by jsendak | Jan 10, 2024 | Computer Science
Conversational Swarm Intelligence: Amplifying Group Intelligence
Conversational Swarm Intelligence (CSI) is an innovative communication technology that has the potential to revolutionize the way large groups deliberate and make decisions. This pilot study focused on the application of CSI in the context of selecting players for a weekly Fantasy Football contest. The results of the study are extremely promising and suggest that CSI can significantly amplify the intelligence of groups engaged in real-time conversational deliberation.
The concept of CSI is inspired by the dynamics of biological swarms, where large groups of individuals work together to achieve collective goals. In the case of CSI, networked groups consisting of 25 to 2500 people engage in real-time text-chat deliberation using a platform called Thinkscape. This technology combines the benefits of small-group reasoning with the collective intelligence advantages of large-groups.
To compare the effectiveness of CSI with traditional decision-making methods, participants in the pilot study were divided into two groups. The first group completed a survey to record their player selections individually, while the second group used CSI to collaboratively select sets of players. The results were quite remarkable:
- The real-time conversational group using CSI outperformed 66% of survey participants
- The CSI method significantly outperformed the most popular choices from the survey (the Wisdom of Crowd)
These findings indicate that CSI has the potential to enhance decision-making outcomes compared to individual decision-making or relying solely on popular choices obtained through surveys. The amplification of intelligence observed through CSI is a significant step forward in harnessing the collective wisdom of large groups.
This pilot study’s results provide intriguing insights into the possibilities offered by CSI technology. With further research and development, CSI could potentially be applied to a wide range of domains and industries, such as business strategy, policy-making, and problem-solving. The prospect of collective superintelligence, where groups can achieve intelligence levels beyond what any single individual could achieve, is both exciting and promising.
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by jsendak | Jan 10, 2024 | Computer Science
There has been a growing interest in the task of generating sound for silent
videos, primarily because of its practicality in streamlining video
post-production. However, existing methods for video-sound generation attempt
to directly create sound from visual representations, which can be challenging
due to the difficulty of aligning visual representations with audio
representations. In this paper, we present SonicVisionLM, a novel framework
aimed at generating a wide range of sound effects by leveraging vision language
models. Instead of generating audio directly from video, we use the
capabilities of powerful vision language models (VLMs). When provided with a
silent video, our approach first identifies events within the video using a VLM
to suggest possible sounds that match the video content. This shift in approach
transforms the challenging task of aligning image and audio into more
well-studied sub-problems of aligning image-to-text and text-to-audio through
the popular diffusion models. To improve the quality of audio recommendations
with LLMs, we have collected an extensive dataset that maps text descriptions
to specific sound effects and developed temporally controlled audio adapters.
Our approach surpasses current state-of-the-art methods for converting video to
audio, resulting in enhanced synchronization with the visuals and improved
alignment between audio and video components. Project page:
https://yusiissy.github.io/SonicVisionLM.github.io/
Analysis: SonicVisionLM – Generating Sound for Silent Videos
Generating sound for silent videos has gained significant interest in recent years due to its practicality in streamlining video post-production. However, existing methods face challenges in aligning visual representations with audio representations. In this paper, the authors propose SonicVisionLM, a novel framework that leverages vision language models (VLMs) to generate a wide range of sound effects.
The adoption of VLMs in SonicVisionLM represents a multi-disciplinary approach that combines computer vision and natural language processing. By using VLMs, the framework is able to identify events within a silent video and suggest relevant sounds that match the visual content. This shift in approach simplifies the complex task of aligning image and audio, transforming it into more well-studied sub-problems of aligning image-to-text and text-to-audio.
These sub-problems are addressed through the use of diffusion models, which have been widely used in the field of multimedia information systems. Diffusion models facilitate the process of converting text descriptions into specific sound effects. Additionally, the authors have developed temporally controlled audio adapters to improve the quality of audio recommendations with VLMs. This integration of different techniques enhances the overall synchronization between audio and video components.
With the proposed SonicVisionLM framework, the authors have surpassed current state-of-the-art methods for converting video to audio. They have achieved enhanced synchronization with visuals and improved alignment between audio and video components. By utilizing VLMs and diffusion models, the framework demonstrates the potential of combining various disciplines to advance the field of multimedia information systems. This research opens up possibilities for further exploration and development of advanced techniques in animations, artificial reality, augmented reality, and virtual realities.
For more details and access to the project page, please visit: https://yusiissy.github.io/SonicVisionLM.github.io/
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by jsendak | Jan 10, 2024 | Computer Science
Cyber-attacks pose an escalating threat to global networks and information infrastructures, with their increasing destructiveness and the difficulty of countering them. To address this urgent need for more sophisticated cyber security methods and techniques, this paper proposes a multidisciplinary remote cognitive observation technique. By combining Cognitive Psychology and Artificial Intelligence (AI), this method offers a non-traditional approach to identify threats and can be incorporated into the design of cyber security systems. The innovative aspect lies in the ability to remotely access cognitive behavioral parameters of intruders/hackers through online connections, without the need for physical contact or regard for geographical distance.
The ultimate objective of this research is to develop a supplementary cognitive cyber security tool for next-generation secure online banking, finance, or trade systems. With the exponential growth of global networks, there is a pressing need to enhance security countermeasures. The traditional methods in use are proving insufficient in the face of emerging threats.
Analysis
The proposed multidisciplinary approach addresses the limitations of current cyber security methods by incorporating Cognitive Psychology and AI. This combination allows for a deeper understanding of the thought processes and behavioral patterns of hackers or intruders. By remotely accessing these cognitive parameters, security professionals can gain critical insights into potential threats without directly engaging with the perpetrators.
This approach holds promise for future cyber security systems, particularly in the realms of online banking, finance, and trade. These sectors handle sensitive information and financial transactions on a global scale, making them prime targets for cyber-attacks. By supplementing existing security measures with a cognitive cyber security tool, organizations can strengthen their defenses against sophisticated hacking attempts.
However, there are potential challenges to consider. Remote cognitive observation requires a high level of expertise in both Cognitive Psychology and AI. Implementing such a technique on a large scale may require significant investment in training personnel and infrastructure. Additionally, there are ethical implications to consider when monitoring the cognitive behavior of individuals without their consent or knowledge.
Future Implications
The introduction of a remote cognitive observation technique opens up new avenues for research and innovation in the field of cyber security. Further studies can explore the effectiveness of this approach in real-world scenarios and refine the methodology to address potential limitations.
In the long term, advancements in AI technology, such as machine learning and natural language processing, can augment the capabilities of the proposed method. These improvements could enable the system to autonomously detect and respond to threats, reducing human intervention and enhancing the overall security posture.
Conclusion
The multidisciplinary remote cognitive observation technique presented in this paper offers a fresh perspective on cyber security by incorporating Cognitive Psychology and AI. By remotely accessing an intruder’s cognitive parameters, security professionals can gain valuable insights without physical contact or geographical limitations. While there are challenges to overcome and ethical considerations to address, this approach holds promise for developing supplementary cyber security tools for secure online banking, finance, or trade systems. As the global network expands, it becomes imperative to explore innovative methodologies like this to tackle evolving cyber threats effectively.
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by jsendak | Jan 10, 2024 | Computer Science
Automatically understanding funny moments (i.e., the moments that make people
laugh) when watching comedy is challenging, as they relate to various features,
such as body language, dialogues and culture. In this paper, we propose
FunnyNet-W, a model that relies on cross- and self-attention for visual, audio
and text data to predict funny moments in videos. Unlike most methods that rely
on ground truth data in the form of subtitles, in this work we exploit
modalities that come naturally with videos: (a) video frames as they contain
visual information indispensable for scene understanding, (b) audio as it
contains higher-level cues associated with funny moments, such as intonation,
pitch and pauses and (c) text automatically extracted with a speech-to-text
model as it can provide rich information when processed by a Large Language
Model. To acquire labels for training, we propose an unsupervised approach that
spots and labels funny audio moments. We provide experiments on five datasets:
the sitcoms TBBT, MHD, MUStARD, Friends, and the TED talk UR-Funny. Extensive
experiments and analysis show that FunnyNet-W successfully exploits visual,
auditory and textual cues to identify funny moments, while our findings reveal
FunnyNet-W’s ability to predict funny moments in the wild. FunnyNet-W sets the
new state of the art for funny moment detection with multimodal cues on all
datasets with and without using ground truth information.
FunnyNet-W: Exploiting Multimodal Cues for Funny Moment Detection in Videos
Understanding humor and what makes people laugh is a complex task that involves several factors, including body language, dialogues, and cultural references. In the field of multimedia information systems, detecting funny moments in videos has been a challenge due to the multi-disciplinary nature of the concept. However, a recent paper introduces a groundbreaking model called FunnyNet-W, which leverages cross- and self-attention mechanisms to predict funny moments using visual, audio, and text data.
The unique aspect of FunnyNet-W is its reliance on modalities naturally present in videos, rather than relying on ground truth data like subtitles. The model utilizes video frames to capture visual information critical for scene understanding. Additionally, it leverages audio cues associated with funny moments, such as intonation, pitch, and pauses. Text data extracted using a speech-to-text model is also processed by a Large Language Model to extract valuable information. By combining these modalities, FunnyNet-W aims to accurately identify and predict funny moments in videos.
The paper also introduces an unsupervised approach for acquiring labels to train FunnyNet-W. This approach involves spotting and labeling funny audio moments. By doing so, the model can learn from real-life instances of humor rather than relying solely on pre-defined annotations.
To evaluate the performance of FunnyNet-W, the researchers conducted experiments on five datasets, including popular sitcoms like The Big Bang Theory (TBBT), Modern Family (MHD), and Friends, as well as the TED talk dataset UR-Funny. The comprehensive experiments and analysis showed that FunnyNet-W successfully utilizes visual, auditory, and textual cues to identify funny moments. Moreover, the results demonstrate FunnyNet-W’s ability to predict funny moments in diverse and uncontrolled video environments.
From a broader perspective, this research contributes to the field of multimedia information systems by showcasing the effectiveness of combining multiple modalities for humor detection. FunnyNet-W’s reliance on visual, audio, and textual data highlights the multi-disciplinary nature of understanding funny moments in videos. By incorporating insights from computer vision, audio processing, and natural language processing, this model represents a step forward in multimodal analysis.
Furthermore, the concepts presented in FunnyNet-W have implications beyond just humor detection. The model’s ability to leverage multiple modalities opens up possibilities for applications in various domains. For example, this approach could be utilized in animations to automatically identify comedic moments and enhance the viewer’s experience. Additionally, the integration of visual, audio, and textual cues can also be valuable for improving virtual reality and augmented reality systems, where realistic and immersive experiences rely on multimodal input.
In conclusion, FunnyNet-W establishes a new state-of-the-art for funny moment detection by effectively exploiting multimodal cues across various datasets. This research not only advances our understanding of humor detection but also demonstrates the power of combining visual, audio, and textual information in the wider context of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Jan 10, 2024 | Computer Science
Expert Commentary: Exploring the Role of Laser Technology in Wearable Device Fabrication
Wearable technology has witnessed a significant surge in popularity, particularly in the areas of personal healthcare and smart VR/AR applications. This has led to a pressing need for the development of efficient fabrication methods that can cater to the demands of these emerging technologies. Laser technology, with its unique properties of remote, sterile, rapid, and site-selective processing, has emerged as a leading solution in this field. In this review, we will explore recent developments in laser processes for wearable device fabrication and analyze their implications for the future.
Transformative Approaches: Laser-Induced Graphene (LIG)
Laser-induced graphene (LIG) stands out as a transformative approach in the realm of wearable device fabrication. LIG offers not only design optimization and alteration possibilities for native substrates but also enables the creation of more complex material compositions and multilayer device configurations. The ability to simultaneously transform heterogeneous precursors or sequentially add functional layers and electronic elements opens up exciting avenues for creating advanced wearable devices with enhanced functionalities.
Conventional Laser Techniques: Ablation, Sintering, and Synthesis
In addition to transformative approaches like LIG, conventional laser techniques such as ablation, sintering, and synthesis continue to play a vital role in enhancing the functionality of wearable devices. These techniques enable the expansion of applicable materials, making it possible to incorporate new mechanisms and components into wearable device designs. By leveraging these techniques, researchers have successfully developed various wearable device components, with a particular focus on chemical/physical sensors and energy devices.
All-Laser Fabrication: Multiple Laser Sources and Processes
One intriguing development in the field of laser-based wearable device fabrication is the exploration of all-laser fabrication methods. Researchers are now exploring the potential of utilizing multiple laser sources and processes to streamline the fabrication process. This approach holds immense promise as it offers a way to simplify the manufacturing pipeline and achieve a more efficient and scalable production of wearable devices.
In conclusion, laser technology has established its prominence in the realm of wearable device fabrication. The transformative approach of laser-induced graphene, coupled with conventional laser techniques, has enabled the creation of highly functional wearable devices with diverse applications. The ongoing exploration of all-laser fabrication methods further holds the potential to revolutionize the manufacturing process and drive the rapid advancement of wearable technology.
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by jsendak | Jan 10, 2024 | Computer Science
Audio and video are two most common modalities in the mainstream media
platforms, e.g., YouTube. To learn from multimodal videos effectively, in this
work, we propose a novel audio-video recognition approach termed audio video
Transformer, AVT, leveraging the effective spatio-temporal representation by
the video Transformer to improve action recognition accuracy. For multimodal
fusion, simply concatenating multimodal tokens in a cross-modal Transformer
requires large computational and memory resources, instead we reduce the
cross-modality complexity through an audio-video bottleneck Transformer. To
improve the learning efficiency of multimodal Transformer, we integrate
self-supervised objectives, i.e., audio-video contrastive learning, audio-video
matching, and masked audio and video learning, into AVT training, which maps
diverse audio and video representations into a common multimodal representation
space. We further propose a masked audio segment loss to learn semantic audio
activities in AVT. Extensive experiments and ablation studies on three public
datasets and two in-house datasets consistently demonstrate the effectiveness
of the proposed AVT. Specifically, AVT outperforms its previous
state-of-the-art counterparts on Kinetics-Sounds by 8%. AVT also surpasses one
of the previous state-of-the-art video Transformers [25] by 10% on VGGSound by
leveraging the audio signal. Compared to one of the previous state-of-the-art
multimodal methods, MBT [32], AVT is 1.3% more efficient in terms of FLOPs and
improves the accuracy by 3.8% on Epic-Kitchens-100.
In this article, the authors propose a novel approach called audio video Transformer (AVT) to effectively learn from multimodal videos. They aim to improve action recognition accuracy by leveraging the spatio-temporal representation provided by the video Transformer. However, instead of simply concatenating multimodal tokens in a cross-modal Transformer, they introduce an audio-video bottleneck Transformer to reduce computational and memory resources required for multimodal fusion.
One interesting aspect of this approach is the integration of self-supervised objectives into AVT training. This includes audio-video contrastive learning, audio-video matching, and masked audio and video learning. By mapping diverse audio and video representations into a common multimodal representation space, they enhance the learning efficiency of the multimodal Transformer.
The authors also propose a masked audio segment loss to specifically learn semantic audio activities in AVT. This is a valuable addition as it allows for more nuanced understanding of the audio component in multimodal videos.
The experimental results and ablation studies conducted on various datasets show the effectiveness of AVT. It outperforms previous state-of-the-art approaches on Kinetics-Sounds by 8% and on VGGSound by 10% by leveraging the audio signal. Additionally, compared to a previous multimodal method called MBT, AVT is more efficient in terms of FLOPs and improves accuracy by 3.8% on Epic-Kitchens-100.
This work demonstrates the multi-disciplinary nature of multimedia information systems and its intersection with concepts such as animations, artificial reality, augmented reality, and virtual realities. The effective recognition and understanding of audio and video content in multimodal videos have significant implications in various fields, including entertainment, education, healthcare, and communication.
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