Reviewerly/exHarmony huggingface dataset:
Towards Stochastic Neural Network via Feature Distribution Calibration

Stochastic neural network (SNN) has attracted increasing attention in recent years, which benefits several important tasks by modeling samples uncertainly, such as adversarial defense, label noise robustness, and model calibration. The current implementations of existing stochastic neural networks are mainly Gaussian noise injection, e.g., deep Variational Information Bottleneck (VIB) uses fixed Gaussian prior to derive noise injection, simple and effective stochastic neural network (SE-SNN) uses a non-informative Gaussian prior to implement it. However, Gaussian distribution assumption is insufficient to model more complex distributions of data in practical, such as the skewed distribution or multi-modal distribution. In this paper, we relax the strict Gaussian prior assumption, and propose a novel distribution calibrated stochastic neural network (DCSNN) which integrates two successive steps. These two steps are as follows: 1) The trained feature vector is preprocessed to make its feature distribution closer to the Gaussian-like distribution. 2) Gaussian distribution’s mean and variance are used to model the sample’s activation indeterminacy. The experimental results show that, compared with the existing methods, our proposed method can achieve state-of-the-art results in a variety of datasets, backbone architectures and multiple applications.
PCAM: A Data-driven Probabilistic Cyber-alert Management Framework

We propose PCAM , a Probabilistic Cyber-Alert Management framework, that enables chief information security officers to better manage cyber-alerts. Workers in Cyber Security Operation Centers usually work in 8- or 12-hour shifts. Before a shift, PCAM analyzes data about all past alerts and true alerts during the shift time-frame to schedule a given set of analysts in accordance with workplace constraints so that the expected number of “uncovered” true alerts (i.e., true alerts not shown to an analyst) is minimized. PCAM achieves this by formulating the problem as a bi-level non-linear optimization problem and then shows how to linearize and solve this complex problem. We have tested PCAM extensively. Using statistics derived from 44 days of real-world alert data, we are able to minimize the expected number of true alerts that are not manually examined by a team consisting of junior, senior, and principal analysts. We are also able to identify the optimal mix of junior, senior, and principal analysts needed during both day and night shifts given a budget, outperforming some reasonable baselines. We tested PCAM ’s proposed schedule (from statistics on 44 days) on a further 6 days of data, using an off-the-shelf false alarm classifier to predict which alerts are real and which ones are false. Moreover, we show experimentally that PCAM is robust to various kinds of errors in the statistics used.
An Empirical Study of Intelligent Security Analysis Methods Utilizing Big Data

The frequency of cyber-terrorist attacks targeting broadcasting companies or key financial institutions, in which PCs affected by malicious code obstruct normal operations, has been increasing. With the widespread availability of internet technology, the frequency of intelligent cyber-attacks in virtual cyber environments, such as APT attacks, continues to rise. Through an intelligent analysis of cyber threats which are still unknown, it is possible to prevent security accidents before they occur through the use of intelligent security analysis methods based on big data. The researcher conducted this empirical study concerning intelligent security analysis methods utilizing big data which are capable of preventing cyber threats, while protecting information assets from risks through evaluation and prediction.
Political influence and banking performance: Evidence from the African countries

This paper aims to explore the impact of political factors on the performance of state-owned banks in 50 African countries over the 2005–2012 period. We use panel data methods (fixed effects and quantile regression models) with unique hand-collected data concerning political factors and ownership structures of African banks. Our main results confirm the social and political view of state-owned banks for the African countries, but the results are somewhat heterogeneous according to the level of banking performance: ownership structure significantly affects the performance of banks with low profitability compared to those with high profitability. However, we find no significant political interference in banking activity. Finally, our results confirm that politicians do not prioritize monetary policy or banking regulation in their electoral programs in African countries.
Reimagining chess with AlphaZero

AI is driving the next evolution of chess, giving players a glimpse into the game's future.
Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System

With the recent advances in mobile sensing technologies, large amounts of sequential data are collected, such as vehicle GPS records, stock prices, sensor data from air quality detectors. Recurrent neural networks (RNNs) have been studied extensively to learn complex patterns for sequential data, with applicatons in natural language processing for sentence prediction/completion, human activity recognition for predicting or classifying human activities. However, there are many practical issues when training RNNs, e.g., vanishing and exploding gradients often occur due to the repeatability of network weights, etc. In this paper, we study the training stability in deep recurrent neural networks (RNNs), and propose a novel network, namely, deep incremental RNN (DIRNN). In contrast to the literature, we prove that DIRNN is essentially a Lyapunov stable dynamical system where there is no vanishing or exploding gradient in training. To demonstrate the applicability in practice, we also propose a novel implementation, namely TinyRNN, that sparsifies the transition matrices in DIRNN using weighted random permutations to reduce the model sizes. We evaluate our approach on seven benchmark datasets, and achieve state-of-the-art results. Demo code is provided in the supplementary file.
Solving nonlinear classification and regression problems by wave interference with a single-layer complex-valued perceptron

The classical single-layer perceptron, while being able to solve linearly separable problems, fails when applied to nonlinear problems such as the XOR problem and thus is unable to represent a universal gate. In this paper, we show how a modification of the activation function of the perceptron can overcome this restriction and provide a geometric explanation of this finding. While these modifications have a negative effect on the convergence properties of real-valued networks, the introduction of complex weights and training only their phases enables wave-interference mechanisms to take place that can guarantee convergence for gradient-descent training, which is shown both analytically and by computer simulations. It is further demonstrated that the framework can be extended and applied to other nonlinear classification and regression problems. The relevance of the results with regard to artificial and biological neural networks is discussed.
Accelerating JPEG Decompression on GPUs

The JPEG compression format has been the standard for lossy image compression for over multiple decades, offering high compression rates at minor perceptual loss in image quality. For GPU-accelerated computer vision and deep learning tasks, such as the training of image classification models, efficient JPEG decoding is essential due to limitations in memory bandwidth. As many decoder implementations are CPU-based, decoded image data has to be transferred to accelerators like GPUs via interconnects such as PCI-E, implying decreased throughput rates. JPEG decoding therefore represents a considerable bottleneck in these pipelines. In contrast, efficiency could be vastly increased by utilizing a GPU-accelerated decoder. In this case, only compressed data needs to be transferred, as decoding will be handled by the accelerators. In order to design such a GPU-based decoder, the respective algorithms must be parallelized on a fine-grained level. However, parallel decoding of individual JPEG files represents a complex task. In this paper, we present an efficient method for JPEG image decompression on GPUs, which implements an important subset of the JPEG standard. The proposed algorithm evaluates codeword locations at arbitrary positions in the bitstream, thereby enabling parallel decompression of independent chunks. Our performance evaluation shows that on an A100 (V100) GPU our implementation can outperform the state-of-the-art implementations libjpegturbo (CPU) and nvJPEG (GPU) by a factor of up to 51 (34) and 8.0 (5.7). Furthermore, it achieves a speedup of up to 3.4 over nv JPEG accelerated with the dedicated hardware JPEG decoder on an A100.
Using Stain Decomposition for Nucleus Segmentation on Multisource H&amp;E Slide Images

The deep learning technique is widely used for whole slide image (WSI) analysis due to its speed and relatively objective results. Nucleus instance segmentation, in which the nuclei in the WSI are individually identified, can provide many useful parameters to assist and accelerate the diagnosis process for pathologists. Hematoxylin and eosin stain (H&E stain) is commonly used on pathology tissue slides to observe the behavior and structure information of cells. However, the color-oriented features of the nuclei vary widely depending on the dye and staining protocol employed and also the scanning system used to obtain the digital WSIs. Accordingly, the proposed lightweight network module attached to the front of an arbitrary network can provide a more robust segmentation performance irrespective of staining protocols employed and scanning circumstances. Notably, the proposed module improves the performance for unseen color-oriented features and thus can benefit the realworld applications, where the WSIs may be collected from multiple hospitals and laboratories.
Patterns for Blockchain-Based Payment Applications

As the killer application of blockchain technology, blockchain-based payments have attracted extensive attention ranging from hobbyists to corporates to regulatory bodies. Blockchain facilitates fast, secure, and cross-border payments without the need for intermediaries such as banks. Because blockchain technology is still emerging, systematically organised knowledge providing a holistic and comprehensive view on designing payment applications that use blockchain is yet to be established. If such knowledge could be established in the form of a set of blockchain-specific patterns, architects could use those patterns in designing a payment application that leverages blockchain. Therefore, in this paper, we first identify a token’s lifecycle and then present 12 patterns that cover critical aspects in enabling the state transitions of a token in blockchain-based payment applications. The lifecycle and the annotated patterns provide a payment-focused systematic view of system interactions and a guide to effective use of the patterns.
Research on a Strategy of Improving Students’ Satisfaction with Teaching: An Empirical Study Based on Undergraduate Universities in Sichuan

Objective. To understand the impact of platform selection on each influencing factor of students’ teaching evaluation. Method. The factor analysis method was used to analyze the questionnaire survey of students’ satisfaction with teaching evaluation in 16 undergraduate colleges and universities in Sichuan Province. The impact of five key factors on students’ satisfaction with teaching, i.e., teachers’ teaching ethics, teaching ability, teaching content, teaching method, and teaching effect, was explored. Results. It shows that platform selection has a significant impact on various factors in the whole online teaching process. Regarding the degree of impact of platform selection on students’ teaching evaluation satisfaction, senior students are slightly higher than junior students, boys are slightly higher than girls, and students majoring in science and technology are slightly higher than those in other disciplines. Conclusion. Due to the great influence of teaching platform selection on online teaching satisfaction, teachers must choose an appropriate teaching platform according to teaching needs.
Remove and recover: Deep end-to-end two-stage attention network for single-shot heavy rain removal

In this paper, we propose a novel heavy rain removal algorithm using a deep neural network. Unlike most of the existing deraining methods, heavy rain removal is a more challenging task because it is necessary to remove both the rain marks and the haze effects, which are entangled in a complex manner. Motivated by this, we propose a new end-to-end two-stage attention network for single-shot heavy rain removal. The proposed network is connected serially with a removal network and a recovery network, which are based on a newly introduced dilation-wise attention block and skip attention block. Based on these attention techniques, the removal network predicts the heavy rain effect that needs to be removed from a given image, and the recovery network successfully predicts the details that need to be recovered, resulting in a clean image. We also introduce a new realistic RainCityscapes+ dataset, composed of synthesized outdoor images, and demonstrate extensive experiments, the results of which show our approach outperforms the state-of-the-art methods on both real and synthetic datasets quantitatively and qualitatively.
Violent Views Detection in Urdu Tweets

The widespread use of social media has led to substantial increase in the global connectivity. Consequently, the content shared on social media has the potential to become viral in a short span of time. While some content is desired to become viral, there is a high risk that the inappropriate messages can also become viral which could be disastrous for the society. For instance, spreading violent views may lead to riots and unrest in the society. Therefore, it is desired to detect violent views to ensure stopping them from spreading. To that end, this study has scrapped Twitter to develop and publicly release the first-ever Violent Views Detection corpus for Urdu (VVD-21). The corpus is composed of 3297 Urdu tweets which are manually classified into Violent and Non-Violent views. Furthermore, experiments are performed using six traditional and two deep learning techniques to evaluate their effectiveness of these techniques for automatically detecting violent views in Urdu text. The results of the experiments show that Logistic Regression is the most effective technique as it achieved the highest F1 score of 0.881.
IPGAN: Identity-Preservation Generative Adversarial Network for unsupervised photo-to-caricature translation

Photo-to-caricature translation is an extremely challenging task because there are not only texture differences between caricatures and photos, but also various spatial deformations in caricatures. Most of existing methods tend to introduce difficult obtained additional information such as precise facial landmarks to guide caricature generation. In addition, identity preservation is a crucial characteristic of caricatures, but unfortunately there seems to be few methods to consider it. Motivated by the aforementioned observations, we propose an Identity-Preservation Generative Adversarial Network (IPGAN) for unsupervised photo-to-caricature translation. In particular, considering the importance of identity retention, we propose a novel identity preservation loss to hold the identity information of original photos and improve the quality of generated caricatures. To capture realistic caricature styles, we design a style differentiation loss to help our model produce caricatures with styles that remarkably differ from photos. Moreover, to learn satisfactory deformations without supervision, our model uses a warp controller to acquire exaggerations automatically that enable to customize diverse exaggerations. As an unsupervised translation method, our IPGAN can also be applied to caricature-to-photo translation. Experiments on the WebCaricature dataset suggest that our IPGAN achieves state-of-the-art performance and can generate realistic as well as identity preservation caricatures.
Movie Recommendation System Using Deep Learning

It is a challenge to design a movie recommendation system with artificial intelligent technology. It is difficult to get satisfactory results as not all data is reliable and valuable from the internet. To solve this problem, researchers recommend using a recommendation system which presents relevant information instead of searching the same information multiple times. Generally, collaborative filtering methods based on user information (such as gender type, geographical area, or preference) are effective. However, in today’s era, everyone is concerned about suggestions or guidelines so that he/she can make the decision, our area of research includes movies recommendation system by which we can provide the list of similar movies to the user. Now the user is assisted with some lists of movies recommended to the user by our model, he/she only has to decide which movie he should watch next. Instead of looking into the whole list it’s better to have some recommendations based on which he/she can decide easily. The overall performance for the purposed framework resulted in an accuracy of 66% which is good for such initiative.
Analysis of Structural Bias in Differential Evolution Configurations

Differential Evolution is a popular optimisation method with a small number of parameters. However, different hyper-parameters and Differential Evolution variants such as different mutation operators and the F and $$C_r$$ parameter may introduce structural bias. Structural bias is a form of bias where artefacts in the algorithm lead to a preference to particular regions in the search space regardless of the objective function. Many algorithm configurations suffer from structural bias, but it is very hard to automatically detect it and even harder to detect what kind of structural bias is involved and what component might be the cause of it. A comprehensive study of the occurrence and type of structural bias in Differential Evolution configurations has not yet been carried out till now. In this chapter, we systematically evaluate 10980 Differential Evolution configurations on structural bias with the open-source BIAS toolbox. Using this toolbox we identify which configurations cause bias and what type of bias it is. In addition, we analyse the results to make clear recommendations on which components and parameters can be used in Differential Evolution to ensure unbiased behaviour within reasonable computational budget.
Boosting Deep Ensemble Performance with Hierarchical Pruning

Deep neural network ensembles have become attractive learning techniques with better generalizability over individual models. Some mission critical applications may require a large number of deep neural networks to achieve desirable accuracy and generalizability, making the ensemble execution costly with respect to runtime and space. This paper proposes a novel hierarchical ensemble pruning approach, which can effectively examine a given pool of M base models and identify smaller high quality deep ensembles of size $S(\ll M)$ with higher ensemble accuracy than the entire deep ensemble of all M models. Our hierarchical pruning approach, coined as HQ, combines three novel techniques. First, we show that the focal diversity metrics is innovative and can accurately capture the negative correlation among the member models of an ensemble, and the use of focal diversity metrics can boost ensemble accuracy. Second, we introduce a focal-diversity based hierarchical pruning algorithm to progressively identify low-cost ensembles with high ensemble diversity and accuracy. Third, we design a focal diversity consensus method to find smaller deep ensembles with low negative correlation. We demonstrate such ensembles offer high accuracy and high robustness while being more time and space efficient in ensemble decision making. Evaluated using two benchmark datasets, we show that the proposed focal diversity powered hierarchical pruning can find significantly smaller ensembles of deep neural network models while achieving the same or better classification generalizability.
Streaming Dynamic Graph Neural Networks for Continuous-Time Temporal Graph Modeling

Dynamic graphs are suitable for modeling structured data that evolve over time and have been widely used in many application scenarios such as social networks, financial transaction networks, and recommendation systems. Recently, many dynamic graph methods are proposed to deal with temporal networks. However, due to the limitations of storage space and computational efficiency, most approaches evolve node representations by aggregating the latest state information of neighbor nodes, thus losing a lot of information about neighbor nodes’ state changes. Besides, high computational complexity makes it challenging to deploy dynamic graph algorithms in real-time. To tackle these challenges, we propose a novel streaming dynamic graph neural network (SDGNN) for modeling continuous-time temporal graphs, which can fully capture the state changes of neighbors and reduce the computational complexity of inference. Under SDGNN, an incremental update component is designed to incrementally update node representation based on the interaction sequence, an inference component is utilized for specific downstream tasks, and a message propagation component is employed to propagate interactive information to the influenced nodes by considering the update time interval, position distance, and influence strengths. Extensive experiments demonstrated that the proposed approach significantly outperforms state-of-the-art methods by capturing more state change information and efficient parallelization.
A Study of Transfer Learning in a Generation Constructive Hyper-Heuristic for One Dimensional Bin Packing

The research presented in this paper investigates the use of transfer learning in a genetic programming generation constructive hyper-heuristic for discrete optimisation, namely, the one dimensional bin packing problem (1BPP). The source hyper-heuristic solves easy and medium problem instances from the Scholl benchmark set and the target hyper-heuristic solves the hard problem instances in the same benchmark set. Performance is assessed in terms of objective value, i.e. the number of bins, computational effort and generality of the hyper-heuristic. This study firstly compares the performance of two transfer learning approaches previously shown to be effective for generation constructive hyper-heuristics, for the one dimensional bin packing problem. Both these approaches performed better than not using transfer learning, with the approach transferring the best elements from each generation of the source hyper-heuristic to the target hyper-heuristic (TL2) producing the best results. The study then investigated transferring knowledge on an area of the search space rather than a point in the search space. Three approaches were developed and evaluated for this purpose. Two of these approaches were able to improve the performance of TL2 on three of the ten problem instances with respect to objective value.