A Person Re-identification based Misidentification-proof Person Following Service Robot
Two years ago, I attended a robot contest, in which one of the missions required the robot to follow the pedestrian to complete the task. At that time, I used their demo program to complete the task. Not long after, I found two main issues: 1. The program follows the closest point read by the depth camera, which if I walk close to a wall next to, the robot may likely ‘follow’ the wall. 2. Not to mention if another pedestrian crosses between the robot and the target. Regarding these two issues, I decided to improve it. We’ve designed a procedure of using YOLO Object Detection and Person re-identification to re-identify the target for continuous following.
THE BLIND'S SHINE
Nowadays, all blind people are able to read any type of text thanks to The Braille system, which is a system of reading by touch. The Braille system provides a good and important help to blind people but it’s not enough. They try to integrate into society, try to read and write the same as those blessed with sight. However, only 18% of people can actually write in Braille and doing so takes a lot of effort. While Braille techniques have existed for many years now, there isn’t one that takes advantage of the comfort and easiness of modern technology. To be fair and give more chances to the blind people, and with all the available technologies nowadays, we must find solutions and innovative ideas to fulfill this objective. And this is how our project was born. To be honest, it’s not just a project, for us it’s a dream. Our focus in this project is creating an automatic machine based on transforming our language to their language and transforming this new invention to assist the blind in writing whole texts through speaking. It will be easier than the traditional way. This part of the project will help us print Braille on paper automatically in the cheapest, fastest and easiest way, unlike the traditional method that not only takes a lot of time but also needs a professional. Our printer will only cost approximately 250$ compared to regular printers that cost 6500$! the principles of the code of the speech recognition are voice pickup in all languages as first step, second speech recognition, then speech translation, after that speech analysis and finally translation into Braille and printing it. As for the future, we will work on face, object and image recognition as a scanner to make the visually impaired people read the name of the object and recognize people’s faces like anyone else.
HoneySurfer: Intelligent Web-Surfing Honeypots
In Singapore’s evolving cyber landscape, 96% of organisations have suffered at least one cyber attack and 95% of organisations have been reporting more sophisticated attacks in the frame of one year according to a 2019 report[1] by Carbon Black. As such, more tools must be utilised to counter increasingly refined attacks performed by malicious actors. Honeypots are effective tools for studying and mitigating these attacks. They work as decoy systems, typically deployed alongside real systems to capture and log the activities of the attacker. These systems are useful as they can actively detect potential attacks, help cybersecurity specialists study an attacker’s tactics and even misdirect attackers from their intended targets. Honeypots can be classified into two main categories: 1. Low-interaction honeypots merely emulate network services and internet protocols, allowing for limited interaction with the attacker. 2. High-interaction honeypots emulate operating systems, allowing for much more interaction with the attacker. Although honeypots are powerful tools, its value diminishes when its true identity is uncovered by attackers. This is especially so with attackers becoming more skilled through system fingerprinting or analysing network traffic from targets and hence, hindering honeypots from capturing more experienced attackers. While substantial research has been done to defend against system fingerprinting scans (see 1.1 Related Work), not much has been done to defend against network traffic analysis. As pointed out by Symantec[2][3], when attackers attempt to sniff network traffic of the system in question, the lack of network traffic raises a red flag, increasing the likelihood of the honeypot’s true identity being discovered. In addition, the main concern with regards to honeypot deployment being their ability to attract and engage attackers for a substantial period of time, an increased ability to interest malicious actors is invaluable. Producing human-like network activity on a honeypot would appeal to more malicious actors. Hence, this research aims to build an intelligent web-surfer which can learn and thus simulate human web-surfing behaviour, creating evidence of human network activities to disguise the identity of honeypots as production systems and luring in more attackers interested in packet sniffing for malicious purposes.
Predicting the Binding Affinity between Medicine and Estrogen Receptor Beta
Recent studies showed that the probability of Taiwanese females developing breast cancer has risen dramatically over the past 30 years. We are now facing younger and more breast cancer patients in Taiwan. What makes the matter even more severe, is the fact that patients that take cancer treating medicine will suffer from its serious side effects, some may even lose the ability to reproduce. We hope to develop a new system that can help doctors and researchers develop new medicine for treating breast cancer, the way medicine cures cancer tumors are by attaching onto the infected cells’ receptors. After collecting MACCS data (converted from SMILES), the dataset will be used for training the machine learning program. Due to the problem of insufficient training data, we used an ensemble method to generate our machine learning model. Among the three basic ensemble techniques, Max Voting, Averaging, and Weighted Averaging. we selected the max voting technique to perform the prediction for this research. We created two separate datasets, positive and negative, the two datasets will later be used as training data for the program. We weren’t sure of the ratio of positive and negative in the training data, therefore we compare 40 different ratios and evaluate the results. By comparing the accuracy of the models, we found out that when the ratio between positive data and negative data is 1:3000, the machine learning program will have the highest precision. After we created the final model through voting among the 1000 models generated, we evaluate the precision of the model through the following methods, AUC, precision, recall. The ultimate goal of this research is to assist doctors and researchers shorten the process of developing and testing new medicines.
Limited Query Black-box Adversarial Attacks in the Real World
We study the creation of physical adversarial examples, which are robust to real-world transformations, using a limited number of queries to the target black-box neural networks. We observe that robust models tend to be especially susceptible to foreground manipulations, which motivates our novel Foreground attack. We demonstrate that gradient priors are a useful signal for black-box attacks and therefore introduce an improved version of the popular SimBA. We also propose an algorithm for transferable attacks that selects the most similar surrogates to the target model. Our black-box attacks outperform state-of-the-art approaches they are based on and support our belief that the concept of model similarity could be leveraged to build strong attacks in a limited-information setting.
Method of prosthetic vision
This work is devoted to solving the problem of orientation in the space of visually impaired people. Working on the project, a new way of transmitting visual information through an acoustic channel was invented. In addition, was developed the device, which uses distance sensors to analyze the situation around a user. Thanks to the invented algorithm of transformation of the information about the position of the obstacle into the sound of a certain tone and intensity, this device allows the user to transmit subject-spatial information in real time. Currently, the device should use a facette locator made of 36 ultrasonic locators grouped in 12 sectors by the azimuth and 3 spatial cones by the angle. Data obtained in such a way is converted into its own note according to the following pattern : the angle of the place corresponds to octave, the azimuth corresponds to the note and the distance corresponds to the volume. The choice of the notes is not unambiguous. However, we used them for the reason that over the centuries, notes have had a felicitous way of layout on the frequency range and on the logarithmic scale. Therefore, the appearance of a new note in the total signal will not be muffled by a combination of other notes. Consequently, a blind person, moving around the room with the help of the tone and volume of the sound signals, will be able to assess the presence and location of all dangerous obstacles. After theoretical substantiation of the hypothesis and analysis of the available information, we started the production of prototypes of the devices that would implement the idea of transmitting information via the acoustic channel.
Limited Query Black-box Adversarial Attacks in the Real World
We study the creation of physical adversarial examples, which are robust to real-world transformations, using a limited number of queries to the target black-box neural networks. We observe that robust models tend to be especially susceptible to foreground manipulations, which motivates our novel Foreground attack. We demonstrate that gradient priors are a useful signal for black-box attacks and therefore introduce an improved version of the popular SimBA. We also propose an algorithm for transferable attacks that selects the most similar surrogates to the target model. Our black-box attacks outperform state-of-the-art approaches they are based on and support our belief that the concept of model similarity could be leveraged to build strong attacks in a limited-information setting.