Reviewerly/exHarmony huggingface dataset:
BiTNet: A lightweight object detection network for real-time classroom behavior recognition with transformer and bi-directional pyramid network

In this paper, we propose BiTNet, a real-time object detection network, for enhancing teaching quality and providing feedback through real-time analysis of student behavior in classroom. BiTNet addresses challenges faced by current methods, such as occlusion and small objects in images. Specifically, it incorporates an Efficient Transformer Block (ETB) and Efficient Convolution Aggregation Block (ECAB) for extracting image features. ETB uses convolutional multi-head self-attention (CMHSA) to capture contextual information thus improves the capability to recoginze occluded objects. Its homogeneous multi-branch design reduces the computational costs. The single aggregation operation of ECAB aggregates the extracted features into a single feature map, improving the accuracy of recognition while reducing the computational cost. Then, the semantic and positional information extracted from the backbone are fused in Bi-directional Feature Pyramid Network (BiFPN). BiTNet also employs a smaller size detection head for recognition of small objects. Experimental results on classroom behavior dataset demonstrate that our proposed method has impressive accuracy (82.9%) and speed (256.7 FPS on GPU, 30.7 FPS on CPU, and 18.9 on camera). We further experiment with our method on Pascal VOC 2007 and VisDrone2021, validating BiTNet’s generalization and its capability to recognize small objects. In conclusion, BiTNet has a simple yet efficient network structure and feature extraction module, which reduce computational cost while maintaining recognition accuracy, making it a contender for real-time classroom behavior recognition tasks.
Temporal-channel convolution with self-attention network for human activity recognition using wearable sensors

Human activity recognition (HAR) is an essential task in many applications such as health monitoring, rehabilitation, and sports training. Sensor-based HAR has received increasing attention due to the widespread availability of sensors in daily life. In this paper, we propose two novel architectures, the convolution with self-attention network (CSNet) and the temporal-channel convolution with self-attention network (TCCSNet), for classifying sequences of human activity data from different sensors. CSNet leverages both convolution and self-attention to capture both local and global dependencies in the input data, while TCCSNet exploits both temporal and inter-channel dependencies through two branches of convolutions and self-attentions for extracting time-wise and channel-wise information. The proposed methods are evaluated on seven different sensor-based HAR datasets, namely: MHEALTH, PAMAP2, UTD1, UTD2, WHARF, USC-HAD, and WISDM, using the leave-one-subject-out cross-validation protocol. Our experiments show that the proposed models outperform other modern approaches, such as Transformers and long short-term memory (LSTM) based models.
Geospatial modelling of mineral potential zones using data-driven based weighting factor and statistical index techniques

Mineral prospectivity models (MPMs) are significantly essential in delineating target zones with the optimum likelihood of containing a particular sought-after mineral deposit. This present study carried out mineral potential mapping over the Collette Prospecting Licence (PL) Area of north-western Ghana using bivariate data-driven spatial statistical models composed of statistical index (SI) and weighting factor (WF) approaches. In the first instance, the geographic coordinates of variously known locations of artisanal mining operations as well as high Au concentration locations were mapped during a field survey. As a result, 181 known locations of Au occurrences were identified, out of which 127 (70%) were selected randomly for training and creating the mineral prospectivity models, whereas the remaining 54 (30%) were used to assess and validate the accuracy of the predictive models produced. The efficacy of mineral prospectivity models generated enormously depends on the appropriate selection of mineral-related factors. In this study, the following mineral-related condition factors (evidential layers) comprising analytic signal, lineament density, uranium-thorium ratio, uranium, potassium-thorium ratio, potassium, reduction-to-equator, and geology were used. The aforementioned evidential layers were derived and sourced from geophysical and geological datasets, which were later prepared for the generation of the models in a geographic information systems (GIS) environment. Finally, the validation of the mineral prospectivity models generated was carried out by applying the receiver operating characteristics (ROC) curve. The estimated results based on the ROC plots obtained for the predictive models showed that the area under the ROC curve (AUC) scores obtained for the SI-based and WF-based mineral prospectivity models were respectively, 0.780 and 0.733. Hence, it can be concluded that both mineral predictive models created in this study produced reasonably good accuracy (AUC score greater than 0.7) in predicting the potential zones of gold mineralisation occurrences within the Collette PL Area of north-western Ghana. These MPMs can serve as essential models for mineral exploration programmes within the study area.
Human-in-the-Loop Machine Learning for the Treatment of Pancreatic Cancer

Human-in-the-Loop Machine Learning (HITL-ML) is a set of techniques that attempt to actively introduce experts into the learning loop of machine learning (ML) models to improve the learning process. In this paper we present a HITL-ML strategy for the treatment of pancreatic cancer in which a classifier should decide whether a chemotherapy treatment is suitable or not for the patient. The contribution of this work is, first, to demonstrate that involving human experts in the learning process improves the learning capacity of the model; second, to develop a relatively novel Interactive Machine Learning (IML) approach in which unstructured feedback obtained from the experts is used to optimize the synthetic cases generator implemented by a Generative Adversarial Network (GAN). This GAN is used to augment the dataset and to improve the generalization capabilities of the model. Finally, the inclusion of humans in the learning process also poses new challenges, e.g., aspects related to Human-Computer Interaction (HCI), normally irrelevant in ML systems, are now of great importance and can condition the success of a HITL approach. This paper also discusses the approach taken to address these challenges.
FUZZY-TOPSIS method for defining optimal parameters and finding suitable sites for PV power plants

This study aims to assess and locate the potential for solar power plants (SPP) by utilizing TOPSIS decision analysis and based Fuzzy-Boolean logic based on GIS in Tehran province, Iran. GIS was used as a robust tool for data processing to gather practical and necessary information for effective resources and facilities. Through the multicriteria decision-making method (MCDM), alternative sites were identified and prioritized. The fuzzy logic method was employed to evaluate regions and generate thematic maps, revealing that approximately 95% (14,350 km2) of the region's area was unsuitable for constructing a power plant, while 5% (741 km2) was suitable. From the suitable areas, nine locations were selected for photovoltaic plant construction, totaling 22 km2, occupying 0.15% of the province's total area. Fuzzy TOPSIS was then used to prioritize the alternatives, resulting in the selection of Ghiyeh Naserieh in the southern part of the province as the preferred location for the power generation unit.
ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game Design

Large language models (LLMs) have taken the scientific world by storm, changing the landscape of natural language processing and human-computer interaction. These powerful tools can answer complex questions and, surprisingly, perform challenging creative tasks (e.g., generate code and applications to solve problems, write stories, pieces of music, etc.). In this paper, we present a collaborative game design framework that combines interactive evolution and large language models to simulate the typical human design process. We use the former to exploit users' feedback for selecting the most promising ideas and large language models for a very complex creative task---the recombination and variation of ideas. In our framework, the process starts with a brief and a set of candidate designs, either generated using a language model or proposed by the users. Next, users collaborate on the design process by providing feedback to an interactive genetic algorithm that selects, recombines, and mutates the most promising designs. We evaluated our framework on three game design tasks with human designers who collaborated remotely.
Enhance the trust between IoT devices, mobile apps, and the cloud based on blockchain

The current IoT platform is vulnerable to remote device hijacking and substitution attacks, primarily due to weak authentication and authorization mechanisms between IoT devices, mobile apps, and the cloud. Such attacks can lead to privacy breaches and even threaten the safety of users' lives and properties. Thus, ensuring the trustworthiness of IoT devices is crucial. In this paper, we propose a blockchain-based trust management approach to enhance the trust between IoT devices, mobile apps, and the cloud. We have implemented our approach by combining Ethereum with a smart home solution to create a distributed trust management system for IoT devices. Our evaluation results demonstrate the effectiveness of our approach in detecting the above attacks, with acceptable performance.
Syntax-based dynamic latent graph for event relation extraction

This paper focuses on extracting temporal and parent–child relationships between news events in social news. Previous methods have proved that syntactic features are valid. However, most previous methods directly use the static outcomes parsed by syntactic parsing tools, but task-irrelevant or erroneous parses will inevitably degrade the performance of the model. In addition, many implicit higher-order connections that are directly related and critical to tasks are not explicitly exploited. In this paper, we propose a novel syntax-based dynamic latent graph model (SDLG) for this task. Specifically, we first apply a syntactic type-enhanced attention mechanism to assign different weights to different connections in the parsing results, which helps to filter out noisy connections and better fuse the information in the syntactic structures. Next, we introduce a dynamic event pair-aware induction graph to mine the task-related latent connections. It constructs a potential attention matrix to complement and correct the supervised syntactic features, using the semantics of the event pairs as a guide. Finally, the latent graph, together with the syntactic information, is fed into the graph convolutional network to obtain an improved representation of the event to complete relational reasoning. We have conducted extensive experiments on four public benchmarks, MATRES, TCR, HiEve and TB-Dense. The results show that our model outperforms the state-of-the-art model by 0.4%, 1.5%, 3.0% and 1.3% in F1 scores on the four datasets, respectively. Finally, we provide detailed analyses to show the effectiveness of each proposed component.
Zero-shot Temporal Relation Extraction with ChatGPT

The goal of temporal relation extraction is to infer the temporal relation between two events in the document.Supervised models are dominant in this task.In this work, we investigate Chat-GPT's ability on zero-shot temporal relation extraction.We designed three different prompt techniques to break down the task and evaluate ChatGPT.Our experiments show that Chat-GPT's performance has a large gap with that of supervised methods and can heavily rely on the design of prompts.We further demonstrate that ChatGPT can infer more small relation classes correctly than supervised methods.The current shortcomings of ChatGPT on temporal relation extraction are also discussed in this paper.We found that ChatGPT cannot keep consistency during temporal inference and it fails in actively long-dependency temporal inference.
Adaptive Graph Contrastive Learning for Recommendation

Graph neural networks (GNNs) have recently emerged as an effective collaborative filtering (CF) approaches for recommender systems. The key idea of GNN-based recommender systems is to recursively perform message passing along user-item interaction edges to refine encoded embeddings, relying on sufficient and high-quality training data. However, user behavior data in practical recommendation scenarios is often noisy and exhibits skewed distribution. To address these issues, some recommendation approaches, such as SGL, leverage self-supervised learning to improve user representations. These approaches conduct self-supervised learning through creating contrastive views, but they depend on the tedious trial-and-error selection of augmentation methods. In this paper, we propose a novel Adaptive Graph Contrastive Learning (AdaGCL) framework that conducts data augmentation with two adaptive contrastive view generators to better empower the CF paradigm. Specifically, we use two trainable view generators - a graph generative model and a graph denoising model - to create adaptive contrastive views. With two adaptive contrastive views, AdaGCL introduces additional high-quality training signals into the CF paradigm, helping to alleviate data sparsity and noise issues. Extensive experiments on three real-world datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods. Our model implementation codes are available at the link https://github.com/HKUDS/AdaGCL.
Federated Few-shot Learning

Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning model without exchanging their own local data. In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients. Although such a mechanism is proven to be effective in various fields, existing works generally assume that each client preserves sufficient data for training. In practice, however, certain clients can only contain a limited number of samples (i.e., few-shot samples). For example, the available photo data taken by a specific user with a new mobile device is relatively rare. In this scenario, existing FL efforts typically encounter a significant performance drop on these clients. Therefore, it is urgent to develop a few-shot model that can generalize to clients with limited data under the FL scenario. In this paper, we refer to this novel problem as federated few-shot learning. Nevertheless, the problem remains challenging due to two major reasons: the global data variance among clients (i.e., the difference in data distributions among clients) and the local data insufficiency in each client (i.e., the lack of adequate local data for training). To overcome these two challenges, we propose a novel federated few-shot learning framework with two separately updated models and dedicated training strategies to reduce the adverse impact of global data variance and local data insufficiency. Extensive experiments on four prevalent datasets that cover news articles and images validate the effectiveness of our framework compared with the state-of-the-art baselines.
The “Problem” of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation

Human variation in labeling is often considered noise.Annotation projects for machine learning (ML) aim at minimizing human label variation, with the assumption to maximize data quality and in turn optimize and maximize machine learning metrics.However, this conventional practice assumes that there exists a ground truth, and neglects that there exists genuine human variation in labeling due to disagreement, subjectivity in annotation or multiple plausible answers.In this position paper, we argue that this big open problem of human label variation persists and critically needs more attention to move our field forward.This is because human label variation impacts all stages of the ML pipeline: data, modeling and evaluation.However, few works consider all of these dimensions jointly; and existing research is fragmented.We reconcile different previously proposed notions of human label variation, provide a repository of publicly-available datasets with un-aggregated labels, depict approaches proposed so far, identify gaps and suggest ways forward.As datasets are becoming increasingly available, we hope that this synthesized view on the "problem" will lead to an open discussion on possible strategies to devise fundamentally new directions.
Psychometric approaches to exploring the characteristics of smart hotel brand experiences: Scale development and validation

The development of artificial intelligence (AI) and its application in the hotel industry has reshaped the concept of hotel brand management. In this study, we present an investigation of the impact of AI on the hotel brand experience by identifying underlying factors that best describe interaction with AI services. We utilized a mixed-methods research approach to develop and validate a smart hotel brand experiences scale; first, we conducted an exploratory qualitative study to conceptualize smart hotel brand experiences and formulate their underlying constructs and measures. A quantitative study followed to confirm the validity and reliability of the qualitative results. The study produced the following four constructs: anthropomorphic, affective, entertaining, and escapism experiences, and nomological validity was successfully fulfilled. The results suggest that experiences with AI services are essential for enhancing hotel brand values and provide a framework for future empirical studies in this emerging area.
Time-Entanglement QKD: Secret Key Rates and Information Reconciliation Coding

In time entanglement-based quantum key distribution (TE-QKD), Alice and Bob extract the raw key bits from the arrival times of entangled photon pairs. Each entangled pair can contribute to multiple key bits depending on how precisely Alice and Bob can measure the photon arrival times. Thus, TE-QKD can potentially increase the secret key rate compared to typical QKD implementations, which extract up to a bit per photon. Because of entanglement, the times of photon arrivals at Alice’s and Bob’s detectors and, thus, their raw keys should be identical. However, practical photon detectors suffer from time jitter errors. These errors cause discrepancies between Alice’s and Bob’s raw keys. Therefore, Alice must send information to Bob through the public channel to reconcile their raw keys. The amount of data sent for reconciliation represents a loss, rendering secret keys shorter than the raw keys. We compute the secret key rates possible in systems with detector jitter errors and show that they are much higher than those achievable in polarization entanglement-based QKD. We then construct codes for information reconciliation to approach these rates. We demonstrate that short and moderate-length standard error-correcting codes represent excellent information reconciliation choices, making TE-QKD a promising technology.
Breaking the rate-distance limitation of measurement-device-independent quantum secret sharing

Currently most progresses on quantum secret sharing suffer from rate-distance bound, and thus the key rates are limited. In addition to the limited key rate, the technical difficulty and the corresponding cost together prevent large-scale deployment. Furthermore, the performance of most existing protocols is analyzed in the asymptotic regime without considering participant attacks. Here we report a measurement-device-independent quantum secret sharing protocol with improved key rate and transmission distance. Based on spatial multiplexing, our protocol shows it can break rate-distance bounds over network under at least ten communication parties. Compared with other protocols, our work improves the secret key rate by more than two orders of magnitude and has a longer transmission distance. We analyze the security of our protocol in the composable framework considering participant attacks and evaluate its performance in the finite-size regime. In addition, we investigate applying our protocol to digital signatures where the signature rate is improved more than $10^7$ times compared with existing protocols. We anticipate that our quantum secret sharing protocol will provide a solid future for multiparty applications on the quantum network.
Adaptive echo state network with a recursive inverse-free weight update algorithm

Echo State Network (ESN) is widely applied in sequence prediction, physics, and economics. Moore-Penrose inversion is a typical solving process of ESN. However, in the era of big data, the traditional inversion is computationally costly and the data may be irreversible. To solve this problem, a recursive inverse-free algorithm ESN model, called RIFESN, is introduced in this article. The model also introduces an adaptive activation function based on the error between target value and predicted value, which greatly improves the nonlinear mapping capability of the network. Three simulation experiments are conducted on the Mackey-Glass-17 time series, sunspots series and daily exchange rate summary of eight countries from 1990 to 2016, respectively. The results of the three simulations show that RIFESN has better prediction accuracy and operational speed, which proves that it has a good application prospect.
Aspect based sentiment analysis using deep learning approaches: A survey

The wealth of unstructured text on the online web portal has made opinion mining the most thrust area for researchers, academicians, and businesses to extract information for gathering, analyzing, and aggregating human emotions. The extraction of public sentiment from the text at an aspect level has contributed exceptionally to various businesses in the marketplace. In recent times, deep learning-based techniques have learned high-level linguistic features without high-level feature engineering. Therefore, this paper focuses on a rigorous survey on two primary subtasks, aspect extraction and aspect category detection of aspect-based sentiment analysis (ABSA) methods based on deep learning. The significant advancement in the ABSA sector is demonstrated by a thorough evaluation of state-of-the-art and latest aspect extraction methodologies.
Response of mineral market to renewable energy production in the USA: Where lies the sustainable energy future

The policy dilemma regarding climate action and the rising demand of renewable energy has created opportunities for a new market of critical minerals in the USA. Nevertheless, creation of this market is susceptible to structural uncertainties within the economy. Analysis of these impacts is necessary for the attainment of Sustainable Development Goal (SDG) 7 objectives, which has been a pressing policy concern in the USA. The present study addresses this policy objective by analyzing the impact of renewable energy generation on the returns on mineral market in the USA over January 1990 to July 2022. The Multivariate Quantile-on-Quantile Regression (m-QQR) method is introduced in this study to analyze the tail-dependence of the model parameters in presence of uncertainties. The findings of the study have been utilized to develop a policy framework by internalizing the externalities exerted by the uncertainties, and thereby paving a way for developing a new market for the minerals. Contributions of the present study are (a) adopting the New Market Creation approach for critical minerals, and (b) introducing m-QQR method in the literature.