The idea to replicate in a machine human consciousness,understood as an immediate first-person subjective experience,becomes one of the greatest challenges of our time.Scientific understanding of the nature of phenome...The idea to replicate in a machine human consciousness,understood as an immediate first-person subjective experience,becomes one of the greatest challenges of our time.Scientific understanding of the nature of phenomenal consciousness may require an expansion of the scientific method itself.While this resolution is postponed,the task of reproducing the consciousness phenomenon in a computer should be approached within the framework of functionalism.Proposed so far theories of this sort appear problematic.An approach to implementing humanlike consciousness is proposed here based on BICA(biologically inspired cognitive architectures)combined with LLM(large language models).The concept is illustrated by example of a virtual tutor.It is argued that final solutions can be obtained in a purely neuromorphic form,which will enable their further adaptation,growth,and evolution.We start from discussion of the ontological problem of consciousness and its temporary nonreductive solution,which makes a scientific study possible.The main hypothesis for the proposed study is formulated based on the BICA Challenge in terms of the eBICA cognitive architecture.Only the limited case of socially emotional consciousness is analyzed as example.It is explained how LLM,being not conscious on their own,accessible via ChatGPT,can be connected to eBICA,used to build a conscious prototype and validate its claimed property.Selected example of a virtual tutor further illustrates how this technology will allow the tutor to develop human-level teacher-student relationships that will increase the efficiency of tutoring.Future prospects for the proposed direction of research include a variety of sentient agents to be created for the many practical human needs with the possibility of their further autonomous evolution.展开更多
Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artific...Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artificial intelligence(AI)have enabled the development of systems capable of mental health monitoring using multimodal data.However,existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings.This paper addresses these challenges by proposing TRANS-HEALTH,a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention(BDI)reasoning for real-time psychological distress detection.The framework utilizes a multimodal dataset containing EEG,GSR,heart rate,and activity data to predict distress while adapting to individual contexts.The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making.The novelty of the approach lies in its seamless integration of transformermodelswith BDI reasoning,providing both high accuracy and contextual relevance in real time.Performance metrics such as accuracy,precision,recall,and F1-score are employed to evaluate the system’s performance.The results show that TRANS-HEALTH outperforms existing models,achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts,with an enhanced ability to engage users,making it suitable for deployment in wearable and remote healthcare environments.展开更多
The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in clo...The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in cloud environments.However,reliance on cloud infrastructure raises critical security challenges,particularly regarding data integrity.While existing cryptographic methods provide robust integrity verification,they impose significant computational and energy overheads on resource-constrained IoT devices,limiting their applicability in large-scale,real-time scenarios.To address these challenges,we propose the Cognitive-Based Integrity Verification Model(C-BIVM),which leverages Belief-Desire-Intention(BDI)cognitive intelligence and algebraic signatures to enable lightweight,efficient,and scalable data integrity verification.The model incorporates batch auditing,reducing resource consumption in large-scale IoT environments by approximately 35%,while achieving an accuracy of over 99.2%in detecting data corruption.C-BIVM dynamically adapts integrity checks based on real-time conditions,optimizing resource utilization by minimizing redundant operations by more than 30%.Furthermore,blind verification techniques safeguard sensitive IoT data,ensuring privacy compliance by preventing unauthorized access during integrity checks.Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40%compared to traditional bilinear pairing-based methods,making it particularly suitable for IoT-driven applications in smart cities,healthcare,and beyond.These results underscore the effectiveness of C-BIVM in delivering a secure,scalable,and resource-efficient solution tailored to the evolving needs of IoT ecosystems.展开更多
基金Supported by Russian Science Foundation(22-11-00213).
文摘The idea to replicate in a machine human consciousness,understood as an immediate first-person subjective experience,becomes one of the greatest challenges of our time.Scientific understanding of the nature of phenomenal consciousness may require an expansion of the scientific method itself.While this resolution is postponed,the task of reproducing the consciousness phenomenon in a computer should be approached within the framework of functionalism.Proposed so far theories of this sort appear problematic.An approach to implementing humanlike consciousness is proposed here based on BICA(biologically inspired cognitive architectures)combined with LLM(large language models).The concept is illustrated by example of a virtual tutor.It is argued that final solutions can be obtained in a purely neuromorphic form,which will enable their further adaptation,growth,and evolution.We start from discussion of the ontological problem of consciousness and its temporary nonreductive solution,which makes a scientific study possible.The main hypothesis for the proposed study is formulated based on the BICA Challenge in terms of the eBICA cognitive architecture.Only the limited case of socially emotional consciousness is analyzed as example.It is explained how LLM,being not conscious on their own,accessible via ChatGPT,can be connected to eBICA,used to build a conscious prototype and validate its claimed property.Selected example of a virtual tutor further illustrates how this technology will allow the tutor to develop human-level teacher-student relationships that will increase the efficiency of tutoring.Future prospects for the proposed direction of research include a variety of sentient agents to be created for the many practical human needs with the possibility of their further autonomous evolution.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artificial intelligence(AI)have enabled the development of systems capable of mental health monitoring using multimodal data.However,existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings.This paper addresses these challenges by proposing TRANS-HEALTH,a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention(BDI)reasoning for real-time psychological distress detection.The framework utilizes a multimodal dataset containing EEG,GSR,heart rate,and activity data to predict distress while adapting to individual contexts.The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making.The novelty of the approach lies in its seamless integration of transformermodelswith BDI reasoning,providing both high accuracy and contextual relevance in real time.Performance metrics such as accuracy,precision,recall,and F1-score are employed to evaluate the system’s performance.The results show that TRANS-HEALTH outperforms existing models,achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts,with an enhanced ability to engage users,making it suitable for deployment in wearable and remote healthcare environments.
基金supported by King Saud University,Riyadh,Saudi Arabia,through Researchers Supporting Project number RSP2025R498.
文摘The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in cloud environments.However,reliance on cloud infrastructure raises critical security challenges,particularly regarding data integrity.While existing cryptographic methods provide robust integrity verification,they impose significant computational and energy overheads on resource-constrained IoT devices,limiting their applicability in large-scale,real-time scenarios.To address these challenges,we propose the Cognitive-Based Integrity Verification Model(C-BIVM),which leverages Belief-Desire-Intention(BDI)cognitive intelligence and algebraic signatures to enable lightweight,efficient,and scalable data integrity verification.The model incorporates batch auditing,reducing resource consumption in large-scale IoT environments by approximately 35%,while achieving an accuracy of over 99.2%in detecting data corruption.C-BIVM dynamically adapts integrity checks based on real-time conditions,optimizing resource utilization by minimizing redundant operations by more than 30%.Furthermore,blind verification techniques safeguard sensitive IoT data,ensuring privacy compliance by preventing unauthorized access during integrity checks.Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40%compared to traditional bilinear pairing-based methods,making it particularly suitable for IoT-driven applications in smart cities,healthcare,and beyond.These results underscore the effectiveness of C-BIVM in delivering a secure,scalable,and resource-efficient solution tailored to the evolving needs of IoT ecosystems.
文摘目的:观察加味柴胡汤联合帕罗西汀对抑郁症患者贝克抑郁量表(beck depression inventory,BDI)评分、血清神经元特异性烯醇化酶(neuronspecific enolase,NSE)水平、汉密尔顿抑郁量表(Hamilton depression scale,HAMD)评分及自杀意念自评量表(self-rating idea of suicide scale,SIOSS)评分的影响。方法:将108例抑郁症患者按照随机数字表法分为对照组和研究组各54例。对照组口服帕罗西汀,研究组在对照组基础上给予加味柴胡汤,治疗8周后比较两组疗效,并比较治疗前及治疗4、8周后BDI评分、血清NSE水平、HAMD评分、SIOSS评分及两组不良反应发生率。结果:研究组患者总有效率为92.59%(50/54),高于对照组的75.93%(41/54)(P<0.05)。治疗4、8周后两组患者HAMD、BDI、SIOSS评分及血清NSE水平较治疗前降低(P<0.05),研究组低于对照组(P<0.05)。两组患者不良反应发生率比较差异无统计学意义(P>0.05)。结论:加味柴胡汤联合帕罗西汀能有效缓解抑郁症患者抑郁状况,降低血清NSE水平,效果优于单用帕罗西汀。
