Reviewerly/exHarmony huggingface dataset:
Exploring the effect of the Group Size and Feedback of non-player character spectators in virtual reality exergames

Despite the widespread interest in leveraging non-player characters (NPCs) to enhance gameplay experiences, there is a gap in understanding of how NPC spectators (i.e., those virtual characters in the scene that watch users' actions) affect players. For instance, the impact of NPC spectators' presence and feedback on players' performance and experience has not been studied, especially in virtual reality (VR) exergames. This paper aims to fill this gap and reports two user studies that assess their effect on such games. Study 1 explored the impact of having NPC spectators present and their feedback available in a gesture-based VR exergame and found having NPC spectators and their feedback could improve players' game performance, experience, and exertion. Based on Study 1's results, we further explored two characteristics of the spectators-their group size (small/large) and their feedback (with/without). The results show that (1) a large spectator number is more helpful since it improves the overall game experience (higher competence, flow, immersion), increases AvgHR% (the average heart rate percentage divided by the maximum heart rate), and enhances performance (improved players' combo performance and increased gesture success rate for particular gesture); (2) spectator feedback is instrumental in improving players' performance (higher gesture success rates, more combos performed successfully, more monster's combos prevented), enhancing game experience (positive affect, competence, flow, and immersion), and reducing negative game experience, increasing exertion (AvgHR% and burned more calories). Based on the results, we derived two main design recommendations for VR exergames that could pave the way for improving gameplay performance and game experience, especially among young adults.
Robust decorrelation-based iterative algorithm for accurate phase extraction from disturbed fringe patterns

We propose an accurate and robust phase extraction method for phase-shifting interferometry to reduce the phase ripple error caused by illumination, contrast, phase-shift spatiotemporal variation, and intensity harmonics. In this method, a general physical model of interference fringes is constructed, and the parameters are decoupled using a Taylor expansion linearization approximation. In the iterative process, the estimated illumination and contrast spatial distributions are decorrelated from the phase, thus reducing damage to the algorithm’s robustness caused by a large number of linear model approximations. To the best of our knowledge, no method has been able to extract the phase distribution robustly and with high accuracy while considering all of these error sources simultaneously without imposing constraints inconsistent with the practical conditions.
Tuning Extreme Learning Machine by Hybrid Planet Optimization Algorithm for Diabetes Classification

This paper explores hyperparameter optimization and training of extreme learning machines (ELM) applied to diabetes diagnostics. Early detection of diabetes is vital, as timely treatment significantly improves the quality of life of those affected. One of the toughest challenges facing artificial intelligence (AI) is the selection of control parameters suited to the problem being addressed. This work proposes a metaheuristics-based approach for adjusting the number of neurons in a single hidden layer of an artificial neural network in an ELM, as well as the selection of weights and biases (training) of every neuron in the hidden layer. Additionally, an exploration of the planet optimization algorithm’s (POA) potential for addressing NP difficult tasks is conducted. Through the process of hybridization with the firefly algorithm (FA), the POAs’ performance is further improved. The resulting algorithm is tasked with selecting optimal control parameter values for an ELM tackling diabetes diagnostics. A comparative analysis of the ELM tuned by the proposed PAO firefly search (POA-FS) metaheuristics with other state-of-the-art algorithms tasked with the same challenge strongly indicates that the suggested ELM-POA-FS displays superior performance, clearly outperforming contemporary algorithms tackling the same task that it was tested against.
The odyssey to next-generation computers: cognitive computers (κC) inspired by the brain and powered by intelligent mathematics

Cognitive computers (κ C ) are intelligent processors advanced from data and information processing to autonomous knowledge learning and intelligence generation. This work presents a retrospective and prospective review of the odyssey toward κ C empowered by transdisciplinary basic research and engineering advances. A wide range of fundamental theories and innovative technologies for κ C is explored, and a set of underpinning intelligent mathematics (IM) is created. The architectures of κ C for cognitive computing and Autonomous Intelligence Generation (AIG) are designed as a brain-inspired cognitive engine. Applications of κ C in autonomous AI (AAI) are demonstrated by pilot projects. This work reveals that AIG will no longer be a privilege restricted only to humans via the odyssey to κ C toward training-free and self-inferencing computers.
MABERT: Mask-Attention-Based BERT for Chinese Event Extraction

Event extraction is an essential but challenging task in information extraction. This task has considerably benefited from pre-trained language models, such as BERT. However, when it comes to the trigger-word mismatch problem in languages without natural delimiters, existing methods ignore the complement of lexical information to BERT. In addition, the inherent multi-role noise problem could limit the performance of methods when one sentence contains multiple events. In this article, we propose a Mask-Attention-based BERT (MABERT) framework for Chinese event extraction to address the above problems. Firstly, in order to avoid trigger-word mismatch and integrate lexical features into BERT layers directly, a mask-attention-based transformer augmented with two mask matrices is devised to replace the original one in BERT. By the mask-attention-based transformer, the character sequence interacts with external lexical semantics sufficiently and keeps its structure information at the same time. Moreover, against the multi-role noise problem, we make use of event type information from representation and classification, two aspects to enrich entity features, where type markers and event-schema-based mask matrix are proposed. Experimental results on the widely used ACE2005 dataset show the effectiveness of our proposed MABERT on Chinese event extraction task compared with other state-of-the-art methods.
A stochastic maximum principle approach for reinforcement learning with parameterized environment

In this work, we introduce a stochastic maximum principle (SMP) approach for solving the reinforcement learning problem with the assumption that the unknowns in the environment can be parameterized based on physics knowledge. For the development of numerical algorithms, we apply an effective online parameter estimation method as our exploration technique to estimate the environment parameter during the training procedure, and the exploitation for the optimal policy is achieved by an efficient backward action learning method for policy improvement under the SMP framework. Numerical experiments are presented to demonstrate that the SMP approach for reinforcement learning can produce reliable control policy, and the gradient descent type optimization in the SMP solver requires less training episodes compared with the standard dynamic programming principle based methods.
EduNER: a Chinese named entity recognition dataset for education research

A high-quality domain-oriented dataset is crucial for the domain-specific named entity recognition (NER) task. In this study, we introduce a novel education-oriented Chinese NER dataset (EduNER). To provide representative and diverse training data, we collect data from multiple sources, including textbooks, academic papers, and education-related web pages. The collected documents span ten years (2012-2021). A team of domain experts is invited to accomplish the education NER schema definition, and a group of trained annotators is hired to complete the annotation. A collaborative labeling platform is built for accelerating human annotation. The constructed EduNER dataset includes 16 entity types, 11k+ sentences, and 35,731 entities. We conduct a thorough statistical analysis of EduNER and summarize its distinctive characteristics by comparing it with eight open-domain or domain-specific NER datasets. Sixteen state-of-the-art models are further utilized for NER tasks validation. The experimental results can enlighten further exploration. To the best of our knowledge, EduNER is the first publicly available dataset for NER task in the education domain, which may promote the development of education-oriented NER models.
Artificial Intelligence and Sensor Technologies the Future of Education Students with and without Intellectual and Developmental Disabilities

Abstract Learning environments such as classrooms and online systems for students with developmental or intellectual disabilities are typically dynamic, multisensory, and make use of top-down attention and working memory mechanisms to promote sense making by the student. However, the last five years have ushered a revolution in computational power, brain mapping, wearable sensors use, large scale data collection, generative artificial intelligence, and physiological signal processing techniques e.g., the 4th industrial revolution. Owing to the advent of inexpensive and highly accurate sensor technologies, generative artificial intelligence, and neurotechnologies, educators now have a new way to assess every student’s learning status, cognitive states, and promote adaption in a multi-modal and multi-dimensional way in real-time. Process data from sensors and neurotechnologies can be available for use by educators within milliseconds as opposed to minutes, hours, or days, as is the case for traditional educational data. Data from artificially intelligent systems can tracks students’ learning progressions using sensor-based data so that content adjustments and differentiation of instruction can meet a student’s needs in real-time. Keywords: Artificial Intelligence, Online Learning, Wearable Sensors, Special Education, Adaptive Learning.
Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure

The practicality of reinforcement learning algorithms has been limited due to poor scaling with respect to the problem size, as the sample complexity of learning an ε-optimal policy is Ω(|S||A|H/ ε2) over worst case instances of an MDP with state space S, action space A, and horizon H. We consider a class of MDPs for which the associated optimal Q* function is low rank, where the latent features are unknown. While one would hope to achieve linear sample complexity in |S| and |A| due to the low rank structure, we show that without imposing further assumptions beyond low rank of Q*, if one is constrained to estimate the Q function using only observations from a subset of entries, there is a worst case instance in which one must incur a sample complexity exponential in the horizon H to learn a near optimal policy. We subsequently show that under stronger low rank structural assumptions, given access to a generative model, Low Rank Monte Carlo Policy Iteration (LR-MCPI) and Low Rank Empirical Value Iteration (LR-EVI) achieve the desired sample complexity of Õ((|S|+|A|)poly (d,H)/ε2) for a rank d setting, which is minimax optimal with respect to the scaling of |S|, |A|, and ε. In contrast to literature on linear and low-rank MDPs, we do not require a known feature mapping, our algorithm is computationally simple, and our results hold for long time horizons. Our results provide insights on the minimal low-rank structural assumptions required on the MDP with respect to the transition kernel versus the optimal action-value function.
Obfuscating the Dataset: Impacts and Applications

Obfuscating a dataset by adding random noises to protect the privacy of sensitive samples in the training dataset is crucial to prevent data leakage to untrusted parties when dataset sharing is essential. We conduct comprehensive experiments to investigate how the dataset obfuscation can affect the resultant model weights —in terms of the model accuracy, ℓ 2 -distance-based model distance, and level of data privacy—and discuss the potential applications with the proposed Privacy, Utility, and Distinguishability (PUD)-triangle diagram to visualize the requirement preferences. Our experiments are based on the popular MNIST and CIFAR-10 datasets under both independent and identically distributed (IID) and non-IID settings. Significant results include a tradeoff between the model accuracy and privacy level and a tradeoff between the model difference and privacy level. The results indicate broad application prospects for training outsourcing and guarding against attacks in federated learning both of which have been increasingly attractive in many areas, particularly learning in edge computing.
Blockchain Private Pools and Price Discovery

In this paper, we examine the balance between allocative efficiency and price discovery in blockchain-based finance. Our analysis reveals that the use of private pools, while improving blockspace allocation efficiency, negatively impacts price discovery. We show that the presence of private pools limits the amount of information revealed through pending orders and increases the execution risk of informed orders. We argue that price discovery can be improved by increasing the number of validators monitoring private pools or the block production rate on the chain.
NAM-YOLOV7: An Improved YOLOv7 Based on Attention Model for Animal Death Detection

Dead animals on the road are very harmful to public health.Because of offensive odours and the potential outspread of diseases, dead animals endanger public health.Existing methods focused on collisions between vehicles and animals on roads, number of animals, protection of crops from animals etc.To solve this issue, two main tasks listed below can be used: (1) Detecting dead animals on the highway and (2) Notification to the appropriate authorities.In this paper, we explore the viability of object detection methods to detect dead animals.We scrutinize and compare various versions of the "YOLO"(you only look once) in detecting dead animals.We compare the performance with the improved YOLOv7 model with the earlier versions when trained on the ADD (Animal Death detection) dataset and results show that improved YOLOv7 performs best when compared to the earlier YOLO models.
More Power to SPARQL: From Paths to Trees

Exploring Knowledge Graphs (KGs, in short) to discover facts and links is tedious even for experts with knowledge of SPARQL due to their unfamiliarity with the structure and labels of entities, classes and relations. Some KG applications require finding the connections between groups of nodes, even if users ignore the shape of these connections. However, SPARQL only allows checking if paths exist, not returning them. A recent property graph query language, GPML, allows also returning connecting paths, but not connections between three or more nodes. We propose to demonstrate RelSearch, a system supporting extended SPARQL queries, featuring standard Basic Graph Patterns (BGPs) as well as novel Connecting Tree Patterns (CTPs); each CTP requests the connections (paths, or trees) between nodes bound to variables. RelSearch evaluates such extended queries using novel algorithms [2] which, unlike prior keyword search methods, return connections regardless of the edge directions and are independent of how we measure the quality (score) of each connection. We will demonstrate RelSearch ’s expressivity and efficiency using a variety of RDF graphs, user-selected score functions, and search exploration orders.
Text Analysis On Twitter Data Using LSA and LDA

The news ecosystem has changed in the modern years from outdated print media to social media sites. Because social media platforms enable us to absorb news much more quickly and with less restrictive editing, fake news is disseminated at an astonishing rate and scale. More and increasingly individuals are using social media as the world becomes more digital since it makes connecting with others relatively simple. But false information is misguiding people. Although fake news is simple to propagate, its effects can be disastrous. Bogus news has frequently resulted in uncontrolled circumstances that killed numerous people. People with limited education might quickly become acclimated to bogus news. Instead of checking the accuracy of the information, they accept what is provided to them. This can be overcome by using Text mining, Statics concepts which can detect fake news more precisely when compared to several machine learning methods. This proposed system is on the analysis of fake news and detection of hatred news using t-distributed Stochastic Neighbor Embedding(t-SNE) to check dimensionality reduction and topic modeling using Latent Dirichlet Allocation(LDA) and data pre-processing using Latent Semantic Analysis (LSA).
Evolution of the time delay signature of chaos generated in three types of optical injection systems

The time delay signature (TDS) of chaos generated in three schemes of optical injection has been investigated, and the mechanism of TDS suppression (TDSS) is revealed. The first scheme is a continuous-wave (CW) laser unidirectionally injecting into a chaotic laser, and the TDS of the chaotic laser is suppressed below 0.08 in this structure. The second scheme is a chaotic laser unidirectionally injecting into a CW laser, where the parameter range of TDSS lower than 0.06 of this structure is the largest among the three schemes. The third scheme is two CW lasers coupling to each other, and two chaotic lasers with TDS (TDS&lt;0.1) suppression can be obtained simultaneously. For the further analysis of the mechanism of TDSS, the quasi-linear relationship between the synchronization coefficient and TDS is revealed. This study will provide insight into the generation of chaotic lasers by optical injection and promote the application of chaotic lasers.
Clustering-Based Imputation for Dropout Buyers in Large-Scale Online Experimentation

In online experimentation, appropriate metrics (e.g., purchase) provide strong evidence to support hypotheses and enhance the decision-making process. However, incomplete metrics are frequently occurred in the online experimentation, making the available data to be much fewer than the planned online experiments (e.g., A/B testing). In this work, we introduce the concept of dropout buyers and categorize users with incomplete metric values into two groups: visitors and dropout buyers. For the analysis of incomplete metrics, we propose a clustering-based imputation method using k-nearest neighbors. Our proposed imputation method considers both the experiment-specific features and users’ activities along their shopping paths, allowing different imputation values for different users. To facilitate efficient imputation of large-scale data sets in online experimentation, the proposed method uses a combination of stratification and clustering. The performance of the proposed method is compared to several conventional methods in both simulation studies and a real online experiment at eBay.
The Metaverse in University Education during COVID-19: A Systematic Review of Success Factors

The experiences that the COVID-19 pandemic brought led educational institutions to use disruptive technologies that guaranteed continuity of learning in the face of any adverse scenario, ranging from the conceptual to the experiential. This article aims to describe how the metaverse contributed to university education, and through these factors the success of its application was evidenced. For which a systematic review of the literature was developed. The research is exploratory-descriptive level. A total of 738 documents from the Scopus, ERIC, Taylor &amp; Francis, and Google Scholar databases were reviewed, from which 16 articles were finally extracted. The content analysis technique was used through the Atlas Ti software. With this, it was obtained as a result that the type of metaverse used to a greater extent was virtual reality. Likewise, the areas of study in which they were applied to a greater extent are in education: medicine, engineering and administration. In addition, the factors that evidenced the improvement in university education as an effect of the application of the metaverse are academic performance and interaction between students. It is concluded that, given the pandemic scenario and the need to continue the teaching-learning process considering the limitations offered by the virtual modality in which experiential learning is not developed, it led to the development of learning scenarios through the metaverse. However, it will be necessary to reduce the gaps in terms of teaching skills, create teaching scenarios in the virtual world, use and evaluate them permanently.
The relationship between education and economic growth: A cross-country analysis

This study investigates the effects of education on economic growth in 89 low, middle, and high-income countries using an index of human capital developed by Penn World Table and economic growth data from the World Bank’s World Development Indicators. This study specifies a growth accounting empirical model to examine the effects of education on economic growth. Using systems Generalized Method of Moments estimation technique on annual data covering the periods from 2002 to 2020, the results show that education significantly enhances economic growth. On average, an increase in the education index by 0.1 increases the growth of real GDP per capita by 0.8 percentage points. The results also show that education does have higher returns for low- and middle-income countries compared to high-income countries. The results of this study lend credence to the argument that poor countries should focus and pool adequate resources towards education as a means to achieving growth.
An ETA-Based Tactical Conflict Resolution Method for Air Logistics Transportation

Air logistics transportation has become one of the most promising markets for the civil drone industry. However, the large flow, high density, and complex environmental characteristics of urban scenes make tactical conflict resolution very challenging. Existing conflict resolution methods are limited by insufficient collision avoidance success rates when considering non-cooperative targets and fail to take the temporal constraints of the pre-defined 4D trajectory into consideration. In this paper, a novel reinforcement learning-based tactical conflict resolution method for air logistics transportation is designed by reconstructing the state space following the risk sectors concept and through the use of a novel Estimated Time of Arrival (ETA)-based temporal reward setting. Our contributions allow a drone to integrate the temporal constraints of the 4D trajectory pre-defined in the strategic phase. As a consequence, the drone can successfully avoid non-cooperative targets while greatly reducing the occurrence of secondary conflicts, as demonstrated by the numerical simulation results.