Reviewerly/exHarmony huggingface dataset:
Student-Sponsored Projects in a Capstone Course : Reflections and Lessons Learned

Undergraduate students in all disciplines sometimes struggle to see the connections between what they learn in the classroom and how it will be used in practice. Capstone courses, internships, and co-op experiences can help students bridge this gap by allowing students to put theory into practice on real-world projects and by connecting students to industry practices and partners. These experiences can better prepare students for their careers, but they may also shield students from real-world risks, such as lack of product feasibility or viability, and from the non-technical tasks of business planning and startup tasks, in order to make the academic aspects of the course manageable and scalable. In this work, we present our experience offering undergraduate Software Engineering students the opportunity to self-sponsor a year-long capstone project. Students began the course with four software product ideas, none of which had been assessed for feasibility or viability. During the academic year, students were responsible for product discovery, technology selection, and technical development following good Software Engineering practices. They also created business and marketing plans, and formed an LLC. This paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> describes the processes, practices and timeline we followed for a student-sponsored capstone team. We also share our lessons learned and offer suggestions for student-sponsored projects in computing capstone courses.
Non-Uniformly Terminating Chase: Size and Complexity

The chase procedure, originally introduced for checking implication of database constraints, and later on used for computing data exchange solutions, has recently become a central algorithmic tool in rule-based ontological reasoning. In this context, a key problem is non-uniform chase termination: does the chase of a database w.r.t. a rule-based ontology terminate? And if this is the case, what is the size of the result of the chase? We focus on guarded tuple-generating dependencies (TGDs), which form a robust rule-based ontology language, and study the above central questions for the semi-oblivious version of the chase. One of our main findings is that non-uniform semi-oblivious chase termination for guarded TGDs is feasible in polynomial time w.r.t. the database, and the size of the result of the chase (whenever is finite) is linear w.r.t. the database. Towards our results concerning non-uniform chase termination, we show that basic techniques such as simplification and linearization, originally introduced in the context of ontological query answering, can be safely applied to the chase termination problem.
Deep patch learning algorithms with high interpretability for regression problems

Improving the performance of machine learning algorithms to overcome the curse of dimensionality while maintaining interpretability is still a challenging issue for researchers in artificial intelligence. Patch learning (PL), based on the improved adaptive network-based fuzzy inference system (ANFIS) and continuous local optimization for the input domain, is characterized by high accuracy. However, PL can only handle low-dimensional data set regression. Based on the parallel and serial ensembles, two deep patch learning algorithms with embedded adaptive fuzzy systems (DPLFSs) are proposed in this paper. First, using the maximum information coefficient (MIC) and Pearson's correlation coefficients for feature selection, the variables with the least relationship (linear or nonlinear) are excluded. Second, principal component analysis is used to reduce the complexity further of DPLFSs. Meanwhile, fuzzy C-means clustering is used to enhance the interpretability of DPLFSs. Then, an improved PL method is put forward for the training of each sub-fuzzy system in a fashion of bottom-up layer-by-layer, and finally, the structure optimization is performed to significantly improve the interpretability of DPLFSs. Experiments on several benchmark data sets show the advantages of a DPLFS: (1) it can handle medium-scale data sets; (2) it can overcome the curse of dimensionality faced by PL; (3) its precision and generalization are greatly improved; and (4) it can overcome the poor interpretability of deep learning networks. Compared with shallow and deep learning algorithms, DPLFSs have the advantages of interpretability, self-learning, and high precision. DPLFS1 is superior for medium-scale data; DPLFS2 is more efficient and effective for high-dimensional problems, has a faster convergence, and is more interpretable.
SIGN: Statistical Inference Graphs based on probabilistic Network activity interpretation

Convolutional neural networks (CNNs) have achieved superior accuracy in many visual-related tasks. However, the inference process through a CNN's intermediate layers is opaque, making it difficult to interpret such networks or develop trust in their operation. In this article, we introduce SIGN method for modeling the network's hidden layer activity using probabilistic models. The activity patterns in layers of interest are modeled as Gaussian mixture models, and transition probabilities between clusters in consecutive modeled layers are estimated to identify paths of inference. For fully connected networks, the entire layer activity is clustered, and the resulting model is a hidden Markov model. For convolutional layers, spatial columns of activity are clustered, and a maximum likelihood model is developed for mining an explanatory inference graph. The graph describes the hierarchy of activity clusters most relevant for network prediction. We show that such inference graphs are useful for understanding the general inference process of a class, as well as explaining the (correct or incorrect) decisions the network makes about specific images. In addition, SIGN provide interesting observations regarding hidden layer activity in general, including the concentration of memorization in a single middle layer in fully connected networks, and a highly local nature of column activities in the top CNN layers.
Discriminative Geometric-Structure-Based Deep Hashing for Large-Scale Image Retrieval

Deep hashing reaps the benefits of deep learning and hashing technology, and has become the mainstream of large-scale image retrieval. It generally encodes image into hash code with feature similarity preserving, that is, geometric-structure preservation, and achieves promising retrieval results. In this article, we find that existing geometric-structure preservation manner inadequately ensures feature discrimination, while improving feature discrimination of hash code essentially determines hash learning retrieval performance. This fact principally spurs us to propose a discriminative geometric-structure-based deep hashing method (DGDH), which investigates three novel loss terms based on class centers to induce the so-called discriminative geometrical structure. In detail, the margin-aware center loss assembles samples in the same class to the corresponding class centers for intraclass compactness, then a linear classifier based on class center serves to boost interclass separability, and the radius loss further puts different class centers on a hypersphere to tentatively reduce quantization errors. An efficient alternate optimization algorithm with guaranteed desirable convergence is proposed to optimize DGDH. We theoretically analyze the robustness and generalization of the proposed method. The experiments on five popular benchmark datasets demonstrate superior image retrieval performance of the proposed DGDH over several state of the arts.
An Optimized Transfer Learning Model Based Kidney Stone Classification

The kidney is an important organ of humans to purify the blood.The healthy function of the kidney is always essential to balance the salt, potassium and pH levels in the blood.Recently, the failure of kidneys happens easily to human beings due to their lifestyle, eating habits and diabetes diseases.Early prediction of kidney stones is compulsory for timely treatment.Image processingbased diagnosis approaches provide a greater success rate than other detection approaches.In this work, proposed a kidney stone classification method based on optimized Transfer Learning(TL).The Deep Convolutional Neural Network (DCNN) models of DenseNet169, MobileNetv2 and GoogleNet applied for classification.The combined classification results are processed by ensemble learning to increase classification performance.The hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer (GTO).The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.
A skeleton-based method and benchmark for real-time action classification of functional movement screen

FMS stands for functional movement screen, which is a simple and effective method to evaluate athletes’ basic sports ability. This paper proposed a real-time FMS action classification method. Correspondingly, a video set was constructed with two different perspectives including 8 testers, 13 independent testing processes and 360574 images. Moreover, a normalization algorithm and a result correction algorithm is proposed to improve the performance of the models and make the result sequence more continuously. Furthermore, it has the vital significance to FMS action evaluation. Finally, this paper analyzed the effectiveness of different models and compared their performance from the aspects of accuracy, continuity, running speed, etc. The experimental results show that our method can achieve 96.7% precision score and 94.7% recall score on the test set. And the average running speed of the system is over 30 FPS. All related data, benchmarks and codes will be uploaded on https://github.com/bobogo/FMS-evaluation-system.
Optimization Induced Equilibrium Networks: An Explicit Optimization Perspective for Understanding Equilibrium Models

To reveal the mystery behind deep neural networks (DNNs), optimization may offer a good perspective. There are already some clues showing the strong connection between DNNs and optimization problems, e.g., under a mild condition, DNN's activation function is indeed a proximal operator. In this paper, we are committed to providing a unified optimization induced interpretability for a special class of networks-equilibrium models, i.e., neural networks defined by fixed point equations, which have become increasingly attractive recently. To this end, we first decompose DNNs into a new class of unit layer that is the proximal operator of an implicit convex function while keeping its output unchanged. Then, the equilibrium model of the unit layer can be derived, we name it Optimization Induced Equilibrium Networks (OptEq). The equilibrium point of OptEq can be theoretically connected to the solution of a convex optimization problem with explicit objectives. Based on this, we can flexibly introduce prior properties to the equilibrium points: 1) modifying the underlying convex problems explicitly so as to change the architectures of OptEq; and 2) merging the information into the fixed point iteration, which guarantees to choose the desired equilibrium point when the fixed point set is non-singleton. We show that OptEq outperforms previous implicit models even with fewer parameters.
Multi-resolution dictionary-based sparse coding method for interferometric phase image denoising

Interferometric synthetic aperture radar (InSAR) is widely used in acquisition of digital elevation data as well as the settlement of Earth surface. Researching on the processing of space-born InSAR is of great importance in improving the accuracy of sensing data. The phase denoising method based on multi-resolution dictionary sparse coding (SC) is proposed. First, noisy blocks are extracted from the complex valued interferometric image. Then, the dictionary is trained by online method, which uses a portion of elements in the training set to update the dictionary column by column. And the trained dictionary is used to sparsely represent the noisy blocks by greedy method to denoise the image patches with different resolutions. The whole image of a given resolution is formed by combining the patches in the same resolution together. Finally, the denoising results of different resolutions are merged based on hypothesis testing and the phase estimations according to each pixels are computed. The experiments prove that the proposed denoising method based on multi-resolution SC achieves higher peak signal to noise ratio than traditional SC method.
Point Cloud Video Super-Resolution via Partial Point Coupling and Graph Smoothness

Point cloud (PC) is a collection of discrete geometric samples of a physical object in 3D space. A PC video consists of temporal frames evenly spaced in time, each containing a static PC at one time instant. PCs in adjacent frames typically do not have point-to-point (P2P) correspondence, and thus exploiting temporal redundancy for PC restoration across frames is difficult. In this paper, we focus on the super-resolution (SR) problem for PC video: increase point density of PCs in video frames while preserving salient geometric features consistently across time. We accomplish this with two ideas. First, we establish partial P2P coupling between PCs of adjacent frames by interpolating interior points in a low-resolution PC patch in frame t and translating them to a corresponding patch in frame t + 1, via a motion model computed by iterative closest point (ICP). Second, we promote piecewise smoothness in 3D geometry in each patch using feature graph Laplacian regularizer (FGLR) in an easily computable quadratic form. The two ideas translate to an unconstrained quadratic programming (QP) problem with a system of linear equations as solution-one where we ensure the numerical stability by upper-bounding the condition number of the coefficient matrix. Finally, to improve the accuracy of the ICP motion model, we re-sample points in a super-resolved patch at time t to better match a low-resolution patch at time t+1 via bipartite graph matching after each SR iteration. Experimental results show temporally consistent super-resolved PC videos generated by our scheme, outperforming SR competitors that optimized on a per-frame basis, in two established PC metrics.
Code-switched end-to-end Marathi speech recognition for especially abled people

There is no need for eyes or arms in speech communication. Instead, both humans and machines communicate by speaking and listening. Therefore, speech communication not only helps physically disabled but also help people with hearing loss and/or language disorders. It is also used by people with vision or motor disabilities. There are various types of devices that are used for people with hearing loss. They can be classified as audio-visual stimulation devices, tactile devices, and speech processing devices. These devices consider the characteristics of speech and/or deploy automatic speech recognition inside it. This article focuses on such ASR system based on E2E model which can be useful in such devices. This research is carried out on the language used by the families of India’s over 80 million speakers i.e., Marathi. These assessments provide valuable insights into the development of SR models for low-resourced Indian languages. We present E2E ASR model trained with code-switched text of Marathi language and decoded similarly to a normal E2E ASR system. This model achieves a better word error rate of 35.09%, according to experimental results on the code-switched Marathi corpus. We compared our model to Facebook’s wav2vec2 model, which produces a WER of 23.72 percent on code-switched Marathi datasets without any language model and 12.10 percent on open ASR Marathi datasets.
Investigating Multi-Level Semantic Extraction with Squash Capsules for Short Text Classification

At present, short text classification is a hot topic in the area of natural language processing. Due to the sparseness and irregularity of short text, the task of short text classification still faces great challenges. In this paper, we propose a new classification model from the aspects of short text representation, global feature extraction and local feature extraction. We use convolutional networks to extract shallow features from short text vectorization, and introduce a multi-level semantic extraction framework. It uses BiLSTM as the encoding layer while the attention mechanism and normalization are used as the interaction layer. Finally, we concatenate the convolution feature vector and semantic results of the semantic framework. After several rounds of feature integration, the framework improves the quality of the feature representation. Combined with the capsule network, we obtain high-level local information by dynamic routing and then squash them. In addition, we explore the optimal depth of semantic feature extraction for short text based on a multi-level semantic framework. We utilized four benchmark datasets to demonstrate that our model provides comparable results. The experimental results show that the accuracy of SUBJ, TREC, MR and ProcCons are 93.8%, 91.94%, 82.81% and 98.43%, respectively, which verifies that our model has greatly improves classification accuracy and model robustness.
Analyzing the Effect of Diverse Gaze and Head Direction on Facial Expression Recognition With Photo-Reflective Sensors Embedded in a Head-Mounted Display

As one of the facial expression recognition techniques for Head-Mounted Display (HMD) users, embedded photo-reflective sensors have been used. In this paper, we investigate how gaze and face directions affect facial expression recognition using the embedded photo-reflective sensors. First, we collected a dataset of five facial expressions (Neutral, Happy, Angry, Sad, Surprised) while looking in diverse directions by moving 1) the eyes and 2) the head. Using the dataset, we analyzed the effect of gaze and face directions by constructing facial expression classifiers in five ways and evaluating the classification accuracy of each classifier. The results revealed that the single classifier that learned the data for all gaze points achieved the highest classification performance. Then, we investigated which facial part was affected by the gaze and face direction. The results showed that the gaze directions affected the upper facial parts, while the face directions affected the lower facial parts. In addition, by removing the bias of facial expression reproducibility, we investigated the pure effect of gaze and face directions in three conditions. The results showed that, in terms of gaze direction, building classifiers for each direction significantly improved the classification accuracy. However, in terms of face directions, there were slight differences between the classifier conditions. Our experimental results implied that multiple classifiers corresponding to multiple gaze and face directions improved facial expression recognition accuracy, but collecting the data of the vertical movement of gaze and face is a practical solution to improving facial expression recognition accuracy.
An adaptive direction-assisted test for microbiome compositional data

Microbial communities have been shown to be associated with many complex diseases, such as cancers and cardiovascular diseases. The identification of differentially abundant taxa is clinically important. It can help understand the pathology of complex diseases, and potentially provide preventive and therapeutic strategies. Appropriate differential analyses for microbiome data are challenging due to its unique data characteristics including compositional constraint, excessive zeros and high dimensionality. Most existing approaches either ignore these data characteristics or only account for the compositional constraint by using log-ratio transformations with zero observations replaced by a pseudocount. However, there is no consensus on how to choose a pseudocount. More importantly, ignoring the characteristic of excessive zeros may result in poorly powered analyses and therefore yield misleading findings.We develop a novel microbiome-based direction-assisted test for the detection of overall difference in microbial relative abundances between two health conditions, which simultaneously incorporates the characteristics of relative abundance data. The proposed test (i) divides the taxa into two clusters by the directions of mean differences of relative abundances and then combines them at cluster level, in light of the compositional characteristic; and (ii) contains a burden type test, which collapses multiple taxa into a single one to account for excessive zeros. Moreover, the proposed test is an adaptive procedure, which can accommodate high-dimensional settings and yield high power against various alternative hypotheses. We perform extensive simulation studies across a wide range of scenarios to evaluate the proposed test and show its substantial power gain over some existing tests. The superiority of the proposed approach is further demonstrated with real datasets from two microbiome studies.An R package for MiDAT is available at https://github.com/zhangwei0125/MiDAT.Supplementary data are available at Bioinformatics online.
Solar Flare Forecast Model Based on Resampling and Fusion Method

Abstract Predicting the activity of solar flares is of great significance for studying its physical mechanism and the impact on human production and life. Problems such as class imbalance, high time-series sensitivity, and over-localization of important features exist in the sample data used for flare forecasting. We design a solar flare fusion method based on resampling and the CNN-GRU algorithm to try to solve the above problems. In order to verify the effectiveness of this fusion method, first, we compared the forecast performance of different resampling methods by keeping the forecast model unchanged. Then, we used the resampling algorithm with high performance to combine some single forecast models and fusion forecast models respectively. We use the 2010–2017 sunspot data set to train and test the performance of the flare model in predicting flare events in the next 48 h. Through the conclusion of the above steps, we prove that the resampling method SMOTE and its variant SMOTE-ENN are more advantageous in class imbalance problem of flare samples. In addition, after the fusion of one-dimensional convolution and recurrent network with “forget-gate” , combined with the SMOTE-ENN to achieve TSS = 61%, HSS = 61%, TP Rate = 77% and TN Rate = 83%. This proves that the fusion model based on resampling and the CNN-GRU algorithm is more suitable for solar flare forecasting.