated Schottky diodes (GSDs) is fabricated. This GSD operates in reverse mode, so the synapse current is considerably low, helping to implement a low-power hardware-based neural networks (HNNs). The reverse Schottky diode current, which represents the synaptic weight, is modulated by applying program or erase pulses. In this GSD, the reverse diode current is used as the synapse current, but the forward diode current is cut off. This is an important feature to prevent the sneak path problem in the crossbar array. A synapse array consisting of fabricated 200 GSDs shows a variation (σ/μ) of 0.34, 0.22, and 0.14 for three different synaptic weight states. By using this GSD array, we perform the vector-by-matrix multiplication, and evaluate the inference accuracy of MNIST. As a baseline accuracy for MNIST classification, a convolutional neural network similar to Lenet-5 is designed and gives an accuracy of 99.53%. Normalization method is applied to the weights trained in the network to map the weights into the conductance range of synapse device. The adaptive weight quantization is then applied to the normalized weights. We verify that the HNN using GSDs works well in comparison to the baseline network even in the presence of non-ideal characteristics of synapse devices.
Analysing and Inferring of Intimacy Based on fNIRS Signals and Peripheral Physiological Signals

Intimacy refers to a relatively long-lasting affinity relationship between individuals, which involves complex neuronal activities and physiological changes in the body. Recent advancements in the field of neuroimaging have demonstrated that functional near-infrared spectroscopy (fNIRS) has excellent potential for intimate relationship analysis. Signals such as fNIRS and physiological signals are increasingly utilised in this regard due to their consistency and complementarity. In this paper, first, we apply fNIRS and physiological database collected from 26 subjects when viewing lover, friend and stranger pictures to analyse and infer the intimacy. Then, the time domain information from both the fNIRS and physiological signals are utilised to exploit the representation of intimacy by General Linear Model (GLM) and Complex Brain Network Analysis (CBNA) methods. Based on these two methods, the intimacy can be analysed with different brain activation patterns. Finally, different machine learning techniques are utilised to predict the intimate relationship. The results demonstrate that multi-modal features are more efficient for intimacy research. Moreover, the average classification accuracy of ensemble learning is 98.72% whereas for KNN it is 91.03%.
SE-GAN: A Swap Ensemble GAN Framework

In recent years, Generative adversial network(GAN) becomes prevalent in the research and was applied in various fields. Though GAN attracting great attention undoubtedly, it was still blamed for the difficulty of training. Training a GAN is very likely to get into undesirable results such as gradient vanishing, gradient explosion, model collapse. In order to train GAN more efficiently, this study proposes the Swap Ensemble Generative Adversial Network(SE-GAN), a framework for training an ensemble GAN. Based on the concept of information sharing, SE-GAN allows serveral GANs to be trained in parallel: one of them is the leader, while the others are the workers. The leader pair swaps with each worker periodically to collect the information of all workers. At the same time, each worker acquires the information the leader learned. Under such implementation, the leader and workers would help each other.In this paper, a toy dataset was used to describe the concept and better show the effect of swap ensemble framework. And three real-world datasets were used to validate the effect. Results of the experiments shows this framework improves the performance and efficiency in training GAN.
Recent Advances in Machine Learning-based Anomaly Detection for Industrial Control Networks

The convergence of operation technology (OT) and information and communication technology (ICT) in industrial control networks presents new challenges for anomaly detection algorithms. Machine learning has achieved great success over the past few years, which has also been considered as one of the popular solutions for anomaly detection in industrial control networks. In this paper, we survey the recent work on anomaly detection for industrial control networks exploiting machine learning techniques. We first summarize major differences between OT and ICT and analyze the corresponding advantages and disadvantages for anomaly detection. Based on the features of the industrial control networks, existing work are classified into data-based learning and industrial network-specified learning methods. We analyze the pros and cons of these two learning methods. We finally point out the promising future work within this research area.
A Cognitive Architecture for Socially Adaptable Robots

A social robot that's aware of our needs and continuously adapts its behaviour to them has the potential of creating a complex, personalized, human-like interaction of the kind we are used to have with our peers in our everyday lives. However adaptability, being a result of a process of learning and making errors, brings with itself also uncertainty, and we as humans are heavily relying on the machines we use to always be predictable and consistent. To further explore this, we propose a cognitive architecture for the humanoid robot iCub supporting adaptability and we attempt to validate its functionality and establish the potential benefits it could bring with respect to the more traditional pre-scripted interaction protocols for robots.
Analysis of Two Various Approaches for Attributes Classification Based on User-Submitted Photos

There are some challenges in processing big data, namely multilabel enormous size that the affect may the time and the computing nature of the multilabel which data further complicate the process. In a quest of exploring the may approaches to resolve such challenges, we experimented right with two big data classification approaches, which are different two-steps approach and the three-steps approach. The the two-steps approach focuses the classification of on of attributes individual restaurant as a basis for determining the images of a restaurant attributes calculating the score averages from of each labels. On the image hand, the three-steps other approach focuses the classification of restaurant attributes on based its photos’ features on scores. Such approaches average tested in order to find out the different outcomes. The were were conducted on a dataset, which size reaches classifications up to gigabytes, consisting of 13 user-submitted 234,841 restaurant from a crowdsourced photos reviews restaurant website. We that the approaches produced different found outcomes which have applicability when those different are intended be implemented in to crowdsourced review site. a the two-steps approach has lower F-1 score, Moreover, precision, and recall score than three-steps average approaches.
Locating Vulnerability in Binaries Using Deep Neural Networks

Binary fault localization is important for vulnerability analysis, but many current techniques face problems in locating vulnerability accurately and effectively, especially for real-world programs. In this paper, we propose a novel gradient-guided vulnerability locating method named DeepVL, which leverages deep neural networks to diagnose the root cause of weakness in binaries and provide guidance information for further analysis. DeepVL collects sufficient amounts of crashed execution traces and normal execution traces as input of the constructed neural networks. Based on trained neural network, DeepVL calculates the gradient information for each basic block in traces and filter out the vulnerable basic blocks according to corresponding gradients. To demonstrate the applicability of DeepVL, we perform plenty of experiments on different datasets. According to the experimental results on Common Weakness Enumeration (CWE) dataset, DeepVL could locate different types of vulnerabilities accurately and effectively, with $recall\text{@}10$ reaching 96.9% and $precision\text{@}10$ reaching 70.1%. Additionally, the results on Cyber Grand Challenge (CGC) program and LibTIFF 4.0. 10 show that DeepVL is capable of locating vulnerable basic blocks in large-scale programs. As a fault localization tool, DeepVL could greatly reduce the manual effort of finding vulnerabilities in binaries.
Identifying Reusable Early-Life Options

We introduce a method for identifying short-duration reusable motor behaviors, which we call early-life options, that allow robots to perform well even in the very early stages of their lives. This is important when agents need to operate in environments where the use of poor-performing policies (such as the random policies with which they are typically initialized) may be catastrophic. Our method augments the original action set of the agent with specially-constructed behaviors that maximize performance over a possibly infinite family of related motor tasks. These are akin to primitive reflexes in infant mammals—agents born with our early-life options, even if acting randomly, are capable of producing rudimentary behaviors comparable to those acquired by agents that actively optimize a policy for hundreds of thousands of steps. We also introduce three metrics for identifying useful early-life options and show that they result in behaviors that maximize both the option's expected return while minimizing the risk that executing the option will result in extremely poor performance. We evaluate our technique on three simulated robots tasked with learning to walk under different battery consumption constraints and show that even random policies over early-life options are already sufficient to allow for the agent to perform similarly to agents trained for hundreds of thousands of steps.
Cystoid Fluid Color Map Generation in Optical Coherence Tomography Images Using a Densely Connected Convolutional Neural Network

Optical Coherence Tomography (OCT) is a medical imaging modality that is currently the focus of many advancements in the field of ophthalmology. It is widely used to diagnose relevant diseases like Diabetic Macular Edema (DME) or Age-related Macular Degeneration (AMD), both among the principal causes of blindness. These diseases have in common the presence of pathological cystoid fluid accumulations inside the retinal layers that tear its tissues, hindering the correct vision of the patient.In the last years, several works proposed a variety of methodologies to obtain a precise segmentation of these fluid regions. However, many cystoid patterns present several difficulties that harden significantly the process. In particular, some of these cystoid bodies present diffuse limits, others are deformed by shadows, appear mixed with other tissues and other complex situations. To overcome these drawbacks, a regional analysis has been proven to be reliable in these problematic regions.In this work, we propose the use of the DenseNet architecture to perform this regional analysis instead of the classical machine learning approaches, and use it to represent the pathological identifications with an intuitive color map. We trained, validated and tested the DenseNet neural network with a dataset composed of 3247 samples labeled by an expert. They were extracted from 156 images taken with two of the principal OCT devices of the domain. Then, this network was used to generate the color map representations of the cystoid areas in the OCT images. Our proposal achieved robust results in these regions, with a satisfactory 97.48% ± 0.7611 mean test accuracy as well as a mean AUC of 0.9961 ± 0.0029.
Exploiting Action-Value Uncertainty to Drive Exploration in Reinforcement Learning

Most of the research in Reinforcement Learning (RL) focuses on balancing exploration and exploitation. Indeed, the reasons for the success or failure of an RL algorithm often deal with the choice between the execution of exploratory actions and the exploitation of actions that are known to be good. In the context of Multi-Armed Bandits (MABs), many algorithms have addressed this dilemma. In particular, Thompson Sampling (TS) is a solution that, besides having good theoretical properties, usually works very well in practice. Unfortunately, the success of TS in MAB problems has not been replicated in RL, where it has shown to scale very poorly w.r. t. the dimensionality of the problem. Nevertheless, the application of TS in RL, instead of more myopic strategies such as ε-greedy, remains a promising solution. This paper addresses such issue proposing several algorithms to use TS in RL and deep RL in a feasible way. We present these algorithms explaining the intuitions and theoretical considerations behind them and discussing their advantages and drawbacks. Furthermore, we provide an empirical evaluation on an increasingly complex set of RL problems, showing the benefit of TS w.r. t. other sampling strategies available in classical and more recent RL literature.
Parameter Reduction For Deep Neural Network Based Acoustic Models Using Sparsity Regularized Factorization Neurons

In this paper, we propose a deep neural network (DNN) model parameter reduction technique for an efficient acoustic model. One of the most common DNN model parameter reduction techniques is to use low-rank matrix approximation. Although it can reduce a significant number of model parameters, there are two problems to be considered; one is the performance degradation, and the other is the appropriate rank selection. To solve these problems, retraining is carried out, and so-called explained variance is used. However, retraining takes additional time, and explained variance is not directly related to classification performance.Therefore, to mitigate these problems, we propose an approach that performs model parameter reduction simultaneously during model training from the aspect of minimizing classification error. The proposed method uses the product of three factorized matrices instead of a dense weight matrix, and applies sparsity constraint to make entries of the center diagonal matrix zero. After finishing training, a parameter-reduced model can be obtained by discarding the left and right vectors corresponding to zero entries within the center diagonal matrix.
Probabilistic Programming and Big Data

The advent of the Internet in the late 1990s led to the increased flood of data termed Big Data. To derive meaningful value from big data, specialized tools and techniques are required. These tools and techniques are categorized under data management and data analysis. Under data analysis, predictive tools and techniques exists. These predictive tools and techniques use statistical models and machine learning algorithms to predict future events. Models are developed using probability theories such as Bayesian networks. However, development of probabilistic models require extreme technical expertise and it is a difficult task to model complex real-life situations. Thus, the emergence of probabilistic programming. The idea of probabilistic programming is new and its potential in AI and big data processing is important. This paper presents discussions on probabilistic reasoning and probabilistic programming with respect to big data. An investigation of the potential of probabilistic programming in big data is also presented by conducting a search through literature to find available big data solutions that use probabilistic programming. This search found one solution called InferSpark built on top of Apache Spark to process big data. This is an indication that more big data applications that uses the concept of probabilistic programming needs to be done.