The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time video analytics, smart sensing, Extended Reality (XR), etc., necessitates reliable wireless connectivity in indoor environments. In such environments, accurate design of Radio Environment Maps (REMs) enables adaptive wireless network planning and optimization of Access Point (AP) placement. However, generating realistic REMs remains difficult due to the variability of indoor environments and the limitations of existing modeling approaches, which often rely on simplified layouts or fully synthetic data. These challenges are further amplified by the adoption of next-generation Wi-Fi standards, which operate at higher frequencies and suffer from limited range and wall penetration. To support the efforts in addressing these challenges, we collected a dataset that combines high-resolution 3D LiDAR scans with Wi-Fi RSSI measurements collected across 20 setups in a multi-room indoor environment. The dataset includes two measurement scenarios, the first without human presence in the environment, and the second with human presence, enabling the development and validation of REM estimation models that incorporate physical geometry and environmental dynamics. The described dataset supports research in data-driven wireless modeling and the development of high-capacity indoor communication networks.

Safe and trustworthy Human Robot Interaction (HRI) requires robots not only to complete tasks but also to regulate impedance and speed according to scene context and human proximity. We present SafeHumanoid, an egocentric vision pipeline that links Vision Language Models (VLMs) with Retrieval-Augmented Generation (RAG) to schedule impedance and velocity parameters for a humanoid robot. Egocentric frames are processed by a structured VLM prompt, embedded and matched against a curated database of validated scenarios, and mapped to joint-level impedance commands via inverse kinematics. We evaluate the system on tabletop manipulation tasks with and without human presence, including wiping, object handovers, and liquid pouring. The results show that the pipeline adapts stiffness, damping, and speed profiles in a context-aware manner, maintaining task success while improving safety. Although current inference latency (up to 1.4 s) limits responsiveness in highly dynamic settings, SafeHumanoid demonstrates that semantic grounding of impedance control is a viable path toward safer, standard-compliant humanoid collaboration.

Recent advances in vision-language models (VLMs) have enabled impressive multi-modal reasoning and understanding. Yet, whether these models truly grasp visual persuasion-how visual cues shape human attitudes and decisions-remains unclear. To probe this question, we construct a high-consensus dataset for binary persuasiveness judgment and introduce the taxonomy of Visual Persuasive Factors (VPFs), encompassing low-level perceptual, mid-level compositional, and high-level semantic cues. We also explore cognitive steering and knowledge injection strategies for persuasion-relevant reasoning. Empirical analysis across VLMs reveals a recall-oriented bias-models over-predict high persuasiveness-and weak discriminative power for low/mid-level features. In contrast, high-level semantic alignment between message and object presence emerges as the strongest predictor of human judgment. Among intervention strategies, simple instruction or unguided reasoning scaffolds yield marginal or negative effects, whereas concise, object-grounded rationales significantly improve precision and F1 scores. These results indicate that VLMs core limitation lies not in recognizing persuasive objects but in linking them to communicative intent.

Long-term planning for robots operating in domestic environments poses unique challenges due to the interactions between humans, objects, and spaces. Recent advancements in trajectory planning have leveraged vision-language models (VLMs) to extract contextual information for robots operating in real-world environments. While these methods achieve satisfying performance, they do not explicitly model human activities. Such activities influence surrounding objects and reshape spatial constraints. This paper presents a novel approach to trajectory planning that integrates human preferences, activities, and spatial context through an enriched 3D scene graph (3DSG) representation. By incorporating activity-based relationships, our method captures the spatial impact of human actions, leading to more context-sensitive trajectory adaptation. Preliminary results demonstrate that our approach effectively assigns costs to spaces influenced by human activities, ensuring that the robot trajectory remains contextually appropriate and sensitive to the ongoing environment. This balance between task efficiency and social appropriateness enhances context-aware human-robot interactions in domestic settings. Future work includes implementing a full planning pipeline and conducting user studies to evaluate trajectory acceptability.

Device-free human presence detection is a crucial technology for various applications, including home automation, security, and healthcare. While camera-based systems have traditionally been used for this purpose, they raise privacy concerns. To address this issue, recent research has explored the use of wireless channel state information (CSI) extracted from commercial WiFi access points (APs) to provide detailed channel characteristics. In this paper, we propose a device-free human presence detection system for multi-room scenarios using a time-selective conditional dual feature extract recurrent network (TCD-FERN). Our system is designed to capture significant time features on current human features using a dynamic and static data preprocessing technique. We extract both moving and spatial features of people and differentiate between line-of-sight (LoS) and non-line-of-sight (NLoS) cases. Subcarrier fusion is carried out in order to provide more objective variation of each sample while reducing the computational complexity. A voting scheme is further adopted to mitigate the feature attenuation problem caused by room partitions, with around 3% improvement of human presence detection accuracy. Experimental results have revealed the significant improvement of leveraging subcarrier fusion, dual-feature recurrent network, time selection and condition mechanisms. Compared to the existing works in open literature, our proposed TCD-FERN system can achieve above 97% of human presence detection accuracy for multi-room scenarios with the adoption of fewer WiFi APs.

Coolant leaks, space debris collisions and unplanned engine thrusts are just some of the unexpected challenges astronauts aboard the International Space Station must overcome. NASA intends to build civilian housing on the lunar surface using 3D-printing robots within two decades, according to several of the organization's scientists. The agency is developing concepts for lunar rocket landing pads, 3D printers, concrete mixtures, construction robots and more to complete structures that would shelter humans on the moon by 2040, according to the New York Times. NASA plans to send a construction robot to the moon, which will use mineral fragments, dust and lunar concrete from the moon's surface to build the dwellings. The workroom inside of NASA's 3D printed Crew Health and Performance Exploration Analog habitat built by ICON.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The oldest direct evidence of human presence in the Americas are likely fossilized human footprints found in New Mexico, challenging once-conventional wisdom regarding humans migrating to the New World from Russia roughly 15,000 years ago, new research confirms. The new discovery suggests that the first people actually arrived in the Americas much earlier than previously believed. According to research published Thursday in the journal Science, footprints discovered at the edge of an ancient lake bed in White Sands National Park date back to between 21,000 and 23,000 years ago.

In recent years, indoor human presence detection based on supervised learning (SL) and channel state information (CSI) has attracted much attention. However, existing studies that rely on spatial information of CSI are susceptible to environmental changes which degrade prediction accuracy. Moreover, SL-based methods require time-consuming data labeling for retraining models. Therefore, it is imperative to design a continuously monitored model using a semi-supervised learning (SSL) based scheme. In this paper, we conceive a bifold teacher-student (BTS) learning approach for indoor human presence detection in an adjoining two-room scenario. The proposed SSL-based primal-dual teacher-student network intelligently learns spatial and temporal features from labeled and unlabeled CSI datasets. Additionally, the enhanced penalized loss function leverages entropy and distance measures to distinguish drifted data, i.e., features of new datasets affected by time-varying effects and altered from the original distribution. Experimental results demonstrate that the proposed BTS system sustains asymptotic accuracy after retraining the model with unlabeled data. Furthermore, BTS outperforms existing SSL-based models in terms of the highest detection accuracy while achieving the asymptotic performance of SL-based methods.

The hesitation previously expressed is that algorithms can be discriminatory, cementing in past biases in an organization's hiring patterns and excluding the possibility for new approaches and greater diversity in types of recruits. This is clearly a problem--though one that can be countered by careful management of the process and designing better algorithms. A recent study, from Megan Fritts of the University of Arkansas and Frank Cabrera University of Wisconsin–Madison, considers another problem--which has received little attention in debates about the ethics of algorithms--that the use of recruitment algorithms will lead to a'dehumanization' of the hiring process and in so doing can negatively impact employee-employer relationships. Algorithms used for sifting through thousands of resumés may exaggerate biases but can hardly be said to be very dehumanizing. Problems really occur when AI-based assessment tools are used to analyze video interviews, or algorithms influence the final selection by recommending the best candidates from the remaining pool.

Advances in cloud computing, AI and sensors are combining to offer insurers new, better variables to characterize occupancy risk in buildings. Commercial general liability insurers traditionally estimate business risk exposure of similar businesses based on variables like floor area and revenue. Advances in cloud computing and artificial intelligence are combining to offer insurers new, better variables to characterize risk. Insurers generally understand that liability risk correlates to human presence and movement. A hair salon with twice the foot traffic should present twice the slip-and-fall risk.