Reviewerly/exHarmony huggingface dataset:
GKT-CD: Make Cognitive Diagnosis Model Enhanced by Graph-based Knowledge Tracing

Recent advancements in online education platforms have caused an increase in research on adaptive learning system, wherein student performance on coursework exercises is predicted over time and directed exercises are recommended. In adaptive learning systems, knowledge tracing and cognitive diagnosis are critical techniques for predicting student performance. The traditional cognitive diagnosis model's terms are suitable for the student abilities analysis, but they rely on handcrafted interaction functions and only use student's response records so that it is difficult to capture the dynamic knowledge mastery ability of students. Although using the knowledge tracing to enhance cognitive diagnosis is a meaningful attempt towards towards capturing student performance, the RNN-based know-eledge tracing model have limited effect. This paper proposes a new model, named GKT-CD, which fuses knowledge tracing and cognitive diagnosis in a synergistic framework. In GKT-CD, we develop Gated-GNN to trace the student-knowledge response records and extract students' latent trait. And then, we use hierarchical structure in knowledge to construct exercise latent vector. At last, we use two-dimensional item response theory (IRT) to predict the probability of students answering exercises correctly. Extensive experiments conducted on realworld datasets show that the GKT-CD model is feasible and obtain excellent performance.
CAN3D: Fast 3D medical image segmentation via compact context aggregation

Direct automatic segmentation of objects in 3D medical imaging, such as magnetic resonance (MR) imaging, is challenging as it often involves accurately identifying multiple individual structures with complex geometries within a large volume under investigation. Most deep learning approaches address these challenges by enhancing their learning capability through a substantial increase in trainable parameters within their models. An increased model complexity will incur high computational costs and large memory requirements unsuitable for real-time implementation on standard clinical workstations, as clinical imaging systems typically have low-end computer hardware with limited memory and CPU resources only. This paper presents a compact convolutional neural network (CAN3D) designed specifically for clinical workstations and allows the segmentation of large 3D Magnetic Resonance (MR) images in real-time. The proposed CAN3D has a shallow memory footprint to reduce the number of model parameters and computer memory required for state-of-the-art performance and maintain data integrity by directly processing large full-size 3D image input volumes with no patches required. The proposed architecture significantly reduces computational costs, especially for inference using the CPU. We also develop a novel loss function with extra shape constraints to improve segmentation accuracy for imbalanced classes in 3D MR images. Compared to state-of-the-art approaches (U-Net3D, improved U-Net3D and V-Net), CAN3D reduced the number of parameters up to two orders of magnitude and achieved much faster inference, up to 5 times when predicting with a standard commercial CPU (instead of GPU). For the open-access OAI-ZIB knee MR dataset, in comparison with manual segmentation, CAN3D achieved Dice coefficient values of (mean = 0.87 ± 0.02 and 0.85 ± 0.04) with mean surface distance errors (mean = 0.36 ± 0.32 mm and 0.29 ± 0.10 mm) for imbalanced classes such as (femoral and tibial) cartilage volumes respectively when training volume-wise under only 12G video memory. Similarly, CAN3D demonstrated high accuracy and efficiency on a pelvis 3D MR imaging dataset for prostate cancer consisting of 211 examinations with expert manual semantic labels (bladder, body, bone, rectum, prostate) now released publicly for scientific use as part of this work.
Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows

Deep learning-based regression and classification models are used in most subareas of neuroimaging because of their accuracy and flexibility. While such models achieve state-of-the-art results in many different applications scenarios, their decision-making process is usually difficult to explain. This black box behaviour is problematic when non-technical users like clinicians and patients need to trust them and make decisions based on their results. In this work, we propose to build self-explainable generative classifiers and regressors using a flexible and efficient normalizing flow framework. We directly exploit the invertibility of those normalizing flows to explain the decision-making process in a highly accessible way via consistent and spatially smooth attribution maps and counterfactual images for alternate prediction results. The evaluation using more than 5000 3D MR images highlights the explainability capabilities of the proposed models and shows that they achieve a similar level of accuracy as standard convolutional neural networks for image-based brain age regression and brain sex classification tasks.
Multi-Temporal Convolutions for Human Action Recognition in Videos

Effective extraction of temporal patterns is crucial for the recognition of temporally varying actions in video. We argue that the fixed-sized spatio-temporal convolution kernels used in convolutional neural networks (CNNs) can be improved to extract informative motions that are executed at different time scales. To address this challenge, we present a novel convolution block that is capable of extracting spatio-temporal patterns at multiple temporal resolutions. Our proposed multi-temporal convolution (MTConv) blocks utilize two branches that focus on brief and prolonged spatio-temporal patterns, respectively. The extracted time-varying features are aligned in a third branch, with respect to global motion patterns through recurrent cells. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture. This introduces a substantial reduction in computational costs. Extensive experiments on Kinetics, Moments in Time and HACS action recognition benchmark datasets demonstrate competitive performance of MTConvs compared to the state-of-the-art with a significantly lower computational footprint <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Our code is available at: https://git.io/JfuPi.
Fine-Grained Implicit Sentiment in Financial News: Uncovering Hidden Bulls and Bears

The field of sentiment analysis is currently dominated by the detection of attitudes in lexically explicit texts such as user reviews and social media posts. In objective text genres such as economic news, indirect expressions of sentiment are common. Here, a positive or negative attitude toward an entity must be inferred from connotational or real-world knowledge. To capture all expressions of subjectivity, a need exists for fine-grained resources and approaches for implicit sentiment analysis. We present the SENTiVENT corpus of English business news that contains token-level annotations for target spans, polar spans, and implicit polarity (positive, negative, or neutral investor sentiment, respectively). We both directly annotate polar expressions and induce them from existing schema-based event annotations to obtain event-implied implicit sentiment tuples. This results in a large dataset of 12,400 sentiment–target tuples in 288 fully annotated articles. We validate the created resource with an inter-annotator agreement study and a series of coarse- to fine-grained supervised deep-representation-learning experiments. Agreement scores show that our annotations are of substantial quality. The coarse-grained experiments involve classifying the positive, negative, and neutral polarity of known polar expressions and, in clause-based experiments, the detection of positive, negative, neutral, and no-polarity clauses. The gold coarse-grained experiments obtain decent performance (76% accuracy and 63% macro-F1) and clause-based detection shows decreased performance (65% accuracy and 57% macro-F1) with the confusion of neutral and no-polarity. The coarse-grained results demonstrate the feasibility of implicit polarity classification as operationalized in our dataset. In the fine-grained experiments, we apply the grid tagging scheme unified model for &lt;polar span, target span, polarity&gt; triplet extraction, which obtains state-of-the-art performance on explicit sentiment in user reviews. We observe a drop in performance on our implicit sentiment corpus compared to the explicit benchmark (22% vs. 76% F1). We find that the current models for explicit sentiment are not directly portable to our implicit task: the larger lexical variety within implicit opinion expressions causes lexical data scarcity. We identify common errors and discuss several recommendations for implicit fine-grained sentiment analysis. Data and source code are available.
Guiding Neural Machine Translation with Retrieved Translation Template

While various neural machine translation (NMT) methods have integrated multiple prior knowledge to guide the translation, no research is available on combining with source-target bilingual translation template. In this paper, we firstly propose a maximal-length noun phrase template (MNP-Template), which constructs a novel translation template focusing on the constituency syntactic structure. Secondly, building on the multi-source transformer framework, we design a template-based machine translation (TBMT) model to integrate the syntactic knowledge of the retrieved target template similar to the ground-truth translation in the NMT decoder. Experiment results show the effectiveness of MNP- Template and TBMT on test subsets filtered by the fuzzy match score. Moreover, our method achieves significant improvement in out-of-domain test sets, which well-validated the university across diverse domains.
Two-Level Regression Method Using Ensembles of Trees with Optimal Divergence

Abstract The article discusses a new two-level regression analysis method in which a corrective procedure is applied to optimal ensembles of regression trees. Optimization is carried out based on the simultaneous achievement of the divergence of the algorithms in the forecast space and a good approximation of the data by individual algorithms of the ensemble. Simple averaging, random regression forest, and gradient boosting are used as corrective procedures. Experiments are presented comparing the proposed method with the standard decision forest and the standard gradient boosting method for decision trees.
Learning Adaptations for Case-Based Classification: A Neural Network Approach

Case-based Reasoning (CBR) solves a new problem by retrieving a stored case for a similar problem and adapting its solution to fit. Acquiring the required case adaptation knowledge is a classic problem. A popular method for addressing it is the case difference heuristic (CDH) approach, which learns adaptations from pairs of cases based on their problem differences and solution differences. The CDH approach was originally used to generate adaptation rules, but recent CBR research on case-based regression has investigated replacing learning rules with learning CDH-based network models for adaptation. This paper presents and evaluates a neural network-based CDH approach for learning adaptation models for classification, C-NN-CDH. It examines three variants, (1) training a single neural network on problem-solution differences, (2) segmenting adaptation knowledge by the classes of source cases, with a separate neural network to generate adaptations for each class, and (3) adapting from an ensemble of source cases and taking the majority vote. Experimental results demonstrate improved performance compared to previous research on statistical methods for computing CDH differences for classification. Additional results support that C-NN-CDH achieves classification performance comparable to that of multiple classic classification approaches.
Digital investigations: relevance and confidence in disclosure

Abstract The field of digital forensics has grown exponentially to include a variety of digital devices on which digitally stored information can be processed and used for different types of crimes. As a result, as this growth continues, new challenges for those conducting digital forensic examinations emerge. Digital forensics has become mainstream and grown in importance in situations where digital devices used in the commission of a crime need examining. This article reviews existing literature and highlights the challenges while exploring the lifecycle of a mobile phone examination and how the disclosure and admissibility of digital evidence develops.
Mental Workload Evaluation of Virtual Object Manipulation on WebVR: An EEG Study

Virtual object manipulation as a key feature has been studied in virtual reality (VR) environments. Previous studies highlighted user experience on three basic types of virtual object manipulation, translation, rotation and scaling. However, prior literature mainly studied task performance in manipulation modes with different degrees of freedom (DoF), and few studies assessed user experience by evaluating the psychological response, such as mental workload on these three basic manipulation types in virtual environments. This paper compared manipulation modes with 1DoF and 3DoF to assess users’ mental workload as a critical indicator of user experience by electroencephalogram (EEG) measurement and questionnaires in manipulation tasks on the webpage with VR effects (also known as WebVR). By applying signal processing and statistical methods to analyze EEG data from ten subjects, the results demonstrated that the participants generally perceive less mental workload by 1DoF manipulation modes than 3DoF on WebVR. Besides, this study also found some different results between objective and subjective data.
Sketch to portrait generation with generative adversarial networks and edge constraint

A novel method for generating color portraits from sketch images using the edge constraint algorithm and generative adversarial networks (GANs) is proposed in this paper. In converting sketches into color portraits, the details of portrait output by GANs are often blurred and unrealistic. A new method with edge constraint is proposed in this work to address this issue. The image generated from generator network and its edge generated from the followed edge network are combined and provided to the discriminator for authenticity identification. Experiments show that the portrait output by the proposed method provides a more clear and realistic edge than a Pix2Pix model and has a better ability to generate color portraits from sketches compared with other common methods. The average structural similarity index measure (SSIM) value of the proposed method is 82.78%, while the values obtained by other methods and Pix2Pix are 42.99% and 78.60%, respectively.
FedOVA: One-vs-All Training Method for Federated Learning with Non-IID Data

Federated Learning (FL) is a privacy-oriented framework that allows distributed edge devices to jointly train a shared global model without transmitting their sensed data to centralized servers. FL aims to balance the naturally conflicting objectives of obtaining massive amounts of data while protecting sensitive information. However, the data stored locally on each edge device are typically not independent and identically distributed (non-IID). Such data heterogeneity poses a severe statistical challenge for the optimization and convergence of the global model. In response to this issue, we propose Federated One-vs-All (FedOVA), an efficient FL algorithm that first decomposes a multi-class classification problem into more straightforward binary classification problems and then combines their respective outputs using ensemble learning. Experiments on several public datasets show that FedOVA achieves higher accuracy and faster convergence than federated averaging and data sharing. Furthermore, our approach can support practical settings with a large number of clients (up to 1000 clients) in FL.
SF-LG: Space-Filling Line Graphs for Visualizing Interrelated Time-series Data on Smartwatches

Multiple embedded sensors enable smartwatch apps to amass large amounts of interrelated time-series data simultaneously, such as heart rate, oxygen levels or steps walked. Visualizing multiple interlinked datasets is possible on smartphones but remains challenging on small smartwatch displays. We propose a new technique, the Space-Filling Line Graph (SF-LG), that preserves the key visual properties of time-series graphs while making available space on the display to augment such graphs with additional information. Results from our first study (N=30) suggest that, while SF-LG makes available additional space on the small display, it also enables effective (i.e. quick and accurate) comprehension of key line graph tasks. We next implement a greedy algorithm to embed auxiliary information in the most suitable regions on the display. In a second study (N=27), we find that participants are efficient at locating and linking interrelated content using SF-LG in comparison to two baselines approaches. We conclude with guidelines for smartwatch space maximization for visual displays.
A Hybrid Capsule Network-based Deep Learning Framework for Deciphering Ancient Scripts with Scarce Annotations: A Case Study on Phoenician Epigraphy

A hybrid capsule network-based deep learning framework for deciphering ancient scripts with scarce annotations is presented. To verify the feasibility of our proposed framework, the Phoenician epigraphy is used as a case study. A corpus of labeled data of Phoenician alphabets that covers all different styles and stages is presented. This corpus can help in contributing to the digitization process of the Phoenician culture. This dataset is preprocessed by performing conventional pre-processing techniques and then processed and augmented using a hybrid architecture of autoencoders that preserves its human-like nature. The augmented dataset is fed to a custom capsule network in order to decipher the Phoenician character and classify it into one of the 22 alphabets. Our model achieves state-of-the-art performance in recognizing handwritten characters with an overall accuracy of 0.9891 and a loss of 0.021. Therefore, our model can help develop an automated deciphering system to save epigraphists' valuable time and effort in deciphering the Phoenician epigraphy in a short period. Moreover, this work can be replicated for any other ancient scripts with minor modifications considering the systematic methodology that we proposed since it has proven its effectiveness in deciphering the Phoenician epigraphy. Our model can be employed as a transfer learning backbone for recognizing other existing alphabets which suffer from a lack of annotated data.
Neural Topic Models for Hierarchical Topic Detection and Visualization

Given a corpus of documents, hierarchical topic detection aims to learn a topic hierarchy where the topics are more general at high levels of the hierarchy and they become more specific toward the low levels. In this paper, we consider the joint problem of hierarchical topic detection and document visualization. We propose a joint neural topic model that can not only detect topic hierarchies but also generate a visualization of documents and their topic structure. By being able to view the topic hierarchy and see how documents are visually distributed across the hierarchy, we can quickly identify documents and topics of interest with desirable granularity. We conduct both quantitative and qualitative experiments on real-world large datasets. The results show that our method produces a better hierarchical visualization of topics and documents while achieving competitive performance in hierarchical topic detection, as compared to state-of-the-art baselines.
Object Detection on TPU Accelerated Embedded Devices

Modern edge devices are capable of onboard processing of computational heavy tasks, such as artificial intelligence-driven computer vision. An increasing number of deep learning-based object detection networks are frequently proposed with lightweight structures to be deployed on mobile platforms without the need for cloud computing. Comparing these networks is challenging due to the variety in hardware and frameworks and because of different model complexity. This paper investigates models that can be deployed on cross-functional single-board computers without utilizing the power of GPUs. This paves the way towards performing accurate, cheap, and fast object detection, even suited for industrial applications within Industry 4.0. Four state-of-the-art neural networks are trained via transfer learning, then deployed and tested on the Raspberry Pi 4B and the Coral Edge TPU accelerator from Google as a co-processor. The comparison of the models focuses on the inference time, the versatility of the deployment, training, and finally the accuracy of the retrained networks on a selection of datasets with different feature characteristics.Our code can be found in the following repository: https://github.com/kberci/Deep-Learning-based-Object-Detection.
Digital opportunities in the healthcare enterprises during COVID-19: An empirical analysis of the developing country

The health sector in Bangladesh is yet to develop and provide universal healthcare services. The aim of this study is to investigate whether the applicability of digitization especially medical robots and blockchain technology can help to improve healthcare enterprises in Bangladesh during the ongoing COVID-19 pandemic. The findings indicate that Bangladesh healthcare enterprises are in a vulnerable situation because of unethical work practices of health workers, the need for medical robots, artificial intelligence, and blockchain technology to improve healthcare management. The study suggests that large investment, pro-patient care, corruption-free and ethical services in the healthcare management and service delivery is required, through joint collaboration with the public and the private sectors and also collaborative effort from the foreign sectors to implement the fourth industrial revolution in healthcare enterprises of the country
From Poacher to Protector of Attention: The Therapeutic Turn of Persuasive Technology and Ethics of a Smartphone Habit-breaking Application

This paper critically investigates the ethical perspectives and practices of individuals and organizations who make persuasive technologies (“persuasive technologists”). An organization that claims to be at the forefront of ethical persuasion is behavioral software company Boundless Mind. Yet Boundless Mind sells ostensibly oxymoronic software products: an Application Programming Interface for third-party applications that optimizes the capture of end user attention, and an application for end users on how to make third-party applications less persuasive. Drawing upon Foucault’s interpretation of ethics as an “aesthetics of existence” and the related concept of “therapeutic authority,” I argue Boundless Mind justify the “poaching” and “protecting” of user attention based on a view of the human subject as fixable and their capability to instrumentalize user subjectivity to socially desirable ends. I walkthrough Boundless Mind’s technology-habit-breaking application Space and highlight a behavioral technique administered by Space called stimulus devaluation, which enables the user to develop a transformative relationship with their technology habits and persuasive applications. I conclude the paper by arguing that a persuasive technology ethics based on fixing the user obfuscates the power of persuasive technologists by limiting the scope of ethical inquiry to the activities of the user.