We present the first automatic emotion detection system for Cantonese. This system classifies input text into eight emotion classes: expectancy, joy, love, surprise, anxiety, sorrow, angry, or hate. While a number of emotion corpora and lexica for Mandarin Chinese have been developed, no emotion dataset is available for Cantonese. We leverage existing Mandarin Chinese emotion resources to build the system, with support from Cantonese-Mandarin lexical mappings from a machine translation system, as well as English-Mandarin lexical mappings to handle code-switching in Cantonese input. Evaluation on a set of Cantonese sentences from social media shows promising results.

Cluster Hire is defined as a problem of hiring a group of experts to maximize profits with the ability to complete multiple projects simultaneously under a budget. It assumes that we have a set of projects which require skills and experts who possess various skills. The process of hiring a group of experts to complete a set of projects under the given conditions is proven to be the NP-hard problem. Individuals expect financial support (i.e.salary) which can be handled by a specific budget that we get, to work on the projects. Addition to maximizing the total profit, we are interested in hiring productive experts who can work many projects concurrently with effective result. Therefore, this paper examines the problem of hiring a cluster of experts, so that the total salary does not exceed more than a given budget and maximizes the total benefit of the projects that a highly productive team can cover collectively. We propose two greedy algorithms to solve this problem with different strategies. We illustrate the effectiveness of our approach by experimenting with the synthetic data sets. The results from a study of the synthetic dataset were compared with Bruteforce and Random Algorithm. It suggested that our proposed both project greedy and expert greedy algorithms performed well regarding both accuracy and run-time.

We demonstrate that it is possible to leverage big data in the form of tweets and linked webpages to find expressions of sentiment that signal "bad behavior" such as cyber attacks. We hypothesize that expressions of "outrage" (high intensity, negative affect sentiment) against an organization in public data may be predictive of cyber attacks for two reasons: 1) threat actors may be motivated to launch an attack based on anger/discontent, and 2) outrage associated with an organization or industry may increase the likelihood of that organization or industry being victimized by threat actors (i.e., as a form of "vigilante justice"). We measure sentiment in online content and determine trends in public emotion and their correlation to trends in cyber attacks, as reported in Hackmageddon. We demonstrate that dimensions of sentiment, as afforded by our use of the Circumplex model of emotion, do yield correlations to reported cyber attacks, but differ dependent upon the domain of the data. Thus the use of this technique requires careful analysis for optimal application.

Today, significant impacts of comments on the web affect people decisions while they are about to choose a product. Unfavorable effect of spam attacks in these reviews follows heavy damages for customers and organizations. The majority of methods so far classify reviews to spam and non-spam groups. Therefore, most researches are done on feature learning techniques to enhance the classification performance. From another point of view, presence of huge amount of features makes text classification overwhelming. Attention mechanism has lately been used to improve neural networks performance on sequence modeling. Instead of mining all existing features, attention can enables the model to concentrate on most important parts of the data. To these ends, we applied an attention based deep structure for detecting deceptive reviews. This model contributes distinguishing between truthful and fake reviews and benefits an attentional part to engineering better features. Our proposed model accuracy and scalability is comparable regard to the other common models.

We apply ideas from abstract argumentation theory to study cooperative game theory. Building on Dung's results in his seminal paper, we further the correspondence between Dung's four argumentation semantics and solution concepts in cooperative game theory by showing that complete extensions (the grounded extension) correspond to Roth's subsolutions (respectively, the supercore). We then investigate the relationship between well-founded argumentation frameworks and convex games, where in each case the semantics (respectively, solution concepts) coincide; we prove that three-player convex games do not in general have well-founded argumentation frameworks.

Wireless traffic prediction is a fundamental enabler to proactive network optimisation in 5G and beyond. Forecasting extreme demand spikes and troughs is essential to avoiding outages and improving energy efficiency. However, current forecasting methods predominantly focus on overall forecast performance and/or do not offer probabilistic uncertainty quantification. Here, we design a feature embedding (FE) kernel for a Gaussian Process (GP) model to forecast traffic demand. The FE kernel enables us to trade-off overall forecast accuracy against peak-trough accuracy. Using real 4G base station data, we compare its performance against both conventional GPs, ARIMA models, as well as demonstrate the uncertainty quantification output. The advantage over neural network (e.g. CNN, LSTM) models is that the probabilistic forecast uncertainty can directly feed into decision processes in self-organizing-network (SON) modules.

With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like Machine Learning (ML) detection. Although powerful for conviction of malicious artifacts, these methods do not produce any further information about the type of malware that has been detected. In this work, we address the information gap between ML and signature-based detection methods by introducing an ML-based tagging model that generates human interpretable semantic descriptions of malicious software (e.g. file-infector, coin-miner), and argue that for less prevalent malware campaigns these provide potentially more useful and flexible information than malware family names. For this, we first introduce a method for deriving high-level descriptions of malware files from an ensemble of vendor family names. Then we formalize the problem of malware description as a tagging problem and propose a joint embedding deep neural network architecture that can learn to characterize portable executable (PE) files based on static analysis, thus not requiring a dynamic trace to identify behaviors at deployment time. We empirically demonstrate that when evaluated against tags extracted from an ensemble of anti-virus detection names, the proposed tagging model correctly identifies more than 93.7% of eleven possible tag descriptions for a given sample, at a deployable false positive rate (FPR) of 1% per tag. Furthermore, we show that when evaluating this model against ground truth tags derived from the results of dynamic analysis, it correctly predicts 93.5% of the labels for a given sample. These results suggest that an ML tagging model can be effectively deployed alongside a detection model for malware description.

Recent technological advances have proliferated the available computing power, memory, and speed of modern Central Processing Units (CPUs), Graphics Processing Units (GPUs), and Field Programmable Gate Arrays (FPGAs). Consequently, the performance and complexity of Artificial Neural Networks (ANNs) is burgeoning. While GPU accelerated Deep Neural Networks (DNNs) currently offer state-of-the-art performance, they consume large amounts of power. Training such networks on CPUs is inefficient, as data throughput and parallel computation is limited. FPGAs are considered a suitable candidate for performance critical, low power systems, e.g. the Internet of Things (IOT) edge devices. Using the Xilinx SDAccel or Intel FPGA SDK for OpenCL development environment, networks described using the high-level OpenCL framework can be accelerated on heterogeneous platforms. Moreover, the resource utilization and power consumption of DNNs can be further enhanced by utilizing regularization techniques that binarize network weights. In this paper, we introduce, to the best of our knowledge, the first FPGA-accelerated stochastically binarized DNN implementations, and compare them to implementations accelerated using both GPUs and FPGAs. Our developed networks are trained and benchmarked using the popular MNIST and CIFAR-10 datasets, and achieve near state-of-the-art performance, while offering a >16-fold improvement in power consumption, compared to conventional GPU-accelerated networks. Both our FPGA-accelerated determinsitic and stochastic BNNs reduce inference times on MNIST and CIFAR-10 by >9.89x and >9.91x, respectively.

A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e.g. summing a periodic and linear kernel can capture seasonal variation with a long term trend. Despite a well-studied link between GPs and Bayesian neural networks (BNNs), the BNN analogue of this has not yet been explored. This paper derives BNN architectures mirroring such kernel combinations. Furthermore, it shows how BNNs can produce periodic kernels, which are often useful in this context. These ideas provide a principled approach to designing BNNs that incorporate prior knowledge about a function. We showcase the practical value of these ideas with illustrative experiments in supervised and reinforcement learning settings.

Penetration testing (pentesting) involves performing a controlled attack on a computer system in order to assess it's security. Although an effective method for testing security, pentesting requires highly skilled practitioners and currently there is a growing shortage of skilled cyber security professionals. One avenue for alleviating this problem is automate the pentesting process using artificial intelligence techniques. Current approaches to automated pentesting have relied on model-based planning, however the cyber security landscape is rapidly changing making maintaining up-to-date models of exploits a challenge. This project investigated the application of model-free Reinforcement Learning (RL) to automated pentesting. Model-free RL has the key advantage over model-based planning of not requiring a model of the environment, instead learning the best policy through interaction with the environment. We first designed and built a fast, low compute simulator for training and testing autonomous pentesting agents. We did this by framing pentesting as a Markov Decision Process with the known configuration of the network as states, the available scans and exploits as actions, the reward determined by the value of machines on the network. We then used this simulator to investigate the application of model-free RL to pentesting. We tested the standard Q-learning algorithm using both tabular and neural network based implementations. We found that within the simulated environment both tabular and neural network implementations were able to find optimal attack paths for a range of different network topologies and sizes without having a model of action behaviour. However, the implemented algorithms were only practical for smaller networks and numbers of actions. Further work is needed in developing scalable RL algorithms and testing these algorithms in larger and higher fidelity environments.