Reviewerly/exHarmony huggingface dataset:
Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

Mycosis fungoides (MF) is a rare, potentially life threatening skin disease, which in early stages clinically and histologically strongly resembles Eczema, a very common and benign skin condition. In order to increase the survival rate, one needs to provide the appropriate treatment early on. To this end, one crucial step for specialists is the evaluation of histopathological slides (glass slides), or Whole Slide Images (WSI), of the patients' skin tissue. We introduce a deep learning aided diagnostics tool that brings a two-fold value to the decision process of pathologists. First, our algorithm accurately segments WSI into regions that are relevant for an accurate diagnosis, achieving a Mean-IoU of 69% and a Matthews Correlation score of 83% on a novel dataset. Additionally, we also show that our model is competitive with the state of the art on a reference dataset. Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema. We created two models that can be applied in different stages of the diagnostic pipeline, potentially eliminating life-threatening mistakes. The classification outcome is considerably more interpretable than using only the WSI as the input, since it is also based on the segmentation map. Our segmentation model, which we call EU-Net, extends a classical U-Net with an EfficientNet-B7 encoder which was pre-trained on the Imagenet dataset.
RLFC

We present a new hierarchical compression scheme for encoding light field images (LFI) that is suitable for interactive rendering. Our method (RLFC) exploits redundancies in the light field images by constructing a tree structure. The top level (root) of the tree captures the common high-level details across the LFI, and other levels (children) of the tree capture specific low-level details of the LFI. Our decompressing algorithm corresponds to tree traversal operations and gathers the values stored at different levels of the tree. Furthermore, we use bounded integer sequence encoding which provides random access and fast hardware decoding for compressing the blocks of children of the tree. We have evaluated our method for 4D two-plane parameterized light fields. The compression rates vary from 0.08 -- 2.5 bits per pixel (bpp), resulting in compression ratios of around 200:1 to 20:1 for a PSNR quality of 40 to 50 dB. The decompression times for decoding the blocks of LFI are 1 -- 3 microseconds per channel on an NVIDIA GTX-960 and we can render new views with a resolution of 512 X 512 at 200 fps. Our overall scheme is simple to implement and involves only bit manipulations and integer arithmetic operations.1
Semi-supervised Federated Learning for Activity Recognition

Training deep learning models on in-home IoT sensory data is commonly used to recognise human activities. Recently, federated learning systems that use edge devices as clients to support local human activity recognition have emerged as a new paradigm to combine local (individual-level) and global (group-level) models. This approach provides better scalability and generalisability and also offers better privacy compared with the traditional centralised analysis and learning models. The assumption behind federated learning, however, relies on supervised learning on clients. This requires a large volume of labelled data, which is difficult to collect in uncontrolled IoT environments such as remote in-home monitoring. In this paper, we propose an activity recognition system that uses semi-supervised federated learning, wherein clients conduct unsupervised learning on autoencoders with unlabelled local data to learn general representations, and a cloud server conducts supervised learning on an activity classifier with labelled data. Our experimental results show that using a long short-term memory autoencoder and a Softmax classifier, the accuracy of our proposed system is higher than that of both centralised systems and semi-supervised federated learning using data augmentation. The accuracy is also comparable to that of supervised federated learning systems. Meanwhile, we demonstrate that our system can reduce the number of needed labels and the size of local models, and has faster local activity recognition speed than supervised federated learning does.
Blockchain-based System Evaluation: The Effectiveness of Blockchain on E-Procurements

Electronic systems tend to simplify the tedious traditional scheme and basically focuses on the platform design and process organization.The integrity of the output of an automated system is not left behind but the possibility of internal manipulation is still high.This paper presents the current issues in company procurements and the solution in the form of blockchain technology.Several individuals and professionals were asked to evaluate a blockchain-based procurement system in comparison to the current electronic (e-procurement) system.A blockchain-based system has the capability to hold transactional data with complete decentralization and eliminate the growing number of fraud cases in companies and organizations.This paper mainly focuses on the effectiveness of a blockchain-based system in company procurements.
On generalization in moment-based domain adaptation

Domain adaptation algorithms are designed to minimize the misclassification risk of a discriminative model for a target domain with little training data by adapting a model from a source domain with a large amount of training data. Standard approaches measure the adaptation discrepancy based on distance measures between the empirical probability distributions in the source and target domain. In this setting, we address the problem of deriving generalization bounds under practice-oriented general conditions on the underlying probability distributions. As a result, we obtain generalization bounds for domain adaptation based on finitely many moments and smoothness conditions.
Meta-Learning with Adaptive Hyperparameters

Despite its popularity, several recent works question the effectiveness of MAML when test tasks are different from training tasks, thus suggesting various task-conditioned methodology to improve the initialization. Instead of searching for better task-aware initialization, we focus on a complementary factor in MAML framework, inner-loop optimization (or fast adaptation). Consequently, we propose a new weight update rule that greatly enhances the fast adaptation process. Specifically, we introduce a small meta-network that can adaptively generate per-step hyperparameters: learning rate and weight decay coefficients. The experimental results validate that the Adaptive Learning of hyperparameters for Fast Adaptation (ALFA) is the equally important ingredient that was often neglected in the recent few-shot learning approaches. Surprisingly, fast adaptation from random initialization with ALFA can already outperform MAML.
Combining Neural and Knowledge-Based Approaches to Named Entity Recognition in Polish

Named entity recognition (NER) is one of the tasks in natural language processing that can greatly benefit from the use of external knowledge sources. We propose a named entity recognition framework composed of knowledge-based feature extractors and a deep learning model including contextual word embeddings, long short-term memory (LSTM) layers and conditional random fields (CRF) inference layer. We use an entity linking module to integrate our system with Wikipedia. The combination of effective neural architecture and external resources allows us to obtain state-of-the-art results on recognition of Polish proper names. We evaluate our model on data from PolEval 2018 NER challenge on which it outperforms other methods, reducing the error rate by 22.4% compared to the winning solution. Our work shows that combining neural NER model and entity linking model with a knowledge base is more effective in recognizing named entities than using NER model alone.
Automatic Digital Modulation Recognition Based on Machine Learning Algorithms

Modulation type recognition has attracted increasing attention in recent years, both in the military and commercial sectors. This paper introduces new methods for the automatic identification of digital modulations. Our work targets the main type of digital modulations, including amplitude-shift keying, quadrature amplitude-shift keying, frequency-shift keying, quadrature frequency-shift keying, phase-shift keying, quadrature phase-shift keying and 16 quadrature amplitude modulation. These modulations are identified and separated by first selecting the appropriate features from the received modulated signal, and then classifying the modulation type with the help of k-nearest neighbors and probabilistic neural network algorithms. To validate our proposed methods, we perform MATLAB simulations with signal-to-noise ratio from -10 dB to 30 dB over the additive white Gaussian noise channel. The simulation results indicate that using the proposed algorithms, selecting useful features, and properly setting the tuning parameters lead to a significant improvement in accuracy and speed of modulation type recognition. Compared to existing works, the proposed algorithms achieve the highest accuracy with the least number of classifiers and adjustable parameters.
Detecting Attackable Sentences in Arguments

Finding attackable sentences in an argument is the first step toward successful refutation in argumentation.We present a first large-scale analysis of sentence attackability in online arguments.We analyze driving reasons for attacks in argumentation and identify relevant characteristics of sentences.We demonstrate that a sentence's attackability is associated with many of these characteristics regarding the sentence's content, proposition types, and tone, and that an external knowledge source can provide useful information about attackability.Building on these findings, we demonstrate that machine learning models can automatically detect attackable sentences in arguments, significantly better than several baselines and comparably well to laypeople. 1
Path Planning Algorithm for an Autonomous Electric Wheelchair in Hospitals

A novel path planning algorithm for an autonomous electric wheelchair that can be used in hospitals is proposed and verified. The objective of this study is to develop an autonomous path planning algorithm that satisfies body acceleration constraints for patient safety and follows paths suitable for hospitals. The wheelchair dynamics and equations of motion, including caster dynamics and the user's influence on the wheelchair, are newly derived in this research. Since the proposed algorithm is based on two-wheel robot kinematics, it is applicable to most two-wheel actuated systems. When a wheelchair's destination is set, a series of waypoints is computed and then the desired speed & attitude at each waypoint of the path are designed. Then, a speed profile is designed to minimize the travel time while adaptively changing the angular rate gain to minimize excessive lateral motion. As a result, the linear speed command of each actuated wheel is computed. Additionally, the hybrid reciprocal velocity obstacle algorithm is updated to guarantee that the body acceleration constraints are satisfied for applications with complicated dynamics. The performance of the proposed algorithm, with the full dynamics of the wheelchair, is demonstrated by extensive numerical simulations with conventional toy problems and then evaluated in two hospitals. The simulation results demonstrate that the proposed algorithm satisfies body acceleration constraints while following paths which are suitable for hospitals and that the proposed autonomous electric wheelchair could be applied in hospitals.
Multi-stage Deep Classifier Cascades for Open World Recognition

At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem are far more challenging because: i)~new classes unseen in the training phase can appear when predicting; ii)~discriminative features need to evolve when new classes emerge in real time; and iii)~instances in new classes may not follow the "independent and identically distributed" (iid) assumption. Most existing work only aims to detect the unknown classes and is incapable of continuing to learn newer classes. Although a few methods consider both detecting and including new classes, all are based on the predefined handcrafted features that cannot evolve and are out-of-date for characterizing emerging classes. Thus, to address the above challenges, we propose a novel generic end-to-end framework consisting of a dynamic cascade of classifiers that incrementally learn their dynamic and inherent features. The proposed method injects dynamic elements into the system by detecting instances from unknown classes, while at the same time incrementally updating the model to include the new classes. The resulting cascade tree grows by adding a new leaf node classifier once a new class is detected, and the discriminative features are updated via an end-to-end learning strategy. Experiments on two real-world datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.
Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction

Many practical graph problems, such as knowledge graph construction and drug-drug interaction prediction, require to handle multi-relational graphs. However, handling real-world multi-relational graphs with Graph Neural Networks (GNNs) is often challenging due to their evolving nature, as new entities (nodes) can emerge over time. Moreover, newly emerged entities often have few links, which makes the learning even more difficult. Motivated by this challenge, we introduce a realistic problem of few-shot out-of-graph link prediction, where we not only predict the links between the seen and unseen nodes as in a conventional out-of-knowledge link prediction task but also between the unseen nodes, with only few edges per node. We tackle this problem with a novel transductive meta-learning framework which we refer to as Graph Extrapolation Networks (GEN). GEN meta-learns both the node embedding network for inductive inference (seen-to-unseen) and the link prediction network for transductive inference (unseen-to-unseen). For transductive link prediction, we further propose a stochastic embedding layer to model uncertainty in the link prediction between unseen entities. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction. The results show that our model significantly outperforms relevant baselines for out-of-graph link prediction tasks.
A Matlab Toolbox for Feature Importance Ranking

More attention is being paid for feature importance ranking (FIR), in particular when thousands of features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR approaches have been proposed, while few are integrated for comparison and real-life applications. In this study, a matlab toolbox is presented and a total of 30 algorithms are collected. Moreover, the toolbox is evaluated on a database of 163 ultrasound images. To each breast mass lesion, 15 features are extracted. To figure out the optimal subset of features for classification, all combinations of features are tested and linear support vector machine is used for the malignancy prediction of lesions annotated in ultrasound images. At last, the effectiveness of FIR is analyzed according to performance comparison. The toolbox is online (https://github.com/NicoYuCN/matFIR). In our future work, more FIR methods, feature selection methods and machine learning classifiers will be integrated.
An Empirical Analysis of Human-Bot Interaction on Reddit

Automated agents ("bots") have emerged as an ubiquitous and influential presence on social media.Bots engage on social media platforms by posting content and replying to other users on the platform.In this work we conduct an empirical analysis of the activity of a single bot on Reddit.Our goal is to determine whether bot activity (in the form of posted comments on the website) has an effect on how humans engage on Reddit.We find that (1) the sentiment of a bot comment has a significant, positive effect on the subsequent human reply, and (2) human Reddit users modify their comment behaviors to overlap with the text of the bot, similar to how humans modify their text to mimic other humans in conversation.Understanding human-bot interactions on social media with relatively simple bots is important for preparing for more advanced bots in the future.
Developing Vocabulary Knowledge among Low Achievers: Mobile Augmented Reality (MAR) Practicality

Difficulty in learning gives huge impact to the process of language discovery among school children. LINUS students are categorized in this group and regarded as Low Achiever (LA) regardless of their education level. Currently, schools are provided with traditional learning materials such as textbook prepared by the Ministry of Education and it is up to the teachers’ own creativity and initiative to turn the materials into a more interesting learning source [1], [2]. However, depending solely on these learning materials alone is not enough as these children will easily lose their attention as they become bored and finally lost interest to proceed with the learning process [3]. Therefore, there is a need to utilize the elements of technology in order to motivate and facilitate these LINUS students in learning vocabulary in English. This experimental study aimed to explore the effectiveness of using mobile augmented reality (MAR) application in vocabulary learning among LINUS students. 45 students were involved and the development of MAR in vocabulary learning in this study was investigated based on the ADDIE Instructional Design (ID) method as a framework. As a form of measurement, British Picture Vocabulary Scale (BPVS) III was used and findings showed that there was a significant improvement in the post test after 6 months of MAR implementation. Although this study showed an encouraging results, there was still lack of skills in terms of writing the words learnt via augmented reality. The results of this study provide the discussion on the practicality of using Augmented Reality to help the struggling learners to cope with acquiring and learning English language in ESL context.
Deep Learning for Abstract Argumentation Semantics

In this paper, we present a learning-based approach to determining acceptance of arguments under several abstract argumentation semantics. More specifically, we propose an argumentation graph neural network (AGNN) that learns a message-passing algorithm to predict the likelihood of an argument being accepted. The experimental results demonstrate that the AGNN can almost perfectly predict the acceptability under different semantics and scales well for larger argumentation frameworks. Furthermore, analysing the behaviour of the message-passing algorithm shows that the AGNN learns to adhere to basic principles of argument semantics as identified in the literature, and can thus be trained to predict extensions under the different semantics – we show how the latter can be done for multi-extension semantics by using AGNNs to guide a basic search. We publish our code at https://github.com/DennisCraandijk/DL-Abstract-Argumentation.
A Modified Salp Swarm Algorithm Based on the Perturbation Weight for Global Optimization Problems

Metaheuristic algorithms are often applied to global function optimization problems. To overcome the poor real-time performance and low precision of the basic salp swarm algorithm, this paper introduces a novel hybrid algorithm inspired by the perturbation weight mechanism. The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. A new coefficient factor is introduced to the basic salp swarm algorithm, and new update strategies for the leader position and the followers are introduced in the search phase. The new leader position updating strategy has a specific bounded scope and strong search performance, thus accelerating the iteration process. The new follower updating strategy maintains the diversity of feasible solutions while reducing the computational load. This paper describes the application of the proposed algorithm to low-dimension and variable-dimension functions. This paper also presents iteration curves, box-plot charts, and search-path graphics to verify the accuracy of the proposed algorithm. The experimental results demonstrate that the perturbation weight salp swarm algorithm offers a better search speed and search balance than the basic salp swarm algorithm in different environments.
Temporal Consistency Objectives Regularize the Learning of Disentangled Representations

There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whilst relying less on annotated data (since annotations can be tedious and costly). Here we build on recent innovations in style-content representations to learn anatomy, imaging characteristics (appearance) and temporal correlations. By introducing a self-supervised objective of predicting future cardiac phases we improve disentanglement. We propose a temporal transformer architecture that given an image conditioned on phase difference, it predicts a future frame. This forces the anatomical decomposition to be consistent with the temporal cardiac contraction in cine MRI and to have semantic meaning with less need for annotations. We demonstrate that using this regularization, we achieve competitive results and improve semi-supervised segmentation, especially when very few labelled data are available. Specifically, we show Dice increase of up to 19\% and 7\% compared to supervised and semi-supervised approaches respectively on the ACDC dataset. Code is available at: https://github.com/gvalvano/sdtnet .
CIST@CL-SciSumm 2020, LongSumm 2020: Automatic Scientific Document Summarization

Our system participates in two shared tasks, CL-SciSumm 2020 and LongSumm 2020.In the CL-SciSumm shared task, based on our previous work, we apply more machine learning methods on position features and content features for facet classification in Task1B.And GCN is introduced in Task2 to perform extractive summarization.In the LongSumm shared task, we integrate both the extractive and abstractive summarization ways.Three methods were tested which are T5 Fine-tuning, DPPs Sampling, and GRU-GCN/GAT.