Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind Power Prediction Based on PSO-SVR and Grey Combination Model

As a kind of green, clean and renewable energy, wind power generation has been widely utilized in various countries in the world. With the rapid development of wind energy, it is also facing prominent problems. Because wind power generation is intermittent, unstable and stochastic, it has caused serious difficulties for power grid dispatch. At present, the important method to solve this problem is to predict wind speed and wind power. Grey model is suitable for uncertain systems with poor information and needs less operation data, so it can be used for wind speed and wind power prediction. However, the traditional grey system model has the disadvantage of low prediction accuracy. Therefore, firstly the GM (1,1) for wind speed prediction is improved by background value optimization in this paper. In order to comprehensively reveal the inherent uncertainty of wind speed random series, the fractional order grey system models with different orders are constructed. Secondly, in order to overcome the shortcoming of single grey model, each grey model is effectively united, and a combination prediction model based on neural network is presented. The two NWP outputs, i.e. ECMWF and GRAPES-MESO, have been added to the prediction model for reducing the uncertainty. The structure parameters of the neural network are optimized by trial and error. Thirdly, the support vector regression model is established to fit the scatter operation data of wind speed-power, and the parameters of the model are optimized by the particle swarm algorithm. Then the power prediction value is obtained by the fitted wind speed-power relationship and the corresponding result of the grey combination model for wind speed prediction. Finally, wind speed and wind power are predicted based on the actual operation data. In addition, the prediction model based on ARIMA is also constructed as a benchmark model. The results show that the proposed grey combination model improves the prediction accuracy.
TORINO Repository ISTITUZIONALE

—The extended Berkeley Packet Filter (eBPF) is a recent technology available in the Linux kernel that enables ﬂexible data processing. However, so far the eBPF was mainly used for monitoring tasks such as memory, CPU, page faults, trafﬁc, and more, with a few examples of traditional network services, e.g., that modify the data in transit. In fact, the creation of complex network functions that go beyond simple proof-of-concept data plane applications has proven to be challenging due to the several limitations of this technology, but at the same time very promising due to some characteristics (e.g., dynamic recompilation of the source code) that are not available elsewhere. Based on our experience, this paper presents the most promising characteristics of this technology and the main encountered limitations, and we envision some solutions that can mitigate the latter. We also summarize the most important lessons learned while exploiting eBPF to create complex network functions and, ﬁnally, we provide a quantitative characterization of the most signiﬁcant aspects of this technology.
Neuro-Robotic Haptic Object Classification by Active Exploration on a Novel Dataset

We present an embodied neural model for haptic object classification by active haptic exploration with the humanoid robot NICO. When NICO’s newly developed robotic hand closes around an object, multiple sensory readings from a tactile fingertip sensor, motor positions, and motor currents are recorded. We created a haptic dataset with 83200 haptic measurements, based on 100 samples of each of 16 different objects, every sample containing 52 measurements. First, we provide an analysis of neural classification models with regard to isolated haptic sensory channels for object classification. Based on this, we develop a series of neural models (MLP, CNN, LSTM) that integrate the haptic sensory channels to classify explored objects. As an initial baseline, our best model achieves a 66.6% classification accuracy over 16 objects. We show that this result is due to the ability of the network to integrate the haptic data both over time domain and over different haptic sensory channels. Furthermore, we make the dataset publically available to address the issue of sparse haptic datasets for machine learning research.
Coral Classification Using DenseNet and Cross-modality Transfer Learning

Coral classification is a challenging task due to the complex morphology and ambiguous boundaries of corals. This paper investigates the benefits of Densely connected convolutional network (DenseNet) and multi-modal image translation techniques in boosting image classification performance by synthesizing missing fluorescence information. To this end, an imageconditional Generative Adversarial Network (GAN) based image translator is trained to model the relationship between reflectance and fluorescence images. Through this image translator, fluorescence images can be generated from the available reflectance images to provide complementary information. During the classification phase, reflectance and translated fluorescence images are combined to obtain more discriminative representations and produce improved classification performance. We present results on the EFC and MLC datasets and report state-of-the-art coral classification performance.
Gated Sequential Recommendation with Dynamic Memory Network

Recommender systems provide users with a ranked list of items based on users’ historical behavior. Existing methods usually embed users with their temporal dynamics, which may have lost their long-term, intrinsic preferences. RNN is widely adopted to model users’ behaviors for its expressiveness on modeling sequential information. However, RNN-based methods are insufficient to capture users’ intrinsic preferences due to short-term dependency issues. In this paper, we aim to make progress from two perspectives: 1. extract users’ intrinsic preferences from their historical behaviors more effectively and robustly; 2. explore a fine-tuning way to combine users’ intrinsic preferences and external dynamic interests. To do so, we propose a modification of Dynamic Memory Network with gating mechanism for generating a dynamic, precise representation for individual users. We also design an automatic and implicit method, and it utilizes the intrinsic preferences as a trigger signal to search the input sequences and retrieves the temporal dynamics of users in the memory module. As a result, a desirable combination of temporal dynamics and intrinsic preferences is achieved. Extensive experiments on three benchmark datasets have shown our model’s superiority over the existing baseline method. Further analysis of the attention weights of each hop demonstrated the effectiveness of the proposed method.
Integrating Dual User Network Embedding with Matrix Factorization for Social Recommender Systems∗

To address the data sparsity problem faced by recommender systems, social network among users is often utilized to complement rating data for improving the recommendation performance. One of current trends is to combine the idea of matrix factorization (MF) for predicting ratings with the idea of graph embedding (GE) for analyzing social network towards recommendation tasks. Despite enjoying many advantages, the existing integrated models have two critical limitations. First, such models are designed to work with either explicit or implicit social network, but little is known in taking both into account. Second, the users’ embeddings learned by GE are fed to the downstream MF, but not reverse, which is sub-optimal because rating information is not considered for learning the users’ embeddings. In this paper, we propose a novel social recommendation algorithm which exploits both explicit and implicit social networks towards the task of rating prediction. In Particular, we seamlessly integrate MF model and GE model within a unified optimization framework, in which MF and GE tasks can be reinforced each other during the learning process. Our encouraging experimental results on three real-world benchmarks validate the superiority of the proposed approach to state-of-the-art social recommendation methods.
JSAC: A Novel Framework to Detect Malicious JavaScript via CNNs over AST and CFG

JavaScript (JS) is a dominant programming language in web/mobile development, while it is also notoriously abused by attackers due to its powerful characteristics, e.g., dynamic, prototype-based and multi-paradigm, which foil most static and dynamic analysis approaches. To detect malicious JS instances, several machine learning-based methods have been developed recently. However, these methods took JS as a natural language instead of a programming one, which can not capture its syntactic and semantic features.In this paper, we present JSAC, a novel framework to detect JS malware. It combines deep learning and program analysis techniques to capture the syntactic and semantic features of JS programs. Specifically, to get a JS program’s syntactic information, we build its abstract syntax tree and employ a tree-based convolutional neural network (CNN) to extract features from it. To get its semantic information, we construct its control flow graph and feed it to another graph-based CNN. Last, the features extracted from two CNNs are fused for final detection. Evaluation on a corpus of 69,523 JS files indicates that JSAC outperforms 4 other models with 98.73% F1-score in detecting JS malware.
Assessment of Spear Phishing User Experience and Awareness: An Evaluation Framework Model of Spear Phishing Exposure Level (SPEL) in the Namibian Financial Industry

Social engineering has become a major threat to organisations’ IT systems. Users are normally the weakest links in security chains and they put their organisations at risk of internet attacks through various social engineering tricks implemented by online criminals. One of the thriving electronic social engineering attacks that is increasingly targeting electronic banking systems and users is spear phishing attack. Consequently, this research assessed user experience and awareness by evaluating users’ abilities to detect if a specific email is spear phishing. Moreover, the research proposes an evaluation framework for an effective assessment of the organisations’ exposure level to spear phishing threat. In the SPEL evaluation framework, two information security frameworks (ISO27001:2013) and (NIST SP 800 -53) were applied to identify threat vital signs within the organisation, whereas the Protection Motivation Theory (PMT) theory was used in the identification of the user vulnerability signs.
From Face Recognition to Facial Pareidolia: Analysing Hidden Neuron Activations in CNNs for Cross-Depiction Recognition

The imagination of non-existent faces in random patterns, clouds and rock formations is known as facial pareidolia. We show that facial pareidolia also occurs naturally in a standard Convolutional Neural Network (CNN) trained on face recognition. For achieving this we propose a new method to analyse CNNs that combines feature visualisation and dimensionality reduction methods to cluster the hidden neuron activations in convolutional layers into groups with discriminative roles. The main contributions of the present paper are 1. ) an approach that uses a CNN trained on human face detection for facial pareidolia simulation without any additional training on a target image set of abstract facial patterns and 2. ) a novel way of improving the generalisation capacity of a CNN for cross-depiction recognition and domain adaptation scenarios using features learned by hidden neurons.
Deep Learning Of P73 Biomarker Expression In Rectal Cancer Patients

By applying deep learning, we were able to compare p73 protein expression patterns of different tissue types including normal mucosa, primary tumor and lymph node metastasis in rectal cancer patients using immunohistochemical slides. The pair-wise pattern comparisons were automatedly carried out by considering color, edge, blobs, and other morphological information in the images. We discovered that when the pattern dissimilarity between primary tumor and lymph node metastasis is relatively low among other tissue pairs (primary tumor and distant normal, biopsy and distant normal, biopsy and primary tumor, biopsy and primary tumor, lymph node metastasis and distant normal, lymph node metastasis and biopsy), there was an implication of short-time survival. This original result suggests a novel application of advanced artificial intelligence in machine learning for clinical finding in rectal cancer and encourages relevant study of multiple biomarker expressions in cancer patients.
Investigation into the Antecedents of Autonomous Car Acceptance using an Enhanced UTAUT Model

This paper investigates the intention of South African drivers to use Autonomous Vehicles (AVs), vehicles where the full-time performance can be handled by an automated driving system without or with minimal human input. The behaviour intention of people to use such vehicles is of interest due to questions of trust, safety, performance, and enjoyment. Little to no research of this kind had been done in South Africa before, and the field of study is still relatively new globally. An appropriately extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) was used as the basis for this research. A survey was completed by 121 respondents. Hedonic Motivation (β = −.19, p < .05) and Performance Expectancy (β = −.053, p < .05) were the only statistically significant predictors of Behavioural Intentions to use Autonomous Vehicles. However, Trust in Safety and Social Influence also exhibit strong correlations. The results validate the findings of similar studies.
Stable Network Morphism

Deep neural networks perform better when they are deeper. Network morphism is one of the paradigms to construct deeper neural networks. It makes developing deeper neural networks building on existing ones possible by morphing a well-trained neural network into a new one with the network function completely preserved. The morphed network also has the potential to continue growing into a more powerful one as it has more parameters. Existing network morphism schemes include Net2Net and NetMorph. However, both of them suffer from significant initial performance drop when the morphed network is continually trained. Such unstability is very much undesired for a continual learning system. In this research, we first identify the reason for the unstability, which is due to the large amount of zeros padded into the parameters. Based on this observation, we propose an algorithm based on modified gradient descent to decompose the network morphism equation. As a result, the morphed parameters are all non-zeros and the continual training process become stable. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed stable network morphism scheme.
MIDS: End-to-End Personalized Response Generation in Untrimmed Multi-Role Dialogue*

Multi-role dialogue is a challenging issue in nature language process (NLP), which needs not only to understand the sentences, but also to simulate the interaction among roles. However, existing methods treat all roles’ speeches as one sequence and assume that only two speakers take turn to talk, which blurs the characteristics of roles and rarely happen in daily life. To address these issues, we propose a Multi-role Interposition Dialogue System (MIDS) which generates reasonable responses based on dialogue context and next speaker prediction. MIDS employs multiple role-defined encoders to understand each speaker, and an independent sequence model to predict the next speaker. The independent sequence model also works as a scheduler to integrate encoders with weights. Then, an attention-enhanced decoder generates responses based on dialogue context, speaker prediction and integrated encoders. Moreover, with the help of the unique speaker prediction, MIDS is able to generate diverse responses and join conversation actively when appropriate. Experimental results demonstrate that MIDS significantly improves the accuracy of speaker prediction and reduces the perplexity of generation over baselines. Furthermore, MIDS is able to interact with users without cue during real-life online conversations. This work marks a first step towards simulating multi-role dialogue generation.
Multiple Fake Classes GAN for Data Augmentation in Face Image Dataset

Class-imbalanced datasets often contain one or more class that are under-represented in a dataset. In such a situation, learning algorithms are often biased toward the majority class instances. Therefore, some modification to the learning algorithm or the data itself is required before attempting a classification task. Data augmentation is one common approach used to improve the presence of the minority class instances and rebalance the dataset. However, simple augmentation techniques such as applying some affine transformation to the data, may not be sufficient in extreme cases, and often do not capture the variance present in the dataset. In this paper, we propose a new approach to generate more samples from minority class instances based on Generative Adversarial Neural Networks (GAN). We introduce a new Multiple Fake Class Generative Adversarial Networks (MFC-GAN) and generate additional samples to rebalance the dataset. We show that by introducing multiple fake class and oversampling, the model can generate the required minority samples. We evaluate our model on face generation task from attributes using a reduced number of samples in the minority class. Results obtained showed that MFC-GAN produces plausible minority samples that improve the classification performance compared with state-of-the-art AC-GAN generated samples.
A Multi-Application, Scalable and Adaptable Hardware SOM Architecture

In this work, a scalable and adaptable hardware SOM architecture allowing to execute multiple applications in parallel is presented. The proposed architecture allows to use simultaneously multiple SOM structures with different parameters in order to satisfy multiple applications with different needs. The application switching is done within a clock cycle at the neuron’s level at run time only by analyzing the received input data. The proposed architecture was tested and validated in an image quantization experiment where 6 quantization applications with different parameters (from 6 × 6 to 15 × 15 SOMs with inputs varying from 3 to 12 elements) were performed simultaneously.
Distributed and Randomized Tensor Train Decomposition for Feature Extraction

Matrix factorizations and their multi-linear extensions, known as tensor factorizations are widely known and useful methods in data analysis and machine learning for feature extraction and dimensionality reduction. Recently, new approaches to factorization models appeared – tensor network (TN) factorizations. They reduce storage, computational complexity, and aim to help with curse of dimensionality in decomposing multi-way data. Tensor train (TT) is one of the most popular TN models used in wide-range areas, such as quantum physics or chemistry. In this study, we improved TTs for classification tasks by combining the fundamental TT model with randomized decompositions and extending it to a distributed version according to the MapReduce paradigm. As a result, the proposed approach is not only scalable but also much faster than competing algorithms, and is able to perform large-scale dimensionality reduction, e.g. in classification tasks.
Efficient and Scalable Exploration via Estimation-Error

Exploring efficiently in complex environments is still a challenging problem in reinforcement learning. Recent exploration algorithms based on "optimism in the face of uncertainty" or intrinsic motivation achieved promising performance in sparse reward settings, but they often rely on additional structures which are hard to build in large scale problems. It renders them impractical and hinders the process of combining with reinforcement learning algorithms. Hence, the most state-of-the-art RL algorithms still use the naive action space noise as exploration strategy. In this paper, we model the uncertainty about environment through agent’s ability to estimate the value across state and action space. Then, we parameterize the uncertainty by a neural network and regard it as a reward bonus signal to reward uncertain states. In this way, we generate an end-to-end bonus which can scale to complex environments with less computational cost. In order to prove the effectiveness of our method, we evaluate it on the challenging Atari 2600 games. We observed that our method achieves superior or comparable exploratory performance compared to action space noise in all environments, including environments whose rewards are sparse. The results demonstrate that our exploration method can motivate agent to explore effectively even in complex environments and it generally outperforms the naive action space noise.
Risk Prediction for Imbalanced Data in Cyber Security : A Siamese Network-based Deep Learning Classification Framework

Risk prediction plays an important role in network security which can be used to predict riskiest parts and then proactive measures can be adopted to avoid potential damage. Most existing literature model risk prediction problems as binary classification problems by using machine learning methods. However, these traditional machine learning models have poor performance – tending to misclassify the risky ones into the category of risk-free – on risk prediction task when the datasets are imbalanced or small in size. In this paper, we propose a Siamese Network Classification Framework (SNCF) that can map the Siamese network to a classification based on the similarity to alleviate imbalance for risk prediction. Experimental results on imbalanced data in risk prediction verify that the deep learning-based classification architecture SNCF has better efficiency when compared with other algorithms.
Towards a Faster Incremental Packrat Parser

This work is an extension to the research done by Dubroy and Warth [1] on incremental packrat parsing. It attempts to further decrease the parse time of the incremental parser using two techniques from experimental algorithmics: algorithm and code tuning. Two modifications were introduced to the original incremental packrat parser: reduction of copy operations, and the use of an array of overlapping entries. Initial results showed that there is a significant difference between the parse times of the original parser $(\mu=15.64ms,\ \sigma=69.29ms)$ and the modified parser $(\mu=4.60ms,\ \sigma=69.53ms)$, with a $t$-score of $t(890)=52.26$ and a $p$ -value of $9.41 \times 10^{-274}$. This means that faster parse times are achieved when using the modified parser.