Reviewerly/exHarmony huggingface dataset:
Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization

Knowledge Tracing (KT), which aims to model student knowledge level and predict their performance, is one of the most important applications of user modeling. Modern KT approaches model and maintain an up-to-date state of student knowledge over a set of course concepts according to students’ historical performance in attempting the problems. However, KT approaches were designed to model knowledge by observing relatively small problem-solving steps in Intelligent Tutoring Systems. While these approaches were applied successfully to model student knowledge by observing student solutions for simple problems, such as multiple-choice questions, they do not perform well for modeling complex problem solving in students. Most importantly, current models assume that all problem attempts are equally valuable in quantifying current student knowledge. However, for complex problems that involve many concepts at the same time, this assumption is deficient. It results in inaccurate knowledge states and unnecessary fluctuations in estimated student knowledge, especially if students guess the correct answer to a problem that they have not mastered all of its concepts or slip in answering the problem that they have already mastered all of its concepts. In this paper, we argue that not all attempts are equivalently important in discovering students’ knowledge state, and some attempts can be summarized together to better represent student performance. We propose a novel student knowledge tracing approach, Granular RAnk based TEnsor factorization (GRATE), that dynamically selects student attempts that can be aggregated while predicting students’ performance in problems and discovering the concepts presented in them. Our experiments on three real-world datasets demonstrate the improved performance of GRATE, compared to the state-of-the-art baselines, in the task of student performance prediction. Our further analysis shows that attempt aggregation eliminates the unnecessary fluctuations from students’ discovered knowledge states and helps in discovering complex latent concepts in the problems.
Interactive Hyperparameter Optimization with Paintable Timelines

We propose a method to integrate more interactivity into automatic hyperparameter optimization systems to leverage the user's prior knowledge on parameter distribution. In our method, the user continuously observes automatic optimization's progress and dynamically specifies where to search in the parameter space. We present a prototype implementation of an interactive dashboard for an optimizer to show our method's feasibility. The interactive dashboard's main feature is "paintable timeline" where the user can not only observe the past parameter values tested as in standard timeline but also specify the range of future parameters to be tested with simple painting operations. We show three examples where user intervention might improve the performance of automatic optimizations. We run a user study with experts and the results show that, with prior knowledge about parameter distribution of the target problem, interactive optimization can reach better results compared to fully automatic optimization.
Work in Progress: An Automated Management System for References in Programming Code

Code reuse is a practice that may both support and hinder the learning process of programming students. To help offset the negative impacts of code reuse, many educators rely on code similarity detection tools as a first step in detecting code plagiarism. An alternative approach, often applied in parallel, is to help deter plagiarism by teaching students the importance of referencing externally sourced code. However, there are no broadly accepted standards for referencing in programming. We have found one standard proposed in the literature, and in this paper we explain the design of a system to help programmers apply that standard. The system we will build is a semi-automated code comment generation system that will rely on a code clone detection approach for detecting similarities between the student's code and the code at a website whose URL the student provides. This will assist the user to create appropriate references, in the form of inline comments, when they reuse code from websites or other sources. This work in progress paper explores the relevant literature, explains the design choices of the system and the plan for its evaluation, and presents a progress report on the work.
Innovation Is Tied to Optimism

“ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">At the height of laughter, the universe is flung into a kaleidoscope of new possibilities</i> .”—Jean Houston
Exploiting Weight Statistics for Compressed Neural Network Implementation on Hardware

Computing hardware like field programmable gate arrays (FPGAs), microcontrollers and microprocessors can have limited compute and on-chip storage resources. This is especially true for computing hardware in Internet of Things (IoT) and low end embedded systems. With the growth in machine and deep learning, it is imperative to build intelligence in these devices. Therefore, this paper proposes exploiting weight statistics to compress floating point based weights in neural networks without any loss in accuracy. The proposed method has been implemented as an optimization pass in open source N2D2 framework. The proposed method thus does not make an assumption that the application can tolerate some accuracy loss which is the case with other methods like quantization, binary weights etc. However, it can also be considered as a further step in optimization after applying existing quantization based methods. The proposed method is able to save nearly 10% on-chip storage requirement, thus reducing the number of Block RAMs (BRAM) in case of FPGAs and the size of on-chip memory (OCM) in case of microcontrollers and microprocessors. We show that layer wise compression gives slightly better compression than global compression. This compression is traded off for execution time overhead in microcontrollers.
Research on the Application Status and Countermeasures of College English Blended Teaching Model under Big Data

Absrtact Under the background of the development of big data, the mixed teaching mode has been widely used in college English teaching, which can make full use of the advantages of big data and realize the sharing of teaching resources. Especially for the English teaching of science and engineering major, the application of big data can provide students with more opportunities for autonomous learning and practical application, which greatly enhances the English thinking ability, inquiry ability and application ability of college students. This paper analyzes the role of hybrid teaching in college English teaching under big data, probes into the present situation of hybrid teaching, and puts forward the strategy of applying hybrid teaching in college English.
Enhanced texture classification through feature compaction using dihybrid bio‐inspired computation techniques

Summary Synchronous data processing is often preferred considering the volume of data available and the requirement of processed data. Data volume increase from current technical advancements should be balanced and suitably dealt with efficient feature processing techniques. Feature selection can efficiently improve the concurrency in data processing on a completely uncorrelated and separately processable feature set. Texture analysis is a significant application of image processing that analyses specific patterns in an image. As the volume of image data available for producing better classification models progressively moves to be out of the human‐manageable range, it is the automation methods that aid the researchers to manage the data. When the number of samples in the data and the dimensionality of the problem is too high, the purpose of data analysis is often compromised. Feature engineering can be utilized to improve the efficiency of the model with a smaller subset of features. This work proposes dibyhrid bio‐inspired computation based feature compaction, that is a combination of improvised genetic algorithm and selective tabu search for texture classification problem. Machine learning techniques are employed to evaluate and minimize the validation error from the applied computational methods.
Supermartingales, Ranking Functions and Probabilistic Lambda Calculus

We introduce a method for proving almost sure termination in the context of lambda calculus with continuous random sampling and explicit recursion, based on ranking supermartingales. This result is extended in three ways. Antitone ranking functions have weaker restrictions on how fast they must decrease, and are applicable to a wider range of programs. Sparse ranking functions take values only at a subset of the program's reachable states, so they are simpler to define and more flexible. Ranking functions with respect to alternative reduction strategies give yet more flexibility, and significantly increase the applicability of the ranking supermartingale approach to proving almost sure termination, thanks to a novel (restricted) confluence result which is of independent interest. The notion of antitone ranking function was inspired by similar work by McIver, Morgan, Kaminski and Katoen in the setting of a first-order imperative language, but adapted to a higher-order functional language. The sparse ranking function and confluent semantics extensions are unique to the higher-order setting. Our methods can be used to prove almost sure termination of programs that are beyond the reach of methods in the literature, including higher-order and non-affine recursion.
Towards Understanding The Space of Unrobust Features of Neural Networks

Despite the convolutional neural network has achieved tremendous monumental success on a variety of computer vision-related tasks, it is still extremely challenging to build a neural network with doubtless reliability. Previous works have demonstrated that the deep neural network can be efficiently fooled by human imperceptible perturbation to the input, which actually revealed the instability for interpolation. Like human-beings, an ideally trained neural network should be constrained within desired inference space and maintain correctness for both interpolation and extrapolation. In this paper, we develop a technique to verify the correctness when convolutional neural networks extrapolate beyond training data distribution by generating legitimated feature broken images, and we show that the decision boundary for convolutional neural network is not well formulated based on image features for extrapolating.
Diagnostic concordance among pathologists interpreting breast core biopsies on secondary review over a 1‐year period at an Australian tertiary hospital

This study provides data on the diagnostic concordance between initial and review diagnoses of all breast core biopsy cases at a single tertiary hospital in Western Australia over a 1-year period. A retrospective review of all breast core biopsy cases between January 1 and December 31, 2016, was carried out at PathWest, Fiona Stanley Hospital in Perth, Western Australia. Each biopsy is reported by a single pathologist and then reviewed within 1 week by a panel of intradepartmental subspecialist breast pathologists, who either agree with the original diagnosis, have a minor discordant diagnosis, or a major discordant diagnosis. Records for 2036 core biopsies were available between January 1 and December 31, 2016. Of these, 56.0% (n = 1141) were classified as benign, 34.3% (n = 699) as malignant, 7.2% (n = 147) as indeterminate, 2.3% (n = 46) as nondiagnostic, and 0.1% (n = 3) as suspicious for malignancy. In 99.1% (n = 2018) of cases, there was agreement between initial and review diagnoses. In total, 0.9% (n = 18) were disagreements: 0.49% (n = 10) were major discordant disagreements and 0.39% (n = 8) were minor discordant disagreements. All cases of major discordant disagreements would have resulted in significant changes to clinical management. This study demonstrates that an Australian institution is providing a high-quality pathology service with a low error rate between initial and review diagnoses of breast core biopsies. It reinforces the importance of secondary review of biopsies in a timely fashion for detecting potentially serious misdiagnoses that could lead to inappropriate management.
Remote Monitoring Application for Automatic Power Supply System in Telecommunication Network

Abstract The source of backup power supply at telecommunication BTS is only temporary because its power only depends on the generator and battery capacity used. However, on the other hand, the backup power system is rarely monitored due to a lack of resources such as human resources and the remote location of the equipment/site. Meanwhile, the use of Android which is open source is now widely used by the public for many things, but there are still not many Android applications used to control the power supply system. The purpose of this research is to design and analyze the automatic backup power system on the integrated power system in the telecommunication network by adding an Android application to monitor the occurrence of faults in the system. The communication used in this research is the connection of the automatic backup power control circuit, the microcontroller, the webserver, and the android application itself. Automatic backup power control circuit in the form of a main power transfer control circuit, for example, the power from State Electricity Company (PLN), if a problem occurs it will switch to the back-up power in the form of a generator and battery. The results show that the design of the automatic power system device can work properly.
Using Partitioned-Based Method for Optimal Epsilon Parameter Extraction on Density-Based Clustering

Clustering is vital and striking in data mining, a powerful tool to analyze an incredible volume of data created by the progress in applications. The DBSCAN clustering is a well-known successful algorithm, which has features that are dependent on the density notion of objects within clusters. It can find clusters of the highest dimensionality as long as high dense regions exist, and find clusters with different shapes. Besides, it is robust in dealing with noise and large spatial datasets, so that it is important and succeeded in dealing with large datasets, i.e., big data. This paper aims at proposing an enhanced version of the algorithm, called eDBSCAN, to gain the suitable input parameter (Eps) on the classical DBSCAN algorithm using the partitioning method. We used the MC-means algorithm in the extraction of the optimal Eps value. The experimental study proves that the eDBSCAN algorithm is efficient in determining the Eps value, through a variety density included in a data set. The results achieved in purity indicate the Eps value effectiveness and accuracy to 100% percent on the data sets used.
Towards Immersive and Social Audience Experience in Remote VR Opera

Opera is a historic art that struggles to be approachable to modern audiences. In partnership with the Irish National Opera (INO), this work considers how VR may be used to develop a new form of immersive opera. To this end, we ran three open-ended focus groups to consider how creative, multisensory, and social VR technology may be employed in digital opera. Our findings assert the importance of creating an immersive experience by safely giving audiences agency to interact, to democratize personal and social experiences, and to consider different ways of representing their bodies, their social rituals, and the virtual social space. Using these findings, we envision a new form of VR opera that couples physical traditions with digital affordances.
A Fast Thinning Algorithm of Square Hmong Character Handwriting Using Template Matching Mechanism

According to the similar features of word-information principles between Chinese and Square Hmong character, this manuscript proposes a fast thinning algorithm based on template matching mechanism to obtain the skeleton of the square Hmong character handwriting accurately. First, the index templates are optimized in this presented algorithm by analysing the relative positions between the centre pixel and neighbouring pixels and the information of the handwriting nodes. Then, the initial shinning operation is implemented based on considering the stroke trend with the refinement of square Hmong character handwriting binary image. Furthermore, a secondary shinning operation is used to deal with short-stemmed and non-single-pixel problems which could be appealed after refinement. The experimental results show that the proposed algorithm owns the ability to accurately define the framework of the square Hmong character handwriting skeleton on the basis of effectively reducing the complexity of the algorithm, and provide effective pre-processing for the character recognition of Square Hmong Character Handwriting.
Summary Grounded Conversation Generation

Many conversation datasets have been constructed in the recent years using crowdsourcing.However, the data collection process can be time consuming and presents many challenges to ensure data quality.Since language generation has improved immensely in recent years with the advancement of pretrained language models, we investigate how such models can be utilized to generate entire conversations, given only a summary of a conversation as the input.We explore three approaches to generate summary grounded conversations, and evaluate the generated conversations using automatic measures and human judgements.We also show that the accuracy of conversation summarization can be improved by augmenting a conversation summarization dataset with generated conversations.
Digital learning at the University of Architecture and Civil Engineering: psychological risks and effects

The article examines the problem of digital education in high school in terms of its psychological risks and effects. The aim of the work is to identify the psychological effects of the transformation of higher education in the digital age and to determine the priority trends of psychological support in the digital educational process. In the theoretical aspect of this study the analysis of the problem of psychological risks and challenges of digital education in high school is presented on the basis of domestic and foreign experience of transition to a remote format of education. Methods of theoretical analysis of modern sources on this problem were applied, in particular, cognitive and personal-developing approach to professional and professional-pedagogical education. The author refers to the separate articles on digital education in high school. The work analyzes the sociopsychological aspects of digital education reflecting the person-oriented system risks, communicative competence risks, individual psychological security risks and a new type of students in the digital age. The article reveals only some of the psychological effects of digitalization discussed in studies on this issue. As a result, the priority trends of digital education psychological effects in high school were identified.
Edge Sparsification for Graphs via Meta-Learning

We present a novel edge sparsification approach for semi-supervised learning on undirected and attributed graphs. The main challenge is to retain few edges while minimizing the loss of node classification accuracy. The task can be mathematically formulated as a bi-level optimization problem. We propose to use meta-gradients, which have traditionally been used in meta-learning, to solve the optimization problem, specifically, treating the graph adjacency matrix as hyperparameters to optimize. Experimental results show the effectiveness of the proposed approach. Remarkably, with the resulting sparse and light graph, in many cases the classification accuracy is significantly improved.
On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.
Skid Row Power Now!

This case study describes the co-design of Charge 'n Chill, a charging cart serving houseless residents of Skid Row. This project brought together community activists, Skid Row residents, and university researchers over the course of an 8-week intensive co-design workshop that culminated in the unveiling of a working prototype in the community. Through this case study, we describe the projects' goals and motivations, as well as the participatory methods which guided our work together. We analyze both our process and its outcome, highlighting lessons we learned and guidelines we offer to others engaged in similar collaborations.
Work-in-Progress: Data Science Framework for Environmental Protection Education

In recent decades, the overpopulation on our planet has had an impact on environmental change. Addressing and understanding such changes in a reliable manner requires contemporary data-science knowledge and methodologies since the amount of information available online has been increasing at an exponential rate. Additionally, to draw meaningful conclusions and predict certain events in terms of environmental changes, one must be familiar with existing natural processes. Thus, combining data science with knowledge about global conditions in the world and how they impact Earth's biosphere, would highly benefit the understanding of possible consequences in the environment. Since our planet is a complex, dynamic system that is likely to change, one must be knowledgeable about the global climate system as a whole. As well, one must understand its processes and interactions with the environment to be able to understand and work on preventing the potential inevitable drastic circumstances for living. This paper addresses the issue of environmental changes and presents a solution in the form of academic education, which is currently in progress, and also related to the COVID-19 issues. As shown in the results section, this type of education aims to teach students about the basics of data science and how to interpret and visualize information related to current environmental changes.
Sefamerve R&amp;D at ICICS 2021 Mowjaz Multi-Topic Labelling Task

This paper describes our contribution to ICICS 2021 Mowjaz Multi-Topic Labelling Task. The purpose of the task is to classify Arabic articles based on their topics. Participating systems are expected to select one or more of the determined topics for each given article. In our system, we experiment with state-of-art pre-trained language models (GigaBERT-v4 and Arabic BERT) and a classical logistic regression to find the best effective model for the problem. We obtained the highest F1-score of 0.8563 with GigaBERT-v4 while Arabic-BERT and logistic regression reached 0.8442 and 0.8081 respectively. Our system ranked 2nd in the competition very close to the winner.