Word Embeddings for Fake Malware Generation

Signature and anomaly-based techniques are the fundamental methods to detect malware. However, in recent years this type of threat has advanced to become more complex and sophisticated, making these techniques less effective. For this reason, researchers have resorted to state-of-the-art machine learning techniques to combat the threat of information security. Nevertheless, despite the integration of the machine learning models, there is still a shortage of data in training that prevents these models from performing at their peak. In the past, generative models have been found to be highly effective at generating image-like data that are similar to the actual data distribution. In this paper, we leverage the knowledge of generative modeling on opcode sequences and aim to generate malware samples by taking advantage of the contextualized embeddings from BERT. We obtained promising results when differentiating between real and generated samples. We observe that generated malware has such similar characteristics to actual malware that the classifiers are having difficulty in distinguishing between the two, in which the classifiers falsely identify the generated malware as actual malware almost of the time

A Hybrid Deep Convolutional Neural Network Approach for Predicting the Traffic Congestion Index

Traffic congestion is one of the most important issues in large cities, and the overall travel speed is an important factor that reflects the traffic status on road networks. This study proposes a hybrid deep convolutional neural network (CNN) method that uses gradient descent optimization algorithms and pooling operations for predicting the short-term traffic congestion index in urban networks based on probe vehicles. First, the input data are collected by the probe vehicles to calculate the traffic congestion index (output label). Then, a CNN that uses gradient descent optimization algorithms and pooling operations is applied to enhance its performance. Finally, the proposed model is chosen on the basis of the R-squared (R2) and root mean square error (RMSE) values. In the best-case scenario, the proposed model achieved an R2 value of 98.7%. In addition, the experiments showed that the proposed model significantly outperforms other algorithms, namely the ordinary least squares (OLS), k-nearest neighbors (KNN), random forest (RF), recurrent neural network (RNN), artificial neural network (ANN), and convolutional long short-term memory (ConvLSTM), in predicting traffic congestion index. Furthermore, using the proposed method, the time-series changes in the traffic congestion status can be reliably visualized for the entire urban network

Energy cost savings based on the UPS

Energy-saving, improving energy efficiency, and finding a new efficient way to use energy are considered as an urgent problem in over the world. In this paper, we consider the economics of energy use in combination with energy storage units where two forms of electricity exist in the power system. Then the problem of optimizing the installation capacity (to optimize the investment costs for energy storage) is presented and investigated in connection with the conversion systems. The topic opens a very significant result, including the introduction of a mathematical model to calculate the simulation in optimizing the installation capacity of the equipment in the system, multi-source power, as well as voltage and power stability benefits

Improving Traffic Efficiency in a Road Network by Adopting Decentralised Multi-Agent Reinforcement Learning and Smart Navigation

In the future, mixed traffic flow will consist of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). Effective traffic management is a global challenge, especially in urban areas with many intersections. Much research has focused on solving this problem to increase intersection network performance. Reinforcement learning (RL) is a new approach to optimising traffic signal lights that overcomes the disadvantages of traditional methods. In this paper, we propose an integrated approach that combines the multi-agent advantage actor-critic (MA-A2C) and smart navigation (SN) to solve the congestion problem in a road network under mixed traffic conditions. The A2C algorithm combines the advantages of value-based and policy-based methods to stabilise the training by reducing the variance. It also overcomes the limitations of centralised and independent MARL. In addition, the SN technique reroutes traffic load to alternate paths to avoid congestion at intersections. To evaluate the robustness of our approach, we compare our model against independent-A2C (I-A2C) and max pressure (MP). These results show that our proposed approach performs more efficiently than others regarding average waiting time, speed and queue length. In addition, the simulation results also suggest that the model is effective as the CAV penetration rate is greater than 20%

Differentiable Bayesian Structure Learning with Acyclicity Assurance

Score-based approaches in the structure learning task are thriving because of their scalability. Continuous relaxation has been the key reason for this advancement. Despite achieving promising outcomes, most of these methods are still struggling to ensure that the graphs generated from the latent space are acyclic by minimizing a defined score. There has also been another trend of permutation-based approaches, which concern the search for the topological ordering of the variables in the directed acyclic graph in order to limit the search space of the graph. In this study, we propose an alternative approach for strictly constraining the acyclicty of the graphs with an integration of the knowledge from the topological orderings. Our approach can reduce inference complexity while ensuring the structures of the generated graphs to be acyclic. Our empirical experiments with simulated and real-world data show that our approach can outperform related Bayesian score-based approaches.Comment: Accepted as a regular paper (9.37%) at the 23rd IEEE International Conference on Data Mining (ICDM 2023

Novel dependencies of currents and voltages in power system steady state mode on regulable parameters of three-phase systems symmetrization

The unbalanced mode, negative/zero sequence, variation of real power are caused by the nonlinear or unbalanced loads increase the power transmission losses in distributing power systems and also harmful to the electric devices. Reactive power compensation is considered as the common methods for overcoming asymmetry. The critical issue in reactive power compensation is the optimal calculation of compensation values that is extremely difficult in complex circuits. We proposed a novel approach to overcome these difficulties by providing the creation of new analytical connections of the steady-state mode parameters (voltages, currents) depends on the controlled parameter for the arbitrary circuits. The base of our approach to reactive power compensation is the fractional-polynomial functions. We present a new description of the behavior of voltages and currents depending on the controlled parameters of the reactive power compensation devices, and we prove its effectiveness

Oral Cancer: The State of the Art of Modern-Day Diagnosis and Treatment

Diagnosing and treating lesions of the mouth and gums is challenging for most clinicians because of the wide variety of disease processes that can present with similar appearing lesions and the fact that most clinicians receive inadequate training in mouth diseases. Oral cancer, a common lesion in oral cavity, is not correctly diagnosing a clinical picture of an early squamous cell carcinoma. The prevalence of oral cancer continues to rise worldwide, related to the increase in consumption of tobacco, alcohol and other carcinogenic products. However, there has also been a significant reduction in mortality due to increasing awareness, early diagnosis and advances in treatments. This chapter is an attempt to provide a comprehensive update encompassing the spectrum of etiologic/risk factors, current clinical diagnostic tools, management philosophies, and molecular biomarkers and progression indicators of oral cancer

HybridMingler: Towards Mixed-Reality Support for Mingling at Hybrid Conferences

Mingling, the activity of ad-hoc, private, opportunistic conversations ahead of, during, or after breaks, is an important socializing activity for attendees at scheduled events, such as in-person conferences. The Covid-19 pandemic had a dramatic impact on the way conferences are organized, so that most of them now take place in a hybrid mode where people can either attend on-site or remotely. While on-site attendees can resume in-person mingling, hybrid modes make it challenging for remote attendees to mingle with on-site peers. In addressing this problem, we propose a collaborative mixed-reality (MR) concept, including a prototype, called HybridMingler. This is a distributed MR system supporting ambient awareness and allowing both on-site and remote conference attendees to virtually mingle. HybridMingler aims to provide both on-site and remote attendees with a spatial sense of co-location in the very same venue location, thus ultimately improving perceived presence

Effects of protein levels of commercial diets on the growth performance and survival rate of rabbitfish (Siganus guttatus) at the nursing stage

This study aimed to determine the effect of a commercial diet's protein level on the fry-to-fingerling stage. Thirty days-old fries having the initial length and weight of 18.25 ± 0.15 mm fish-1 and 0.036 ± 0.50 g fish-1 respectively have been used in this study. Diet having three protein levels i.e. 30% (trial 1 as control), 35% (trial 2), 40% (trial 3), and 45% (trial 4), respectively, have been used to evaluate the effect of protein, and each trial has been repeated three times. During the study, stocking density was allocated to 1000 fish per composite tank with a volume of 1 m3. After 30 days of rearing, the weight of fingerlings in trial 1 reached up to 1.50 ± 0.02 g fish-1 and it was recorded as 1.52 ± 0.01g for trial 2, these two were lower than that of trials 3 and 4, where fingerling weight was reported 1.69 ± 0.01 and 1.58g fish-1 respectively and obtained the best weight compared to others. The length of fingerlings at the end of the experimental period was also changed in different trials and it was recorded 47.12; 46.92; 50.97; and 48.89 mm fish-1 for trail 1, 2, 3, and 4 respectively, among the tested combinations lower fingerlings length was recorded for trial 2 (35% CP), but it is not significantly different for trial 1 and 2 and a significant difference (P < 0.05) was reported for trail 2, 3, and 4. The survival rate of fingerlings ranged from 67.27 to 72.33%. Meanwhile, the herd distribution coefficient variation (CVW) in the treatment using 40% protein (trial 3) was the highest at 72.33% (p < 0.05). The results of the study can be concluded that the level of protein has a significant effect on the various growth parameters of fingerlings

LAKESCAPE UNDER URBAN DEVELOPMENT IN DONG DA DISTRICT, HANOI CITY

Joint Research on Environmental Science and Technology for the Eart