The research advancements have made the neural network algorithms deployed in the autonomous vehicle to perceive the surrounding. The standard exteroceptive sensors that are utilized for the perception of the environment are cameras and Lidar. Therefore, the neural network algorithms developed using these exteroceptive sensors have provided the necessary solution for the autonomous vehicle's perception. One major drawback of these exteroceptive sensors is their operability in adverse weather conditions, for instance, low illumination and night conditions. The useability and affordability of thermal cameras in the sensor suite of the autonomous vehicle provide the necessary improvement in the autonomous vehicle's perception in adverse weather conditions. The semantics of the environment benefits the robust perception, which can be achieved by segmenting different objects in the scene. In this work, we have employed the thermal camera for semantic segmentation. We have designed an attention-based Recurrent Convolution Network (RCNN) encoder-decoder architecture named ARTSeg for thermal semantic segmentation. The main contribution of this work is the design of encoder-decoder architecture, which employ units of RCNN for each encoder and decoder block. Furthermore, additive attention is employed in the decoder module to retain high-resolution features and improve the localization of features. The efficacy of the proposed method is evaluated on the available public dataset, showing better performance with other state-of-the-art methods in mean intersection over union (IoU).

Sixty-five-year-old Michael Williams was released from jail last month after spending almost a year in jail on a murder charge. The "gunshot" sound that pointed the finger at Williams was initially classified as a firework by the AI. After the charges were dropped due to "insufficient evidence" it was revealed that one of ShotSpotter's human "reviewers" had changed the data to fit the crime, reclassifying the sound as a gunshot instead of a firework [1]. The case highlighted the dangers that the system poses to civil liberties and brings to question how much power we should give to AI "witnesses", especially those that can easily be tampered with. Shotspotter is a patented acoustic gunshot detection system of microphones, algorithms, and human reviewers that alerts police to potential gunfire [2].

A thought-leader in cyber technology, Adam Mansour has over 15 years' experience spanning endpoint, network and cloud systems security; audits and architecture; building and managing SOCs; and software development. He is the creator of the IntelliGO Managed Detection and Response platform, acquired by ActZero. When you hear cybersecurity firm Darktrace's customers talk about their experience with the company, they will tell you about'the box' from Darktrace they installed. The idea behind the box is that it allows you to see malicious network traffic and coordinate to the cloud directly so you can react quickly. The main customer feedback is that the box was pretty and showed them lots of nice graphics -- beautiful network maps, gorgeous matrixes, pipe diagrams.

AI biases are common, persistent, and hard to address. We wish people see what AI can do but not its flaws. But this is like driving a Lamborghini with the check engine light on. It may run fine for the next few weeks but accidents are waiting to happen. To address the problem, we need to know what is fairness. Can it be judged or evaluated? In the previous article, we look at the complexity of AI bias. All AI designs need to follow the laws if applicable. In this section, we will discuss these issues. Sensitive characters are bias factors that are practically or morally irrelevant to a decision.

After adding the dataset to our project, we have to create now the input class for our model. Therefore we are going to add a new class in the subfolder "Objects" called SentimentData. Column 0 represents our input text, while column 1 stands for the output label. So far, we have created the input class for the model, but we also need an output class, which contains the output properties after running the model. Let's create a class called SentimentPrediction in the same Objects folder as our SentimentData class.

The titanic is one of the most iconic and at the same time saddest stories in the history of human beings. There are barely any individuals who are not familiar with its story and how lucky some people were on that liner, because of certain characteristics that they took with them. Whether they were kids or had a higher purchasing power, there was a pattern to follow when predicting the probability of getting a safe boat, leaving unharmed the ship. The cleaning of the data is by far the most challenging part in most of the machine learning projects since you can extremely improve (or harm) your model according to the individual features and the types of features you train your model with. For feature selection, we will go through three main aspects.

Precision medicine is a clinical approach for disease prevention, detection and treatment, which considers each individual's genetic background, environment and lifestyle. The development of this tailored avenue has been driven by the increased availability of omics methods, large cohorts of temporal samples, and their integration with clinical data. Despite the immense progression, existing computational methods for data analysis fail to provide appropriate solutions for this complex, high-dimensional and longitudinal data. In this work we have developed a new method termed TCAM, a dimensionality reduction technique for multi-way data, that overcomes major limitations when doing trajectory analysis of longitudinal omics data. Using real-world data, we show that TCAM outperforms traditional methods, as well as state-of-the-art tensor-based approaches for longitudinal microbiome data analysis. Moreover, we demonstrate the versatility of TCAM by applying it to several different omics datasets, and the applicability of it as a drop-in replacement within straightforward ML tasks.

The rise of the new generation of cyber threats demands more sophisticated and intelligent cyber defense solutions equipped with autonomous agents capable of learning to make decisions without the knowledge of human experts. Several reinforcement learning methods (e.g., Markov) for automated network intrusion tasks have been proposed in recent years. In this paper, we introduce a new generation of network intrusion detection methods that combines a Q-learning-based reinforcement learning with a deep-feed forward neural network method for network intrusion detection. Our proposed Deep Q-Learning (DQL) model provides an ongoing auto-learning capability for a network environment that can detect different types of network intrusions using an automated trial-error approach and continuously enhance its detection capabilities. We provide the details of fine-tuning different hyperparameters involved in the DQL model for more effective self-learning. According to our extensive experimental results based on the NSL-KDD dataset, we confirm that the lower discount factor which is set as 0.001 under 250 episodes of training yields the best performance results. Our experimental results also show that our proposed DQL is highly effective in detecting different intrusion classes and outperforms other similar machine learning approaches.

The use of machine learning techniques in biomedical research has exploded over the past few years, as exemplified by the dramatic increase in the number of journal articles indexed on PubMed by the term "machine learning", from 3,200 in 2015 to over 18,000 in 2020. While substantial scientific advancements have been made possible thanks to machine learning, the inner working of most machine learning algorithms remains foreign to many clinicians, most of whom are quite familiar with traditional statistical methods but have little formal training on advanced computer algorithms. Unfortunately, journal reviewers and editors are sometimes content with accepting machine learning as a black box operation and fail to analyze the results produced by machine learning models with the same level of scrutiny that is applied to other clinical and basic science research. The goal of this journal club is to help readers develop the knowledge and skills necessary to digest and critique biomedical journal articles involving the use of machine learning techniques. It is hard for a reviewer to know what questions to ask if he/she does not understand how these algorithms work.

The Asian giant hornet (AGH) appeared in Washington State appears to have a potential danger of bioinvasion. Washington State has collected public photos and videos of detected insects for verification and further investigation. In this paper, we analyze AGH using data analysis,statistics, discrete mathematics, and deep learning techniques to process the data to controlAGH spreading.First, we visualize the geographical distribution of insects in Washington State. Then we investigate insect populations to varying months of the year and different days of a month.Third, we employ wavelet analysis to examine the periodic spread of AGH. Fourth, we apply ordinary differential equations to examine AGH numbers at the different natural growthrate and reaction speed and output the potential propagation coefficient. Next, we leverage cellular automaton combined with the potential propagation coefficient to simulate the geographical spread under changing potential propagation. To update the model, we use delayed differential equations to simulate human intervention. We use the time difference between detection time and submission time to determine the unit of time to delay time. After that, we construct a lightweight CNN called SqueezeNet and assess its classification performance. We then relate several non-reference image quality metrics, including NIQE, image gradient, entropy, contrast, and TOPSIS to judge the cause of misclassification. Furthermore, we build a Random Forest classifier to identify positive and negative samples based on image qualities only. We also display the feature importance and conduct an error analysis. Besides, we present sensitivity analysis to verify the robustness of our models. Finally, we show the strengths and weaknesses of our model and derives the conclusions.