Reviewerly/exHarmony huggingface dataset:
Using interpretative phenomenological analysis to gain a qualitative understanding of presence in virtual reality

Quantitative methods have thus far been the predominant methodological stance of virtual presence research, leaving much to be desired in terms of qualitative understanding. Yet, virtual experiences are a highly personal engagement, unique to each individual, and their presence in virtual reality can be viewed in terms of its experiential individuality. This aspect of the virtual experience is overlooked by conventional quantitative methods, which clusters ratings or scores to form group deductions. Therefore, to address the qualitative gap in the literature and provide an appropriate examination of virtual experiences from the perspective of the individual, an Interpretative Phenomenological Approach was undertaken. This alternate methodology sought to reveal which aspects of virtual experiences users identify as enabling feelings of presence. Examination of common themes among accounts of individuals were performed, to investigate the generation of feelings of presence in virtual reality. Online recruitment provided six interviewees who participated in online semi-structured interviews, prior to Interpretive Phenomenological Analysis. Three superordinate themes were identified: visual satisfaction, freedom of interaction and suspension of real life. Expectance, realism and prevention of disbelief are among the sub-themes identified that contributed to the interviewee's highly present experiences. The identified themes demonstrated the greatest influences of enabling a deeper sense of presence, in turn enhancing their experiences within virtual reality. In acknowledging these mitigating influences, it is hoped this may enable future virtual systems to build upon the research provided and produce consistently high-presence experiences. Consequently, this can aid educational, therapeutic and entertainment applications of virtual reality.
LightFGCNet: A Lightweight and Focusing on Global Context Information Semantic Segmentation Network for Remote Sensing Imagery

Convolutional neural networks have attracted much attention for their use in the semantic segmentation of remote sensing imagery. The effectiveness of semantic segmentation of remote sensing images is significantly influenced by contextual information extraction. The traditional convolutional neural network is constrained by the size of the convolution kernel and mainly concentrates on local contextual information. We suggest a new lightweight global context semantic segmentation network, LightFGCNet, to fully utilize the global context data and to further reduce the model parameters. It uses an encoder–decoder architecture and gradually combines feature information from adjacent encoder blocks during the decoding upsampling stage, allowing the network to better extract global context information. Considering that the frequent merging of feature information produces a significant quantity of redundant noise, we build a unique and lightweight parallel channel spatial attention module (PCSAM) for a few critical contextual features. Additionally, we design a multi-scale fusion module (MSFM) to acquire multi-scale feature target information. We conduct comprehensive experiments on the two well-known datasets ISPRS Vaihingen and WHU Building. The findings demonstrate that our suggested strategy can efficiently decrease the number of parameters. Separately, the number of parameters and FLOPs are 3.12 M and 23.5 G, respectively, and the mIoU and IoU of our model on the two datasets are 70.45% and 89.87%, respectively, which is significantly better than what the conventional convolutional neural networks for semantic segmentation can deliver.
Interpretable Machine Learning to Optimize Early In-Hospital Mortality Prediction for Elderly Patients with Sepsis: A Discovery Study

Sepsis-related mortality rates are high among elderly patients, especially those in intensive care units (ICUs). Early prediction of the prognosis of sepsis is critical, as prompt and effective treatment can improve prognosis. Researchers have predicted mortality and the development of sepsis using machine learning algorithms; however, few studies specifically focus on elderly patients with sepsis. This paper proposes a viable model for early prediction of in-hospital mortality among elderly patients diagnosed with sepsis. We extracted patient information from the Medical Information Mart for Intensive Care IV database. We employed several machine learning algorithms to predict the in-hospital mortality of elderly ICU patients with sepsis. The performance of the model was evaluated by using the AUROC and F1 score. Furthermore, the SHAP algorithm was used to explain the model, analyze how the individual features affect the model output, and visualize the Shapley value for a single individual. Our study included 18522 elderly patients, with a mortality of 15.4%. After screening, 59 clinical variables were extracted to develop models. Feature importance analysis showed that age, PO2, RDW, SPO2, WBC, and urine output were significantly related to the in-hospital mortality. According to the results of AUROC (0.871 (95% CI: 0.854-0.888)) and F1 score (0.547 (95% CI: 0.539-0.661)) analyses, the extreme gradient boosting (XGBoost) model outperformed the other models (i.e., LGBM, LR, RF, DT, and KNN). Furthermore, SHAP force analysis illustrated how the constructed model visualized the individualized prediction of death. XGBoost machine learning framework gives good in-hospital mortality prediction of elderly patients with sepsis and can maximize prediction model accuracy. The XGBoost model could be an effective tool to assist doctors in identifying high-risk cases of in-hospital mortality among elderly patients with sepsis. This could be used to create a clinical decision support system in the future.
Microphone Identification based on Spectral Entropy with Convolutional Neural Network

Microphone identification based on the intrinsic physical features has received significant attention by the research community in recent years. Such properties can be exploited in security and forensics applications in order to assess the authenticity of a certain audio track or for audio attribution. The detection is possible since the specific characteristics of the microphone components slightly change from one microphone to another due to the manufacturing process. Various techniques have been proposed to implement physical microphone identification from the use of hand-tailored features (e.g., entropy measure) to spectral representation (e.g., cepstral coefficients) in combination with machine learning algorithms. In recent times, the application of deep learning to microphone identification was successfully demonstrated especially in comparison to shallow machine learning algorithms. On the other hand, deep learning requires significant computing resources especially with large data sets, as in the case of audio recordings for microphone identification. Then, dimensionality reduction could benefit the computing time efficiency for this task. The proposed study envisaged the combined use of Convolutional Neural Networks with spectral entropy features extraction to improve time efficiency while preserving a high identification accuracy. Spectral features, based on Shannon entropy and Renyi entropy, are proposed in combination with the ReliefF algorithm to implement a dimensionality reduction of the spectral representation of the audio signals recorded from 34 different microphones. Then, the reduced spectral representation is fed to a custom Convolutional Neural Network to perform the classification. The results show that this approach is able to reduce significantly the processing time in comparison with the state of the art while preserving a comparable identification accuracy and an increased robustness to the presence of noise.
Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns

Nowadays, copy detection patterns (CDP) appear as a very promising anti-counterfeiting technology for physical object protection. However, the advent of deep learning as a powerful attacking tool has shown that the general authentication schemes are unable to compete and fail against such attacks. In this paper, we propose a new mathematical model of printingimaging channel for the authentication of CDP together with a new detection scheme based on it. The results show that even deep learning created copy fakes unknown at the training stage can be reliably authenticated based on the proposed approach and using only digital references of CDP during authentication.
Multi-stage feature-fusion dense network for motion deblurring

Although convolutional neural networks (CNNs) have recently shown considerable progress in motion deblurring, most existing methods that adopt multi-scale input schemes are still challenging in accurately restoring the heavily-blurred regions in blurry images. Several recent methods aim to further improve the deblurring effect using larger and more complex models, but these methods inevitably result in huge computing costs. To address the performance-complexity trade-off, we propose a multi-stage feature-fusion dense network (MFFDNet) for motion deblurring. Each sub-network of our MFFDNet has the similar structure and the same scale of input. Meanwhile, we propose a feature-fusion dense connection structure to reuse the extracted features, thereby improving the deblurring effect. Moreover, instead of using the multi-scale loss function, we only calculate the loss function at the output of the last stage since the input scale of our sub-network is invariant. Experimental results show that MFFDNet maintains a relatively small computing cost while outperforming state-of-the-art motion-deblurring methods. The source code is publicly available at: https://github.com/CaiGuoHS/MFFDNet_release.
Improving Density Peaks Clustering through GPU acceleration

Density Peaks Clustering (DPC) is a recently proposed clustering algorithm that has distinct advantages over existing clustering algorithms, which has already been used in a wide range of applications. However, DPC requires computing the distance between every pair of input points, therefore incurring quadratic computation overhead, which is prohibitive for large data sets. To address this efficiency problem, we propose to use GPU to accelerate DPC. We exploit a spatial index structure VP-Tree to efficiently maintain the data points and propose a GPU-friendly parallel VP-Tree construction algorithm. Based on the constructed VP-Tree, we propose a GPU-Accelerated DPC algorithm GDPC, in which the all-pair computation in DPC is greatly accelerated. Furthermore, in order to process dynamic evolving datasets, we propose an incremental GDPC algorithm, Incremental GDPC. Our results show that GDPC can achieve over 5.3-148.9X acceleration compared to the state-of-the-art GPU-based, multicore-based, and distributed DPC implementations, a 2.3-40.5X acceleration compared to the state-of-the-art incremental DPC algorithm.
QIST: One-dimensional quantum integer wavelet S-transform

We present the first quantum version of the one-dimensional integer wavelet S-Transform (QIST). This version is based on a new Quantum Block Representation by Basis States (QBRBS) and the design of a quantum rounding operator. The QBRBS format facilitates storing and manipulating pairs of signal elements, decreasing the use of additional qubits and gates. Also, the quantum rounding operator exploits the features of the S-Transform to avoid nonlinearities. Complete quantum circuits for addition, subtraction, and halving operations were developed. The complexity analysis showed a polynomial quantum complexity, O(n), and a time complexity of O(1). The experiments performed on MATLAB and Qiskit verify the theoretical results coinciding with the results of the classical version. Finally, the new quantum transform's feasibility and accuracy enable new quantum developments in information hiding, compression, and IoT applications.
A multi-measure approach for lexical diversity in writing assessments: Considerations in measurement and timing

Several researchers (e.g., Gebril & Plakans, 2016; Crossley & McNamara, 2012) have noted a relationship between lexical diversity (LD) and writing proficiency. Previous research has indicated that typical LD measures can account for as much as 44 % of variability in writing proficiency (Crossley et al., 2011). However, it remains unclear which of the numerous LD measures (or combination of measures) that exist best predict writing proficiency (e.g., McCarthy & Jarvis, 2010). The present study addresses these issues using scored writing assessment samples from 911 ESL students. LD was assessed with a battery of ten measures through the Python lexical-diversity package (Kyle, 2020). A PCA was conducted to determine how these LD measures varied in deriving LD scores. Writing proficiency scores were then predicted by using linear mixed-effects regressions that included either a single LD measure or multiple measures. The results indicated that LD is a meaningful predictor in timed writing proficiency assessments, multiple LD measures have more predictive power than individual measures, timing has a slight effect on LD, and some LD measures are highly correlated while others are not. Implications for automated writing proficiency assessment and rubric design as well as LD measurement research are explained.
Spatio-temporal matching for siamese visual tracking

Siamese trackers formulate the visual tracking task as a similarity matching problem through cross correlation. It is arduous for such methods to track targets with the presence of distractors. We suspect the reasons are twofold: 1) The irrelevant activated channels in the correlation map will produce ambiguous matching results. 2) The pipeline is a per-frame matching process and cannot handle the response aberrance caused by temporal context variation. In this paper, we propose a spatio-temporal matching process to thoroughly explore the capability of 4-D matching in space (height, width and channel) and time. In spatial matching, we introduce a space-variant instance-aware correlation (SI-Corr) to implement different channel-wise response recalibration for each matching position. SI-Corr can guide the generation of instance-aware features and distinguish the target and distractors at the instance level. In temporal matching, we design an aberrance repressed module (ARM) to investigate the short-term positional relationship between the target and distractors. ARM utilizes a simple optimization method to restrict the abrupt alteration of the interframe response maps, which allows the network to learn a temporal consistency of context structure distribution. Moreover, we efficiently embed temporal consistency into the inference process. Experiments on six benchmarks, including OTB100, VOT2018, VOT2020, GOT-10k, LaSOT and TrackingNet, demonstrate the state-of-the-art performance of the proposed method.
A high-accuracy circular hole measurement method based on multi-camera system

Industrial parts often contain a large number of circular holes that are often used as assembly datums. Measuring circular holes with high accuracy is very challenging due to the low signal-to-noise ratio of the edges on the image. This problem is further complicated when chamfers and nonuniform reflections are present. In this paper, a high-accuracy method for measuring circular holes is proposed to realize on-line inspection under a multi-camera system. In the image processing stage, an ellipse detector is designed based on a high-confidence edge detection algorithm and state-of-the-art ellipse fitting technology to significantly eliminate the ellipse fitting bias caused by blurred edges. In the ellipse matching and validation stage, the ambiguous objects are reconstructed first using ambiguous ellipse pairs matched by the epipolar geometry. Then, the similarity between the reprojected ellipse and the detected ellipse is assessed by calculating the Euclidean distance to significantly eliminate mismatched candidates and select the best matching ones. Subsequently, the discrete 3-D points are calculated by spatial intersection after obtaining the homologous points on the corresponding ellipses. Furthermore, a robust spatial circle fitting method is developed to adapt to discrete 3-D points that are not strictly located on a plane. Finally, extensive experiments on both real-world datasets and simulation datasets demonstrate that the proposed algorithm is beneficial for improving the measurement accuracy compared with the state-of-the-art algorithms and demonstrate great potential for industrial applications.
A quantum walks assisted algorithm for peptide and protein folding prediction

Proteins are considered as the working force of cells. Their functionality is determined by their spatial form. In 1973 Anfinsen proposed that the spatial form is determined by the sequence of amino acids in the protein backbone. Yet, the number of possible sequences as well as the possible configurations is very large, making the task of predicting the protein's spatial form very difficult. Many approaches have been proposed, both classical and hybrid quantum - classical ones. We propose a novel hybrid algorithm. In our approach we utilized quantum walks, a proven model for universal quantum computation. We considered a simplified version of the protein backbone to be the evolution space of the quantum walk. The dihedral angles φ and ψ are introduced as phase factors to the quantum walk evolution. We also utilized a cost function to describe the system, where the R - chain, describing the specific amino acid, corresponds to a discrete value, affecting the cost functions value. Our aim is to minimize the cost function value, by updating the dihedral angles for specific regions of the Ramachandran plot, using a Metropolis algorithm.
Automating Learning Situations in EdTech: Techno-Commercial Logic of Assetisation

Abstract Critical scholarship has already shown how automation processes may be problematic, for example, by reproducing social inequalities instead of removing them or requiring intense labour from education institutions’ staff instead of easing the workload. Despite these critiques, automated interventions in education are expanding fast and often with limited scrutiny of the technological and commercial specificities of such processes. We build on existing debates by asking: does automation of learning situations contribute to assetisation processes in EdTech, and if so, how? Drawing on document analysis and interviews with EdTech companies’ employees, we argue that automated interventions make assetisation possible. We trace their techno-commercial logic by analysing how learning situations are made tangible by constructing digital objects, and how they are automated through specific computational interventions. We identify three assetisation processes: First, the alienation of digital objects from students and staff deepens the companies’ control of digital services offering automated learning interventions. Second, engagement fetishism—i.e., treating engagement as both the goal and means of automated learning situations—valorises particular forms of automation. And finally, techno-deterministic beliefs drive investment and policy into identified forms of automation, making higher education and EdTech constituents act ‘as if’ the automation of learning is feasible.
Feature distillation Siamese networks for object tracking

In recent years, Siamese-based trackers have shown remarkable improvement in visual tracking. The general trends are interested in making deeper and more complicated networks to pursue higher accuracy. However, these advances result in cumbersome trackers with respect to size and speed, which hinders the deployment of deep trackers on edge devices. Due to the tracking scenario complexity and temporal coherence, the backbone network of deep trackers emphasizes the target appearance information more. However, the appearance feature is sensitive to parameter variation, making the traditional deep model compression methods hard to compress a deep tracker. To bridge the gap between deep Siamese trackers and practical use, we propose a new feature distillation algorithm suitable for deep trackers in this paper. Firstly, motivated by the concept of divide-and-conquer, we formulate the feature distillation into a stepwise distillation problem and perform distillation on each minimum unit to relieve the hard-to-distill problem of appearance feature. Secondly, we reconstruct the student model into a combination of convolution kernels and a point-wise convolutional layer, which enables the student model to inherit all the parameters of the teacher model during initialization. Finally, we propose a 3-step warm-up training strategy to address the student model’s degradation and structural adaptation problems during training. Extensive experiments on eight benchmarks demonstrate that our proposed method compresses the fully convolutional Siamese Networks (SiamFC) and its variant Siamd and achieves leading tracking performance with only 2.1 MB model size while running at 225 fps.
BrightFormer: A transformer to brighten the image

Low-light image enhancement algorithms need recover overall information of images, including local details and global information. However, existing image enhancement methods mainly focus on local details or global information. Therefore, it is challenging to balance the two aspects at the same time. This paper proposes a local dual-branch network (BrightFormer) for image enhancement that combines convolutions and transformers as to solution. The salient features of this paper are: (1) convolution is adopted to refine high-frequency information so that local features are preserved and propagated throughout the network; (2) combining gated parameters with prior information on illumination (ill-map) in self-attention can not only improves the flexibility of feature expression but also extract global features more easily; (3) the obtained local details and global features are fused by spatial and channel attention in Feature equalization fusion unit (FEFU); (4) a Deep feedforward network (DFN) is utilized to encode the location information between adjacent pixels, and the GELU activation function is used to retain useful features and eliminate useless features with an attention-like mechanism. Experimental results show that BrightFormer achieves competitive performance on quantitative metrics and visual perception on the datasets such as LOL, MEF and LIME etc.