Reviewerly/exHarmony huggingface dataset:
Investigation of camera-free eye tracking glasses compared to a video-based system

Eye movements provide insight into human cognitive and sensorimotor control. However, most research has been constrained to traditional laboratories. Recent advances in eye tracking technology could facilitate research and application in ecological settings such as aviation training or sideline assessment. It is important to confirm the accuracy of the emerging systems. Here we compare a system consisting of camera-free eye tracking glasses with a research grade video-based system. Eleven healthy participants (22.6±2.8 years) were tested using a saccade task (target range 3-25°). Eye movements were recorded simultaneously using a video-based system (EyeLink-2, SR Research) and a portable eye/gaze tracking system (Mindlink, AdHawk Microsystems) at 250 Hz. Calibration for each system was performed separately following guidelines (accuracy error was <1deg). The head was stabilized in a chinrest throughout the experiment. Results showed a mean amplitude difference between the systems of 0.05°±2.05 and mean peak velocity difference of 28.98°/sec ±65.33. Analyses of the results revealed two important factors associated with larger errors between the systems. First, greater error was found in individuals wearing contact lenses (n=4) compared to non-contact lens wearers (n=7) (amplitude: 0.24°±2.13 vs -0.05°±2.00; peak velocity: 45.96°/sec ±66.6 vs 20.32°/sec ±62.94). Second, linear regression analysis revealed Mindlink calibration precision explained 50% of variance in amplitude difference, and 0.3% variance in peak velocity difference between the systems. The Mindlink system offers unprecedented advantage for recording of eye and gaze movements in ecological settings. Our study provides important insight regarding two factors that could contribute to significant errors in saccade amplitude and peak velocity: 1) errors may be higher in individuals wearing contact lenses; 2) in addition to accuracy, Mindlink’s precision calibration variable has a significant impact on the quality of the recording. These results should be considered when adopting the Mindlink for research applications.
Multi-dimensional Connection Remote Training Farmer College Students

Based on the needs of local economic development in Hubei Province, the learning needs of farmers and college students, the construction of rural distance education courses and the current situation of teachers' teaching, and based on the theory of "Connected learning", this research forms a multi-dimensional connected innovative teaching reform model of "multi-personnel participation, multi-dimensional spatial learning and multi-modal evaluation". Through the composition of the curriculum learning community, to solve the problem of teacher strength is not comprehensive; Use social media to create multiple teaching environment to solve the problem of inflexible learning style; The modularization and integrated design of the course can realize the integration of online teaching and management information, and solve the problem of single learning evaluation.
Federated Optimization Under Intermittent Client Availability

Federated learning is a new distributed machine learning framework, where a bunch of heterogeneous clients collaboratively train a model without sharing training data. In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, where the set of eligible clients may change during the training process. Such intermittent client availability would seriously deteriorate the performance of the classical Federated Averaging algorithm (FedAvg for short). Thus, we propose a simple distributed non-convex optimization algorithm, called Federated Latest Averaging (FedLaAvg for short), which leverages the latest gradients of all clients, even when the clients are not available, to jointly update the global model in each iteration. Our theoretical analysis shows that FedLaAvg attains the convergence rate of $O(E^{1/2}/(N^{1/4} T^{1/2}))$, achieving a sublinear speedup with respect to the total number of clients. We implement FedLaAvg along with several baselines and evaluate them over the benchmarking MNIST and Sentiment140 datasets. The evaluation results demonstrate that FedLaAvg achieves more stable training than FedAvg in both convex and non-convex settings and indeed reaches a sublinear speedup.
Penerapan Sistem Informasi Geografis (SIG) Dalam Mengidentifikasi Potensi Kelongsoran

The primary objective of this study was to enhance the community's understanding and awareness of landslide risks and mitigation strategies by employing Geographic Information Systems (GIS) to identify landslide-prone areas in the Magetan District, East Java. The initiative unfolded in several stages, which included: (1) emphasizing the importance of mapping areas susceptible to landslides; (2) consolidating data through satellite image analysis and GIS; (3) developing GIS-based maps that depict landslide-prone areas; and (4) circulating these maps among the local community and governing bodies. In conclusion, this study demonstrates that a participatory approach, combined with advanced technologies such as GIS, can play a pivotal role in disaster mitigation. Early identification of landslide risks and appropriate mitigation strategies using GIS and community education can significantly enhance community preparedness and resilience to landslides. The potential applicability of this method to other regions with similar topographical and geological conditions underscores its relevance in improving community readiness and resilience against landslide disasters.
Color Calibration in Virtual Reality Using Different Head Mounted Displays

Virtual Reality (VR) has emerged as a groundbreaking tool in science. It is particularly appealing because it allows realistic scenes to be created in a controlled manner. This is critical for a variety of research questions, especially those dealing with perception and action. Outside of science, VR today primarily occupies a niche in the gaming and entertainment markets, but it has the potential to rapidly evolve into new application areas such as business and social media, which will embed more and more real-world content into VR. Therefore, the need for realistic rendering will increase, both for scientific and non-scientific VR applications. A fundamental aspect of realistic rendering is accurate color reproduction, which in VR depends on the type of head-mounted display (HMD), the graphics engine, and the materials and shaders used. We performed a comparison between different HMDs (HTC Vive Pro Eye, Pimax 8KX DMAS and Varjo Aero), systematically testing the effects of the graphics engine (Unity and Unreal) and different materials and shaders (Unlit and Standard). Tone mapping was disabled to avoid related non-linearities. Color measurements were made using a Konica Minolta CS-2000A for RGBW in 8-bit increments of 5. In addition, 27 predefined and 50 random RGB values were measured for validation. Based on these measurements, we provide the transformation for each HMD, graphics engine, and material to convert from XYZ to RGB color space. Color reproduction after calibration was most accurate for the HTC Vive Pro Eye using Unreal with standard material (dE2000 mean error = 0.5). Furthermore, the HTC Vive Pro Eye offers the largest color gamut of all HMDs tested. In summary, we successfully performed color calibration with various combinations of HMDs, graphics engines, and materials and shaders, which allows for accurate color reproduction in VR.
Retinal Slip from Self Motion Modulates the Perceptibility of Jitter in World-Locked Augmented Reality

The human visual system is highly attuned to differentiate egocentric retinal velocities from those that arise from independently moving objects. Augmented reality (AR) head-mounted displays (HMDs) challenge these mechanisms by tracking the wearer’s head motion and projecting world-locked (WL) virtual content. Spatiotemporal artifacts can occur during WL rendering in HMDs, including high frequency random fluctuations in virtual content position (jitter). Perceptual sensitivity to AR WL jitter has been quantified for stationary observers (Wilmott et al., 2022). Importantly, this work did not account for the contributions of real-world references and imperfect retinal stabilizations (e.g., pursuit, saccades, vestibulo-ocular reflex). Here, we investigate how sensitivity to jitter varies as a function of observer movement and virtual content placement. We hypothesize (1) that as observers move their head and eyes during self motion, their sensitivity to added retinal velocity from jitter will decrease, and (2) that rendering virtual content near real-world surfaces will increase jitter sensitivity, given proximal veridical 3D cues. We measured sensitivity to added jitter on a textured cube (.2 m side-length) versus a no-jitter reference in three motion conditions: stationary, head rotation, and walking; and three virtual content placement conditions: floating, on a desk, and against a wall. Using a commercially available AR HMD with eye tracking (HoloLens 2), we asked participants to determine which cube moved more using a two-interval forced choice task. Psychometric thresholds indicated that participants were up to 3x less sensitive to added jitter during self motion compared to when they were stationary. To generalize beyond distinct user motion and content placement conditions, we also analyzed eye movement data. We found that the amount of retinal slip (i.e. how much gaze drifted across the object) could predict jitter thresholds better than measured head movements alone, suggesting a gaze-driven decrease in object motion sensitivity during self-motion.
Integrating users’ long-term and short-term interests with knowledge graph to improve restaurant recommendation

User preferences are typically analyzed by recommendation systems for recommending items. However, in most existing restaurant recommendation methods, recommendation similarity (e.g., reviews, sentiment) is considered only from the perspective of users, whereas restaurant attributes and potential connections between restaurants are ignored. Furthermore, the dining interests of users are diverse and not fixed. For improved recommendations, the changes of user interests should be considered. To address these problems, this study proposed a recommendation approach that combines knowledge graphs and long-term and short-term interests of users. In the approach, user preferences were analyzed from the attributes of restaurant using the semantic structure of knowledge graphs to obtain long-term user representations. Next, we filtered recent historical restaurants based on time series to obtain short-term user representations through the gated recurrent unit. To integrate long- and short-term interests, a novel gated structure was designed to obtain dynamic interest weights. Extensive experiments on a real-world dataset revealed that the proposed approach outperformed baseline methods.
Human visual sampling adaptation to the temporal structure of a dynamic environment

Many daily life situations require to monitor multiple locations to detect an event. When these events are unpredictable, some of the information sources that observers can use to guide their monitoring behaviour are the temporal properties of the environment, such as the duration of the events. We designed a task in which participants had to detect an event that could appear randomly at any of two locations. Participant could only observe one location at a time while the other was occluded, but they could switch between them whenever they wanted by pressing a key and waiting for a short delay without fully visualizing any of the locations. The duration of the event was different on each location, being three times longer in one location than in the other. We manipulated the duration of the delay when participants switched between locations (500, 750 or 1000 ms) and the duration of the events (500, 750 or 1000 ms for the short duration and 1500, 2250 or 3000 ms for the long duration respectively). We registered the monitoring pattern of each participant for every combination of variables by calculating the average looking time at each location before switching to the other. This pattern was then compared with an optimal observer model. Results show that participants are good at determining which location to prioritize, since they dedicated more looking time at the shorter duration event location and as short time as possible to the long duration event location. However, they adjusted this pattern suboptimally to changes of other variables, such as the relative duration of the events and the switch time. In conclusion, participants are able to integrate multiple duration intervals to plan an action pattern, but they seem to fail at incorporating the switch cost to their temporal monitoring pattern.
Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask–RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet–101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask–RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).
Comparative Analysis of Cloning-Hashing Applications for Securing Digital Evidence

The development of the Internet has resulted in an increasing variety of cyber crimes. Cybercrime is closely related to digital evidence, so cybercriminals tend to delete, hide, and format all collected data to eliminate traces of digital evidence. This digital evidence is very vital in proving at trial, so it is necessary to develop applications to secure digital evidence. This study aims to compare the results of cloning and hashing in securing digital evidence and evaluate the performance of a digital forensic application developed under the name Clon-Hash Application v1 compared to applications commonly used by investigators including Autopsy, FTK Imager, md5.exe in terms of its function, the result, CPU usage. The results of the research conducted show that the cloning process is perfectly successful, as evidenced by the hash value results which are the same as paid applications and there are even several other applications that have not been able to display the hash value. Hash values in the Clon-Hash v1 application also vary from MD5, SHA1, and SHA256 which do not exist in other applications. Applications developed are better in terms of function, results, and CPU usage.&#x0D;
Survey of temporal video enhancement techniques

Various fields such as self-driving, remote sensing and controls, robotics, and surveillance have videos as their primary source of information for proper functioning. To create error-free predictions in these applications, a high-quality video with a high frame rate is a necessity. Post-processing of the raw video feed is required to obtain better performance in all of these applications. There have been several classical as well as learning-based approaches to enhance the frame rate and the quality of each frame of the video, which can bridge the gap between cheap camera equipment and software that requires high FPS and high-quality video. The field has evolved from classical image deblurring techniques and video generation based on basic variational methods to current state-of-the-art transformer-based models for blur decoding and video generation methods. A gentle trade-off during the training of such models is to accurately predict the motion and structure of objects while also maintaining the quality of each generated frame. Through this paper, we aim to give a brief overview of different approaches and methods for tackling the problem of deblurring, blur decoding, and video generation, starting from classical methods to the current state-of-the-art approaches in terms of their methodology, improvements, drawbacks, and future direction of the field.
Technology Enhanced Training in Medical Multi-User Scenarios

Simulation and training became a standard activity in several fields of education. These include also the medical sector. The training of complex procedures, or rarely occurring situations, helps to improve the performance of medical professionals under stressful situations. Over the last years, mixed reality solutions grew in importance and made their way into medical training setups. The focus is usually put on the medical process itself, whereas the environment and the used equipment are of secondary importance. The approach of the presented results of the research project MedTech-mR are showing the possibilities of using real life scenarios including room setup and equipment. The solution is based on a virtual reality approach, using the Oculus Quest 2 and hand-tracking mode. The produced and tested solution includes a multiuser setup and therefore allows to train team communication skills with up to five persons actively engaged into one scenario. The observing facilitator guides the users and performs the consecutive debriefing. The evaluation showed that putting the trainees into a realistic medical environment and equipping them with digital clones of actually used devices helps the users to immerse into the simulation scenario and focus more on the tasks themselves.
Which Attacks Lead to Hazards? Combining Safety and Security Analysis for Cyber-Physical Systems

Cyber-Physical Systems (CPS) are exposed to a plethora of attacks and their attack surface is only increasing. However, whilst many attack paths are possible, only some can threaten the system's safety and potentially lead to loss of life. Identifying them is of essence. We propose a methodology and develop a tool-chain to systematically analyse and enumerate the attacks leading to safety violations. This is achieved by lazily combining threat modelling and safety analysis with formal verification and with attack graph analysis. We also identify the minimum sets of privileges that must be protected to preserve safety. We demonstrate the effectiveness of our methodology to discover threat scenarios by applying it to a Communication Based Train Control System. Our design choices emphasise compatibility with existing safety and security frameworks, whilst remaining agnostic to specific tools or attack graphs representations.
Decoding motion-blur using variational autoencoder

Applications of camera-based systems are increasing day by day due to developments in hardware as well as improvements in software processing. One of the major limiting factors to the extension of these systems in some fields with high-speed applications like automation, self-driving automobiles, etc., is the long exposure time of traditional camera systems to capture images, which results in motion-blurred images. Although this limitation is being tackled by utilizing cameras with higher frame rates (smaller exposure time), such systems increase the development costs of the setup. This paper presents a novel learning-based blur decoding solution, i.e., a combined task of video deblurring and intermediate frame generation from a single motion blur image and its corresponding first sharp image. This method can be used to convert low-frame-rate video to high-frame-rate video by interpolating a single blur frame into multiple sharp frames without modifying the video-acquisition hardware. The proposed network accurately predicts the structure and motion of objects from first clear and blurred frames, respectively, and then uses this information to build a precise sequence of frames. The experimental results show that our algorithm performs substantially well on the GoPro dataset for blur decoding and frame interpolation tasks.
Concept2Vec: concept vector generation for biomedical literature using concept modelling

&lt;span&gt;In the biomedical field due to tremendous medical research, every year the medical papers generated is very huge. In order to process these tremendous amounts of textual data, the information retrieval techniques are combined with machine learning methods like natural language processing (NLP) for finding the useful insights in data for NLP tasks. In this work, we have addressed the challenges involved in existing NLP techniques like Word2Vec, Doc2Vec, and Biosent2Vec and followed by the traditional classification of articles with Concept2Vec and random forest classification. Concept vector techniques are emerging methods in NLP and in information retrieval systems. Pretrained concept encoders are available in Machine learning, whereas no such exists in biomedical domain. In this paper a proposal on Concept2Vec trained from biomedical text is highlighted. Our work accurately emphasizes on the quality of semantics defined between the medical terms, which can help the medical practitioners in taking errorless decisions. In this work 20 thousand biomedical documents are considered for Concept2Vec embeddings and the proposed approach have shown best concept similarity in terms of relevant measures compared with existing approaches such as Word2Vec, global vectors (GloVe), fastText, and BioSentVec, bidirectional encoder representations from transformers (BERT), biomedical-BERT.&lt;/span&gt;