Reviewerly/exHarmony huggingface dataset:
TextGraph - A lexicon based framework for concept extraction and visualization

The shift in the news consumption from traditional newspapers to online news has led media analysts and researchers to apply powerful text mining techniques on the vast amount of news data. News has a profound influence on public as it informs about the events happening around them and may affect them. It keeps the people connected and allows them to engage in the decision making process. The words used in the news language are sometimes taken from the regional languages so as to express a new phenomenon, event or idea. In this paper, we have proposed a lexicon based framework named as TextGraph that automatically extracts the concepts from the Dawn news using the Term Frequency–Inverse Document Frequency (TF-IDF) weighting factor and visualizes them in a formal way. To achieve value-add insights, we have developed Pakistani English corpus and used it along with other existing dictionaries. Our proposed corpus incorporates the Pakistani English words used in the Dawn news stories, which are annotated and validated by a human expert. Experimental results show that our concept extraction method out performs and gives more specific concepts. Our research suggests that the proposed framework and corpus opens multiple directions for promising future research in this domain.
An Augmented Reality Framework for Eye Muscle Education

Due to the COVID-19 pandemic, the need for remote education is felt more than ever. New technologies such as Augmented Reality (AR) can improve students’ training experiences and directly affect the learning process, especially in remote education. By using AR in medical education, we no longer need to worry about patient safety during the education process because AR helps students see inside the human body without needing to cut human flesh in the real world. In this paper, we present an augmented reality framework that has the ability to add a virtual eye muscle to a person’s face in a single photo or a video. We go one step further to not just show the muscle of the eye but also customize it for each person by modeling the person’s face with a 3D morphable model (3DMM).
Europe in Search of a Universal Encyclopaedia as a Literary Genre (XIII-XVII Centuries)

The purpose of the study is to analyse the conditions for the formation of the encyclopaedia as a genre of science-based literature that shaped the ideas about scientific knowledge during the late Middle Ages and the Renaissance period, its compatibility with the specific socio-cultural values of the educated circle of the European language community. Scientific novelty lies in carrying out a comprehensive analysis of the stages of preparation of encyclopaedic editions as works of science-based literature, viewed as a phenomenon of the creative process, an element of authors’ creative biography, a manifestation of historical and literary movement in the development of literature in the XIII-XVII centuries. As a result, it has been proved that reference works of this period provided insight into the peculiarities of scholars’ scientific creative work, their spiritual image, the philosophical foundations and consequences of scientific discoveries, had an intellectual-cognitive and aesthetic value.
An Accurate Refinement Pathway for Visual Tracking

Recently, in the field of visual object tracking, visual object tracking algorithms combined with visual object segmentation have achieved impressive results while using mask to label targets in the VOT2020 dataset. Most of the trackers get the object mask by increasing the resolution through multiple upsampling modules and gradually get the mask by summing with the features in the backbone network. However, this refinement pathway does not fully consider the spatial information of the backbone features, and therefore, the segmentation results are not perfect. In this paper, the cross-stage and cross-resolution (CSCR) module is proposed for optimizing the segmentation effect. This module makes full use of the semantic information of high-level features and the spatial information of low-level features, and fuses them by skip connections to achieve a very accurate segmentation effect. Experiments were conducted on the VOT dataset, and the experimental results outperformed other excellent trackers and verified the effectiveness of the algorithm in this paper.
A Case Study of Developing and Running a Competency－Based General Education Class

This study aims to depict the process of developing and running a liberal arts course titled “Life and Dream.” This course was developed to guide the self-awareness of freshman students, along with their career paths, in accordance with the introduction of the “track system,” wherein students are instructed to choose two tracks (also known as sub-disciplines) as their majors with minimal restrictions. However, due to the absence of common textbooks, various complaints were raised about the large deviation between classes and the uncertainty of the class objectives. In the process of resolving these complaints, remarkable improvements were made in many ways. For one thing, &lt;i&gt;Life and Dream&lt;/i&gt; has been developed and utilized as a regular textbook. In addition, as various performance activities related to the textbook were carried out in class, the homogeneity of the class was greatly improved. The effects of these improvements can be proved through analysis of the results of two types of surveys conducted at the end of the semester. In a questionnaire asking the students how much the “Life and Dream” class contributed to the improvement of the core competencies targeted, most students responded that their target competencies, that is, their self-management and interpersonal skills, were improved. They also answered that they were able to find their own identities and to effectively collaborate with others through this class, and that it helped them to better design their careers paths, all of which are exactly consistent with the goals of this course.
Developing interactive computer-aided detection tools to support translational clinical research

Applying computer-aided detection (CAD) generated quantitative image markers has demonstrated significant advantages than using subjectively qualitative assessment in supporting translational clinical research. However, although many advanced CAD schemes have been developed, due to heterogeneity of medical images, achieving high scientific rigor of “black-box” type CAD schemes trained using small datasets remains a big challenge. In order to support and facilitate research effort and progress of physician researchers using quantitative imaging markers, we investigated and tested an interactive approach by developing CAD schemes with interactive functions and visual-aid tools. Thus, unlike fully automated CAD schemes, our interactive CAD tools allow users to visually inspect image segmentation results and provide instruction to correct segmentation errors if needed. Based on users’ instruction, CAD scheme automatically correct segmentation errors, recompute image features and generate machine learning-based prediction scores. We have installed three interactive CAD tools in clinical imaging reading facilities to date, which support and facilitate oncologists to acquire image markers to predict progression-free survival of ovarian cancer patients undergoing angiogenesis chemotherapies, and neurologists to compute image markers and prediction scores to assess prognosis of patients diagnosed with aneurysmal subarachnoid hemorrhage and acute ischemic stroke. Using these ICAD tools, clinical researchers have conducted several translational clinical studies by analyzing several diverse study cohorts, which have resulted in publishing seven peer-reviewed papers in clinical journals in the last three years. Additionally, feedbacks from physician researchers also indicate their increased confidence in using new quantitative image markers and help medical imaging researchers further improve or optimize interactive CAD tools.
Quantifying Reinforcement-Learning Agent’s Autonomy, Reliance on Memory and Internalisation of the Environment

Intuitively, the level of autonomy of an agent is related to the degree to which the agent's goals and behaviour are decoupled from the immediate control by the environment. Here, we capitalise on a recent information-theoretic formulation of autonomy and introduce an algorithm for calculating autonomy in a limiting process of time step approaching infinity. We tackle the question of how the autonomy level of an agent changes during training. In particular, in this work, we use the partial information decomposition (PID) framework to monitor the levels of autonomy and environment internalisation of reinforcement-learning (RL) agents. We performed experiments on two environments: a grid world, in which the agent has to collect food, and a repeating-pattern environment, in which the agent has to learn to imitate a sequence of actions by memorising the sequence. PID also allows us to answer how much the agent relies on its internal memory (versus how much it relies on the observations) when transitioning to its next internal state. The experiments show that specific terms of PID strongly correlate with the obtained reward and with the agent's behaviour against perturbations in the observations.
Research on Malicious URL Detection Based on Random Forest

Malicious URLs have become serious threats to cybersecurity, also forming incubators for Internet criminal activities. With visiting malicious URLs, visitors may undergo illegal actions such as spamming, phishing and drive-by downloads which seriously threat visitors&#x0027; privacy and security that cause losses of billions of dollars every year. Traditional methods such as using URL blacklists to detect malicious URLs can classify most of the known URLs but are poorly effective when processing newly generated ones. To forestall greater economic losses, it is imperative to exert a method that can classify URLs in a timely manner. To improve timeliness of detecting malicious URLs, we use machine learning algorithms to automatically classify URLs. In this article, we selected the experiment results of several common machine learning models on our data set as the baseline and compared them horizontally with the outcome of random forest classifier. After that, we optimize the classifier to make the random forest classifier to achieve the best outcome within the lowest complexity.
Characterizing Concurrency Mechanisms for NVIDIA GPUs under Deep Learning Workloads (Extended Abstract)

Hazelwood et al. observed that at Facebook data centers, variations in user activity (e.g. due to diurnal load) resulted in low utilization periods with large pools of idle resources [4]. To make use of these resources, they proposed using machine learning training tasks. Analagous lowutilization periods have also been observed at the scale of individual GPUs when using both GPU-based inference [1] and training [6]. The proposed solution to this latter problem was colocating additional inference or training tasks on a single GPU.We go a step further than these previous studies by considering the GPU at the microarchitectural level rather than treating it as a black box. Broadly, we consider the following question: are current GPU application- and block-level scheduling mechanisms sufficient to guarantee predictable and low turnaround times for latency-sensitive inference requests, while also consistently making use of unoccupied resources for best-effort training tasks? To answer this question, we explore both NVIDIA's concurrency mechanisms and the characteristics of the workload itself. Complicating our analyses, the NVIDIA scheduling hierarchy is proprietary and some mechanisms (e.g., time-slicing) are not well-documented, so their behavior must be reverseengineered from empirical observation.
Car Recommendation System for Dealers in Different European Countries

Recommendation systems are widely used in all walks of life, This paper is about the design of car recommendation of car rating for different European countries using collaborative filtering(CF). Compared with content-based filtering, collaborative filtering has the advantage that it can recommend items according to the historical information of each user, which has nothing to do with the content attributes of the item itself, which is the reason why we choose CF as our model to develop the recommendation system. The user model is designed using the brand, model, car sales, and country. In this paper, the model gives the similarity between the business pairs. The full model successfully gives the prediction of the top 5 sales cars in each country. The mean square error (MSE) of the whole model is 8.086, while the MSE of both predicts and tests with a rank larger than 3 is 0.4s241379. Without enough data, the MSE of the full model is a bit large. In the future, we could find more countries&#x0027; car data sets to improve the prediction accuracy and can also try to make a prediction for new markets.
Rule Analysis of Teaching Evaluation System Based on Data Mining under Web Platform

In order to understand the advantages of the teaching evaluation system to teachers and students more intuitively, according to the data analysis, the lowest total score of teachers is ˗1.04, and the five index values are all negative, so the relevant teaching contents should be changed according to the index factors of relevant teaching evaluation to make it easier for students to understand and accept. Through the analysis of the total evaluation scores of several teachers surveyed in each semester, we can see that the total evaluation scores of teachers 4 and 7 fluctuate greatly, so we should choose suitable teaching content for rectification. By classifying teachers, they are divided into three categories by means of discipline colleges, etc. It can be seen from the figure that the lowest total score of teachers in category 3 is ˗2.12, which shows that teachers in this category may have similar problems in teaching methods. By comparing the information entropy algorithm of teaching evaluation, we know that the information entropy under the AVF algorithm model takes about 1 ms to the teaching evaluation algorithm, while the greedy algorithm model takes about 20 ms to test the teaching evaluation system. We know that the teaching evaluation system based on the proposed algorithm information entropy algorithm and the teaching evaluation system under the AVF algorithm model and traditional information entropy all pass the test. However, combined with the images, the AVF algorithm model has the highest computational efficiency and the shortest time, followed by the proposed algorithm model. Compared with the traditional teaching evaluation algorithm, it is inefficient and takes a long time. When the number of requests is 1000, the average response time of the AVF algorithm method is 17 ms, and the longest time of the traditional method is 32 ms. Combined with images, when the number of requests is 1000, 1500, 200, 2500, 3000, 350, and 4000, the average response time increases with the increase of the number of requests, but the AVF algorithm method takes the shortest time and meets most teaching evaluation requirements. Combined with the success rate image, we can see that the success rate decreases slightly with the increase of request times, but the success rate of the AVF algorithm method is relatively stable, with the lowest of 99.6% when the request times are 3500. Through a large number of charts and analyses, we can see that the teaching evaluation under the AVF algorithm method has the highest efficiency and the shortest time.
Bridging the domain gap for medical image segmentation with multimodal MIND features

We demonstrate a new approach for blind domain adaptation by employing classic feature descriptors as a first step in a deep learning pipeline. One advantage of our approach over other domain adaptation methods is that no target domain data are required. Therefore, the trained models perform well on a multitude of different datasets as opposed to one specific target dataset. We test our approach on the task of abdominal CT and MR organ segmentation and transfer the models from the training dataset to multiple other CT and MR datasets. We show that modality independent neighborhood descriptors applied prior to a DeepLab segmentation pipeline can yield high accuracies when the model is applied on other datasets including those with a different imaging modality.
SmartDeal: Remodeling Deep Network Weights for Efficient Inference and Training

The record-breaking performance of deep neural networks (DNNs) comes with heavy parameter budgets, which leads to external dynamic random access memory (DRAM) for storage. The prohibitive energy of DRAM accesses makes it nontrivial for DNN deployment on resource-constrained devices, calling for minimizing the movements of weights and data in order to improve the energy efficiency. Driven by this critical bottleneck, we present SmartDeal, a hardware-friendly algorithm framework to trade higher-cost memory storage/access for lower-cost computation, in order to aggressively boost the storage and energy efficiency, for both DNN inference and training. The core technique of SmartDeal is a novel DNN weight matrix decomposition framework with respective structural constraints on each matrix factor, carefully crafted to unleash the hardware-aware efficiency potential. Specifically, we decompose each weight tensor as the product of a small basis matrix and a large structurally sparse coefficient matrix whose nonzero elements are readily quantized to the power-of-2. The resulting sparse and readily quantized DNNs enjoy greatly reduced energy consumption in data movement as well as weight storage, while incurring minimal overhead to recover the original weights thanks to the required sparse bit-operations and cost-favorable computations. Beyond inference, we take another leap to embrace energy-efficient training, by introducing several customized techniques to address the unique roadblocks arising in training while preserving the SmartDeal structures. We also design a dedicated hardware accelerator to fully utilize the new weight structure to improve the real energy efficiency and latency performance. We conduct experiments on both vision and language tasks, with nine models, four datasets, and three settings (inference-only, adaptation, and fine-tuning). Our extensive results show that 1) being applied to inference, SmartDeal achieves up to 2.44× improvement in energy efficiency as evaluated using real hardware implementations and 2) being applied to training, SmartDeal can lead to 10.56× and 4.48× reduction in the storage and the training energy cost, respectively, with usually negligible accuracy loss, compared to state-of-the-art training baselines. Our source codes are available at: https://github.com/VITA-Group/SmartDeal.
Intelligent Marketing Platform of Automobile Exhibition Hall Based on User Facial Expression Recognition based on Visual Tracking Algorithm

This paper designs the intelligent marketing platform of the automobile exhibition hall based on user facial expression recognition based on visual tracking algorithm. In view of the core position of kernel matrix in the research of kernel function, this paper takes kernel matrix as the research object, and analyzes the relationship between the distribution of data in feature space and the performance of kernel function. There is no expeiimental hypothesis for the social meaning of the face in the recognition process, and the researcher found that itis related to the requirements of the experiment. In the similarity judgment, the subject is required to make a quick judgment, so the subject only performed a preliminary processing of the human face, and did not perform in-depth processing and analysis. Regarding model selection, there has not been a unified international standard. In many cases, it is based on experience and has a certain degree of arbitrariness. Through these modelling steps, the designed methodis implemented.
Using patching asymmetric regions to assess ischemic stroke lesion in neuro imaging

Ischemic stroke is a universal ailment that endangers the life of patients and makes them bedridden until death. Over a decade, doctors and radiologists have been dissecting patient status straightforwardly from the printouts of the slice images delivered by different diagnostic imaging modalities. Computed Tomography (CT) is a frequently used imaging strategy for therapeutic analysis and neuroanatomical investigations. The main objective of the paper is to develop a simple technique with less architectural complication and power consumption. The proposed work is to section the ischemic stroke lesion more efficiently from multi-succession CT images using patching the asymmetric region. The Hough transform segment and extracts the features from the asymmetric region of the CT image and finally, the random forest is implemented to classify the unusual tissues from the CT image dependent on their pathological properties. RF classifier has been trained for different parts of the cerebrum for fragmenting the stroke lesion. The acquired outcomes produce better segmentation accuracy when compared with different strategies. The overall efficiency of the proposed method determines the Ischemic stroke with an accuracy of 95% with an RF classifier. Hence this method can be used in the segmentation process of stroke lesions.
Deep Neural Network Training With Distributed K-FAC

Scaling deep neural network training to more processors and larger batch sizes is key to reducing end-to-end training time; yet, maintaining comparable convergence and hardware utilization at larger scales is challenging. Increases in training scales have enabled natural gradient optimization methods as a reasonable alternative to stochastic gradient descent and variants thereof. Kronecker-factored Approximate Curvature (K-FAC), a natural gradient method, preconditions gradients with an efficient approximation of the Fisher Information Matrix to improve per-iteration progress when optimizing an objective function. Here we propose a scalable K-FAC algorithm and investigate K-FAC’s applicability in large-scale deep neural network training. Specifically, we explore layer-wise distribution strategies, inverse-free second-order gradient evaluation, and dynamic K-FAC update decoupling, with the goal of preserving convergence while minimizing training time. We evaluate the convergence and scaling properties of our K-FAC gradient preconditioner, for image classification, object detection, and language modeling applications. In all applications, our implementation converges to baseline performance targets in 9–25% less time than the standard first-order optimizers on GPU clusters across a variety of scales.