Video processing and analysis have become an urgent task since a huge amount of videos (e.g., Youtube, Hulu) are uploaded online every day. The extraction of representative key frames from videos is very important in video processing and analysis since it greatly reduces computing resources and time. Although great progress has been made recently, large-scale video classification remains an open problem, as the existing methods have not well balanced the performance and efficiency simultaneously. To tackle this problem, this work presents an unsupervised method to retrieve the key frames, which combines Convolutional Neural Network (CNN) and Temporal Segment Density Peaks Clustering (TSDPC). The proposed TSDPC is a generic and powerful framework and it has two advantages compared with previous works, one is that it can calculate the number of key frames automatically. The other is that it can preserve the temporal information of the video. Thus it improves the efficiency of video classification. Furthermore, a Long Short-Term Memory network (LSTM) is added on the top of the CNN to further elevate the performance of classification. Moreover, a weight fusion strategy of different input networks is presented to boost the performance. By optimizing both video classification and key frame extraction simultaneously, we achieve better classification performance and higher efficiency. We evaluate our method on two popular datasets (i.e., HMDB51 and UCF101) and the experimental results consistently demonstrate that our strategy achieves competitive performance and efficiency compared with the state-of-the-art approaches.

Most of us love to have a bit of a boogie, and some - but not all - can also keep to the beat! It turns out we're not alone in that, as a new study has found that rats can nod their heads in time to music. Researchers from the University of Tokyo played the rodents clips of Lady Gaga, Queen and Michael Jackson as well as a Mozart Sonata at four different tempos. Any bopping was recorded both on camera and with a miniature sensor strapped to their heads. 'Rats displayed innate - that is, without any training or prior exposure to music - beat synchronization most distinctly within 120-140 beats per minute (bpm),' said Associate Professor Hirokazu Takahashi.

The market report gives a worldwide view of Data Science and Machine Learning Service market.

Using high-throughput genome sequencing and machine learning, scientists at UMass Chan Medical School have shown a strong correlation between the …

Hankuk University of Foreign Studies (HUFS) Researchers Describe New Findings in Machine Learning (Improving insurers’ loss reserve error prediction: …

In this paper, we present a novel approach to adapt a sequence-to-sequence Transformer-Transducer ASR system to the keyword spotting (KWS) task. We achieve this by replacing the keyword in the text transcription with a special token and training the system to detect the token in an audio stream. At inference time, we create a decision function inspired by conventional KWS approaches, to make our approach more suitable for the KWS task. Furthermore, we introduce a specific keyword spotting loss by adapting the sequence-discriminative Minimum Bayes-Risk training technique. We find that our approach significantly outperforms ASR based KWS systems. When compared with a conventional keyword spotting system, our proposal has similar performance while bringing the advantages and flexibility of sequence-to-sequence training. Additionally, when combined with the conventional KWS system, our approach can improve the performance at any operation point.

Dark patterns, which are user interface designs in online services, induce users to take unintended actions. Recently, dark patterns have been raised as an issue of privacy and fairness. Thus, a wide range of research on detecting dark patterns is eagerly awaited. In this work, we constructed a dataset for dark pattern detection and prepared its baseline detection performance with state-of-the-art machine learning methods. The original dataset was obtained from Mathur et al.'s study in 2019, which consists of 1,818 dark pattern texts from shopping sites. Then, we added negative samples, i.e., non-dark pattern texts, by retrieving texts from the same websites as Mathur et al.'s dataset. We also applied state-of-the-art machine learning methods to show the automatic detection accuracy as baselines, including BERT, RoBERTa, ALBERT, and XLNet. As a result of 5-fold cross-validation, we achieved the highest accuracy of 0.975 with RoBERTa. The dataset and baseline source codes are available at https://github.com/yamanalab/ec-darkpattern.

I finally got the opportunity to watch Free Guy recently. Before I go any further, there are probably going to be spoilers here. The further we get from the original release date, the less that will matter. However, there may be some people who still haven't seen this movie and don't want it spoiled. I will say it was a good movie that could have been better. I enjoy movies in this genre.

Artificial Intelligence is for long journeys, quiet nights and club drowsy dawns. Listen with an open mind." Back in 1992 when Warp released the Artificial Intelligence compilation it almost instantly changed both the course of Warp as a label and arguably what many would consider "club" music as an entity to be. Artificial Intelligence came housed inside a prog rock styled gatefold sleeve depicting a cover image of a robot blowing smoke rings whilst reclining on an armchair. Its extra long rolling papers and tin of tobacco just out of reach, whilst a high-end stereo plays out the sounds of Kraftwerk's Autobahn and Pink Floyd's Dark Side Of The Moon, their LP sleeves lay strewn across the floor. This image along with the above text that as printed on the sleeve acted as a guide for the listener on how to best experience this new mode of techno music, one that was designed for those nights when your body stays in but your mind steps out. Having been in operation for three years by the time they compiled and released the Artificial Intelligence compilation, Warp had already proved itself as a worthy force within the world of quickfire 12" singles of acid house and the emerging hardcore scene.

Detroit, Michigan, Nov. 02, 2022 (GLOBE NEWSWIRE) -- Artificial Intelligence Technology Solutions, Inc., (the Company) (OTCPK:AITX), has announced that on October 28, 2022 it issued a $4 million note to its largest single investor thereby securing a loan that matures in 4 years, bears interest at 15% per annum, has an original issue discount of $500,000, provides cash proceeds to the Company of $3.5 million, and includes warrants to acquire additional preferred equity shares (the "Fundraise"). The net effect of the Fundraise does not materially affect the Company's common stock shareholders or common shareholders' equity percentage since Steve Reinharz, AITX Founder and CEO, has effectively reduced his stake by approximately 20% (from fully diluted ownership of 65% to 54%) to achieve the funding without any further dilution to common shareholders. Steve Reinharz commented, "My commitment has always been to make AITX along with its RAD subsidiaries the dominant player in the evolving #proptech industry, which we feel we helped write the book on. This funding is crucial to the Company and will allow us to continue to grow while adding potential value to all stakeholders." Reinharz continued, "Certainly, I don't love reducing my overall stake, but the way this deal issues preferred shares and doesn't affect common shareholders, is a significant demonstration of my commitment to the Company's mission and to our shareholders."