Recently, the research of wireless sensing has achieved more intelligent results, and the intelligent sensing of human location and activity can be realized by means of WiFi devices. However, most of the current human environment perception work is limited to a single person’s environment, because the environment in which multiple people exist is more complicated than the environment in which a single person exists. In order to solve the problem of human behavior perception in a multi-human environment, we first proposed a solution to achieve crowd counting (inferred population) using deep learning in a closed environment with WIFI signals – DeepCount, which is the first in a multi-human environment. step. Since the use of WiFi to directly count the crowd is too complicated, we use deep learning to solve this problem, use Convolutional Neural Network(CNN) to automatically extract the relationship between the number of people and the channel, and use Long Short Term Memory(LSTM) to resolve the dependencies of number of people and Channel State Information(CSI) . To overcome the massive labelled data required by deep learning method, we add an online learning mechanism to determine whether or not someone is entering/leaving the room by activity recognition model, so as to correct the deep learning model in the fine-tune stage, which, in turn, reduces the required training data and make our method evolving over time. The system of DeepCount is performed and evaluated on the commercial WiFi devices. By massive training samples, our end-to-end learning approach can achieve an average of 86.4% prediction accuracy in an environment of up to 5 people. Meanwhile, by the amendment mechanism of the activity recognition model to judge door switch to get the variance of crowd to amend deep learning predicted results, the accuracy is up to 90%. Read More
Daily Archives: June 3, 2019
Cameras That Can See Through Walls!
This AI use echolocation to identify what you’re doing
GUO XINHUA WANTS to teach computers to echolocate. He and his colleagues have built a device, about the size of a thin laptop, that emits sound at frequencies 10 times higher than the shrillest note a piccolo can sustain. The pitches it produces are inaudible to the human ear. When Guo’s team aims the device at a person and fires an ultrasonic pitch, the gadget listens for the echo using its hundreds of embedded microphones. Then, employing artificial intelligencetechniques, his team tries to decipher what the person is doing from the reflected sound alone.
The technology is still in its infancy, but they’ve achieved some promising initial results. Based at the Wuhan University of Technology, in China, Guo’s team has tested its microphone array on four different college students and found that they can identify whether the person is sitting, standing, walking, or falling, with complete accuracy, they report in a paper published today in Applied Physics Letters. While they still need to test that the technique works on more people, and that it can identify a broader range of behaviors, this demonstration hints at a new technology for surveilling human behavior. Read More
A single feature for human activity recognition using two-dimensional acoustic array
Human activity recognition is widely used in many fields, such as the monitoring of smart homes, fire detecting and rescuing, hospital patient management, etc. Acoustic waves are an effective method for human activity recognition. In traditional ways, one or a few ultrasonic sensors are used to receive signals, which require many feature quantities of extraction from the received data to improve recognition accuracy. In this study, we propose an approach for human activity recognition based on a two-dimensional acoustic array and convolutional neural networks. A single feature quantity is utilized to characterize the sound of human activities and identify those activities. The results show that the total accuracy of the activities is 97.5% for time-domain data and 100% for frequency-domain data. The influence of the array size on recognition accuracy is discussed, and the accuracy of the proposed approach is compared with traditional recognition approaches such as k-nearest neighbor and support vector machines where it outperformed them. Read More
The Best and Most Current of Modern Natural Language Processing
Over the last two years, the Natural Language Processing community has witnessed an acceleration in progress on a wide range of different tasks and applications. 🚀 This progress was enabled by a shift of paradigm in the way we classically build an NLP system: for a long time, we used pre-trained word embeddings such as word2vec or GloVe to initialize the first layer of a neural network, followed by a task-specific architecture that is trained in a supervised way using a single dataset.
Recently, several works demonstrated that we can learn hierarchical contextualized representations on web-scale datasets 📖 leveraging unsupervised (or self-supervised) signals such as language modeling and transfer this pre-training to downstream tasks (Transfer Learning).Excitingly, this shift led to significant advances on a wide range of downstream applications ranging from Question Answering, to Natural Language Inference through Syntactic Parsing…
“Which papers can I read to catch up with the latest trends in modern NLP?”
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How DevOps Drives Analytics Operationalization and Monetization
I recently wrote a blog “Interweaving Design Thinking and Data Science to Unleash Economic V…” that discussed the power of interweaving Design Thinking and Data Science to make our analytic efforts more effective. Our approach was validated by a recentMcKinsey article titled “Fusing data and design to supercharge innovation” that stated:
“While many organizations are investing in data and design capabilities, only those that tightly weave these disciplines together will unlock their full benefits.”
I even developed some Data Science playing cards that one could use to help guide this Design Thinking-Data Science interweaving process. Read More
On YouTube’s Digital Playground, an Open Gate for Pedophiles
Christiane C. didn’t think anything of it when her 10-year-old daughter and a friend uploaded a video of themselves playing in a backyard pool.
“The video is innocent, it’s not a big deal,” said Christiane, who lives in a Rio de Janeiro suburb.
XA few days later, her daughter shared exciting news: The video had thousands of views. Before long, it had ticked up to 400,000 — a staggering number for a video of a child in a two-piece bathing suit with her friend.
“I saw the video again and I got scared by the number of views,” Christiane said.
She had reason to be. Read More
Twitter acquires Fabula AI, a machine learning startup that helps spot fake news
Twitter has acquired Fabula AI, a London-based startup that uses machine learning (ML) to help detect the spread of misinformation online.
Terms of the deal were not disclosed, but the acquisition will underpin a research group at Twitter led by Sandeep Pandey that will work toward finding new ways to leverage machine learning across natural language processing (NLP), recommendations systems, reinforcement learning, and graph deep learning. The group will also address ML ethics. Read More
DIY Facial Recognition for Porn Is a Dystopian Disaster
Someone posting on Chinese social network Weibo claims to have used facial recognition to cross-reference women’s photos on social media with faces pulled from videos on adult platforms like Pornhub.
In a Monday post on Weibo, the user, who says he’s based in Germany, claimed to have “successfully identified more than 100,000 young ladies” in the adult industry “on a global scale.”
To be clear, the user has posted no proof that he’s actually been able to do this, and hasn’t published any code, databases, or anything else besides an empty GitLab page to verify this is real. When Motherboard contacted the user over Weibo chat, he said they will release “database schema” and “technical details” next week, and did not comment further. Read More
The Collapsing Crime Rates of the ’90s Might Have Been Driven by Cellphones
It’s practically an American pastime to blame cellphones for all sorts of societal problems, from distracted parents to faltering democracies. But the devices might have also delivered a social silver lining: a de-escalation of the gang turf wars that tore up cities in the 1980s.
The intriguing new theory suggests that the arrival of mobile phones made holding territory less important, which reduced intergang conflict and lowered profits from drug sales. Read More
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