Reviewerly/exHarmony huggingface dataset:
Compilation Forking: A Fast and Flexible Way of Generating Data for Compiler-Internal Machine Learning Tasks

Compiler optimization decisions are often based on hand-crafted heuristics centered around a few established benchmark suites. Alternatively, they can be learned from feature and performance data produced during compilation. However, data-driven compiler optimizations based on machine learning models require large sets of quality data for training in order to match or even outperform existing human-crafted heuristics. In static compilation setups, related work has addressed this problem with iterative compilation. However, a dynamic compiler may produce different data depending on dynamically-chosen compilation strategies, which aggravates the generation of comparable data. We propose compilation forking, a technique for generating consistent feature and performance data from arbitrary, dynamically-compiled programs. Different versions of program parts with the same profiling and compilation history are executed within single program runs to minimize noise stemming from dynamic compilation and the runtime environment. Our approach facilitates large-scale performance evaluations of compiler optimization decisions. Additionally, compilation forking supports creating domain-specific compilation strategies based on machine learning by providing the data for model training. We implemented compilation forking in the GraalVM compiler in a programming-language-agnostic way. To assess the quality of the generated data, we trained several machine learning models to replace compiler heuristics for loop-related optimizations. The trained models perform equally well to the highly-tuned compiler heuristics when comparing the geometric means of benchmark suite performances. Larger impacts on few single benchmarks range from speedups of 20% to slowdowns of 17%. The presented approach can be implemented in any dynamic compiler. We believe that it can help to analyze compilation decisions and leverage the use of machine learning into dynamic compilation.
3D reconstruction of coronary arteries using deep networks from synthetic X-ray angiogram data

Cardiovascular disease (CVD) is one of the most common health problems that are responsible for one-third of all deaths around the globe. Although X-Ray angiography has deficiencies such as two-dimensional (2D) representation of three dimensional (3D) structures, vessel overlapping, noisy background, the existence of other tissues/organs in images, etc., it is used as the gold standard technique for the diagnosis and in some cases treatment of CVDs. To overcome the deficiencies, great efforts have been drawn on retrieval of actual 3D representation of coronary arterial tree from 2D X-ray angiograms. However, the proposed algorithms are based on analytical methods and enforce some constraints. With the evolution of deep neural networks, 3D reconstruction from images can be achieved effectively. In this study, we propose a new data structure for the representation of objects in a tubular shape for 3D reconstruction of arteries using deep learning. Moreover, we propose a method to generate synthetic coronaries from data of real subjects. Then, we validate tubular shape representation using 3 typical deep learning architectures with synthetic X-ray data we produced. The input to deep learning architectures is multi-view segmented X-Ray images and the output is the structured tubular representation. We compare results qualitatively in terms of visual appearance and quantitatively in terms of Chamfer Distance and Mean Squared Error. The results demonstrate that tubular representation has promising performance in 3D reconstruction of coronaries. We observe that convolutional neural network (CNN) based architectures yield better 3D reconstruction performance with 9.9e-3 on Chamfer Distance. On the other hand, LSTM-based network fails to learn the coronary tree structure and we conclude that LSTMs are not appropriate for auto-regression problems as depicted in this study.
Peridynamic Approach to Digital Image Correlation Strain Calculation Algorithm

Digital image correlation is an experimental optical non-contact full field displacement and strain evaluation method based on the surface subsets tracking with photo cameras, digital images processing, and numerical computation. However, the full field of strain computation is a challenging problem, mainly because the displacement field to be differentiated is not continuous, individual subsets are tracked by the optical digital image correlation system. Moreover, the numerical differentiation can also amplify the noise of the displacement field inducing thus strain errors when the displacement data are poor. The peridynamics theory (which equations are cast in terms of spatial integrals of displacements, instead of spatial derivatives in the classical continuum mechanics) based algorithm is considered in this study and applied for the experimental digital image correlation displacement field to analyze possible peridynamic differentiation method advantages. A strains convergence analysis between the digital image correlation and peridynamic differentiation methods is done in this study. The integro-differential strain calculation as an alternative method is validated against digital image correlation and finite element simulation strain fields. It is also shown that the digital image correlation, a noisy displacement field, still provides an accurate and low level noise strain evaluation based on the proposed method.
Creation, evaluation, and optimization of a domain-based dictionary

This work creates, evaluates, and optimizes a domain-based dictionary using labeled domain documents as the input. The dictionary is created using selected unigrams and bigrams from the labeled text documents. Dictionary is evaluated using the Naïve Bayes classification model. Classification Accuracy obtained is used as a metric to evaluate the effectiveness of the dictionary. The paper also studies the impact of applying the Stochastic Gradient Descent (SGD) technique, with Lasso and Ridge Regularization, on the effectiveness of a domain-based dictionary. Both, Lasso and Ridge regularization, with Ridge faring better than Lasso, help to optimize the dictionary size, without any significant reduction in the accuracy. The created dictionaries are evaluated on the dataset used for their creation and subsequently on an unseen dataset as well. The applicability of a created dictionary to classify the documents belonging to a different dataset gives an idea about the generality of that dictionary. The paper establishes that the dictionaries created using the above methodology are generic enough to classify documents of other unseen datasets.
Using TikTok as an Active Learning Strategy

TikTok is a popular social media app that allows users to create short 15- to 30-second videos using music, text, acting, photographs, and voice-over audio to convey a message. The instructor for the fundamental nursing course decided to create weekly TikTok videos that cover the theory material and embed them into the theory modules of the course management system (CMS). Using a popular song and motions that are trending on TikTok, the instructor modified the motions to promote learning for a specific topic. For example, one video on fluid status showed a scale and then 2.2-pound weights to remind the students about the importance of patient daily weight using the same scale, time, and clothes. The 56 TikTok videos are completely optional for students to view throughout the CMS. By the end of the semester, students were requesting specific content (eg, β-blockers, suctioning) for future TikTok videos. A survey about the TikTok videos was given at the end of the semester. Fifty-seven of the 60 students (95% response rate) completed the survey. The average age of the students was 28 years, with a range of 19 to 52 years. Ninety-one percent (52 students) viewed the TikTok videos favorably. Only 7% (4 students) rated the TikTok videos “not helpful.” Because of the overwhelming positive feedback, the TikTok videos will continue as an active learning strategy.
Transport-Oriented Feature Aggregation for Speaker Embedding Learning

Pooling is needed to aggregate frame-level features into utterance-level representations for speaker modeling. Given the success of statistics-based pooling methods, we hypothesize that speaker characteristics are well represented in the statistical distribution over the pre-aggregation layer's output, and propose to use transport-oriented feature aggregation for deriving speaker embeddings. The aggregated representation encodes the geometric structure of the underlying feature distribution, which is expected to contain valuable speaker-specific information that may not be represented by the commonly used statistical measures like mean and variance. The original transport-oriented feature aggregation is also extended to a weighted-frame version to incorporate the attention mechanism. Experiments on speaker verification with the Voxceleb dataset show improvement over statistics pooling and its attentive variant.
Machine Learning post-hoc interpretability: a systematic mapping study

Context: In the pre-algorithm world, humans and organizations made decisions in hiring and criminal sentencing. Nowadays, some of these decisions are entirely made or influenced by Machine Learning algorithms. Problem: Research is starting to reveal some troubling examples in which the reality of algorithmic decision-making runs the risk of replicating and even amplifying human biases. Along with that, most algorithmic decision systems are opaque and not interpretable - which makes it more difficult to detect potential biases and mitigate them. Solution: This paper reports an overview of the current literature on machine learning interpretability. IS Theory: This work was conceived under the aegis of the Sociotechnical theory. Artificial Intelligence systems can only be understood and improved if both ‘social’ and ‘technical’ aspects are brought together and treated as interdependent parts of a complex system. Method: The overview presented in this article has resulted from a systematic mapping study. Summary of Results: We find that, currently, the majority of XAI studies are not for end-users affected by the model but rather for data scientists who use explainability as a debugging tool. There is thus a gap in the quality assessment and deployment of interpretable methods. Contributions and Impact in the IS area: The main contribution of the paper is to serve as the motivating background for a series of challenges faced by XAI, such as combining different interpretable methods, evaluating interpretability, and building human-centered methods. We end by discussing concerns raised regarding explainability and presenting a series of questions that can serve as an agenda for future research in the field.
Proof of Federated Training: Accountable Cross-Network Model Training and Inference

Blockchain has widely been adopted to design accountable federated learning frameworks; however, the existing frameworks do not scale for distributed model training over multiple independent blockchain networks. For storing the pre-trained models over blockchain, current approaches primarily embed a model using its structural properties that are neither scalable for cross-chain exchange nor suitable for cross-chain verification. This paper proposes an architectural framework for cross-chain verifiable model training using federated learning, called Proof of Federated Training (PoFT), the first of its kind that enables a federated training procedure span across the clients over multiple blockchain networks. Instead of structural embedding, PoFT uses model parameters to embed the model over a blockchain and then applies a verifiable model exchange between two blockchain networks for cross-network model training. We implement and test PoFT over a large-scale setup using Amazon EC2 instances and observe that cross-chain training can significantly boosts up the model efficacy. In contrast, PoFT incurs marginal overhead for inter-chain model exchanges.
A Meta-Analysis of the Effects of Storytelling on English Language Learning

The aim of the current meta-analysis was to determine overall effects of storytelling on English language learning and teaching with further analysis of different effects across school levels, outcome variables (such as language skills and affective effects), and across storytelling types. Although storytelling-related meta-analyses have already been conducted, the previous meta-analysis on storytelling did not analyze affective effects or include theses/dissertations. The current study analyzed cognitive and affective effects of storytelling through 277 samples from 18 journal articles, 63 master’s theses and three doctoral dissertations in order to generalize more samples of empirical research on storytelling under Korean EFL context from 2000 to 2021. As a result, the overall effect of storytelling was g = .613. Likewise, effects of the cognitive domain (g = .627) and the affective domain (g = .599) were similar with the overall effect size of storytelling. Vocabulary and attitude were more effective than other subtypes of cognitive and affective domains respectively. Storytelling was more effective with a more directly interactive type when learners started to study English in elementary or secondary school. Finally, educational implications and limitations are discussed.
Few Shot Cross-Lingual TTS Using Transferable Phoneme Embedding

This paper studies a transferable phoneme embedding framework that aims to deal with the cross-lingual text-to-speech (TTS) problem under the few-shot setting. Transfer learning is a common approach when it comes to few-shot learning since training from scratch on few-shot training data is bound to overfit. Still, we find that the naive transfer learning approach fails to adapt to unseen languages under extremely few-shot settings, where less than 8 minutes of data is provided. We deal with the problem by proposing a framework that consists of a phoneme-based TTS model and a codebook module to project phonemes from different languages into a learned latent space. Furthermore, by utilizing phoneme-level averaged self-supervised learned features, we effectively improve the quality of synthesized speeches. Experiments show that using 4 utterances, which is about 30 seconds of data, is enough to synthesize intelligible speech when adapting to an unseen language using our framework.
Understanding the Role of Explanation Modality in AI-assisted Decision-making

Advances in artificial intelligence and machine learning have led to a steep rise in the adoption of AI to augment or support human decision-making across domains. There has been an increasing body of work addressing the benefits of model interpretability and explanations to help end-users or other stakeholders decipher the inner workings of the so-called ”black box AI systems”. Yet, little is currently understood about the role of modalities through which explanations can be communicated (e.g., text, visualizations, or audio) to inform, augment, and shape human decision-making. In our work, we address this research gap through the lens of a credibility assessment system. Considering the deluge of information available through various channels, people constantly make decisions while considering the perceived credibility of the information they consume. However, with an increasing information overload, assessing the credibility of the information we encounter is a non-trivial task. To help users in this task, automated credibility assessment systems have been devised as decision support systems in various contexts (e.g., assessing the credibility of news or social media posts). However, for these systems to be effective in supporting users, they need to be trusted and understood. Explanations have been shown to play an essential role in informing users’ reliance on decision support systems. In this paper, we investigate the influence of explanation modalities on an AI-assisted credibility assessment task. We use a between-subjects experiment (N = 375), spanning six different explanation modalities, to evaluate the role of explanation modality on the accuracy of AI-assisted decision outcomes, the perceived system trust among users, and system usability. Our results indicate that explanations play a significant role in shaping users’ reliance on the decision support system and, thereby, the accuracy of decisions made. We found that users performed with higher accuracy while assessing the credibility of statements in the presence of explanations. We also found that users had a significantly harder time agreeing on statement credibility without explanations. With explanations present, text and audio explanations were more effective than graphic explanations. Additionally, we found that combining graphical with text and/or audio explanations were significantly effective. Such combinations of modalities led to a higher user performance than using graphical explanations alone.
Research on Pedestrian Intrusion Detection in Static Scenes

Security system is an important technical means of implementing security prevention and control, and its use in the field of security technology prevention is becoming more and more widespread in the current situation of expanding demand for security. The security systems used now primarily mainly rely on human visual judgment, which demonstrate the lack of intelligent analysis of video content. Static Pedestrian Intrusion Detection (SPID), which determines whether a pedestrian invades a target area in a static scene, is an important vision task in the field of intelligent video surveillance, and has a wide range of applications in scenarios such as intelligent security. To address the problem of static pedestrian intrusion detection data construction, this paper fully investigates the data set and provides sufficient data preparation for the study of this task. This paper proposes a multi-task deep network model based on target detection region segmentation and fast pedestrian detection to achieve accurate pedestrian intrusion determination in static scenes using the powerful nonlinear feature extraction capability of the network. To solve the real-time problem, the model proposes two mobile network optimization strategies, feature sharing and feature cropping, to reduce the computational complexity of the algorithm. Experimental results show that the proposed model achieves 83.1% accuracy and 20.4 FPS detection speed on the static pedestrian intrusion detection datasets, outperforming existing algorithms in terms of both accuracy and speed to achieve end-to-end real-time pedestrian intrusion detection.
New Government’s Challenges on Higher Education Finance

The purpose of this study is to propose a new government task on higher education finance. For the study, related literature, financial data, and media reports were analyzed.&#x0D; The results are as follows: First, the cost per student in the higher education stage is lower than in OECD countries, and even lower than the elementary level. Despite the increase in the higher education budget over the past decade, the government's financial investment has been mainly on the National Scholarship and universities' tuition income has been effectively decreasing. Second, as a result of evaluating the 10-year basic plan for higher education financial investment, it was analyzed that the government's overall fiscal investment in higher education was quite close to the original target. However, most indicators failed to achieve their goals as the method of financing was achieved through restructuring of existing budgets. Finally, as a plan to expand higher education finances, it was proposed to improve the legal suitability of tuition policies and to prepare a legal basis for stable securing higher education finances.&#x0D; Despite the inadequate level of financial investment in higher education, discussions on higher education finances have mainly focused on the allocation issues, and practical efforts have been insufficient to expand the size of finance itself. According to the 2021-2025 National Financial Management Plan, the government's investment in higher education is unlikely to be improved. Based on the results of the 10-year basic plan for higher education, the government needs to set specific level of financial investment goals in the basic plan for higher education and implement stable higher education finances with a legal foundation beyond securing restructuring levels.
A GPU-based accelerated ELM and deep-ELM training algorithms for traditional and deep neural networks classifiers

The extreme learning machine (ELM) has been effectively used for training single-layer neural networks. In recent years, great attention has been paid to deep extreme learning machine (D-ELM) structures. Deep neural network structures are trained via the ELM method. Some stacked auto-encoders followed by a simple ELM layer can be usually used for solving classification and regression tasks. Although ELM has been employed for speeding up the training process of the neural network, D-ELM based models suffer from some issues such as the time complexity and running time. In this paper, we explore how the evaluation of ELM and D-ELM can be accelerated. GPUs are used to speed up the training process of ELM and D-ELM models. In the proposed method, three separate phases are considered for the algorithms. In the first phase, loading and pre-processing the data are performed serially in the CPU. In the second and third phases, which respectively are the training and testing phases of the algorithm, all the matrix operations of the algorithms are implemented in parallel mode using the GPU memory hierarchy. Also, having access to highly efficient computational libraries, additional support is provided for GPU-based parallel computing. In the simulation setup, five sets of the database are applied to train the ELM and D-ELM on both CPU and GPU platforms. The results obtained show the proposed approach based on GPUs can remarkable save running time. Although both serial and parallel methods measure approximately the same accuracy, the parallel methods provided for the models reduce the run time significantly.
Weakly-Supervised High-Fidelity Ultrasound Video Synthesis with Feature Decoupling

Ultrasound (US) is widely used for its advantages of real-time imaging, radiation-free and portability. In clinical practice, analysis and diagnosis often rely on US sequences rather than a single image to obtain dynamic anatomical information. This is challenging for novices to learn because practicing with adequate videos from patients is clinically unpractical. In this paper, we propose a novel framework to synthesize high-fidelity US videos. Specifically, the synthesis videos are generated by animating source content images based on the motion of given driving videos. Our highlights are three-fold. First, leveraging the advantages of self- and fully-supervised learning, our proposed system is trained in weakly-supervised manner for keypoint detection. These keypoints then provide vital information for handling complex high dynamic motions in US videos. Second, we decouple content and texture learning using the dual decoders to effectively reduce the model learning difficulty. Last, we adopt the adversarial training strategy with GAN losses for further improving the sharpness of the generated videos, narrowing the gap between real and synthesis videos. We validate our method on a large in-house pelvic dataset with high dynamic motion. Extensive evaluation metrics and user study prove the effectiveness of our proposed method.