Reviewerly/exHarmony huggingface dataset:
Ensembling handcrafted features with deep features: an analytical study for classification of routine colon cancer histopathological nuclei images

The use of Deep Learning (DL) based methods in medical histopathology images have been one of the most sought after solutions to classify, segment, and detect diseased biopsy samples. However, given the complex nature of medical datasets due to the presence of intra-class variability and heterogeneity, the use of complex DL models might not give the optimal performance up to the level which is suitable for assisting pathologists. Therefore, ensemble DL methods with the scope of including domain agnostic handcrafted Features (HC-F) inspired this work. We have, through experiments, tried to highlight that a single DL network (domain-specific or state of the art pre-trained models) cannot be directly used as the base model without proper analysis with the relevant dataset. We have used F1-measure, Precision, Recall, AUC, and Cross-Entropy Loss to analyse the performance of our approaches. We observed from the results that the DL features ensemble bring a marked improvement in the overall performance of the model, whereas, domain agnostic HC-F remains dormant on the performance of the DL models.
A multifactorial optimization paradigm for linkage tree genetic algorithm

Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm (EA) to solve complex problems using the linkage information between problem variables. LTGA performs well in various kinds of single-task optimization and yields promising results in comparison with the canonical genetic algorithm. However, LTGA is an unsuitable method for dealing with multi-task optimization problems. On the other hand, Multifactorial Optimization (MFO) can simultaneously solve independent optimization problems, which are encoded in a unified representation to take advantage of the process of knowledge transfer. In this paper, we introduce Genetic Algorithm (MF-LTGA) by combining the main features of both LTGA and MFO. MF-LTGA is able to tackle multiple optimization tasks at the same time, each task learns the dependency between problem variables from the shared representation. This knowledge serves to determine the high-quality partial solutions for supporting other tasks in exploring the search space. Moreover, MF-LTGA speeds up convergence because of knowledge transfer of relevant problems. We demonstrate the effectiveness of the proposed algorithm on two benchmark problems: Clustered Shortest-Path Tree Problem and Deceptive Trap Function. In comparison to LTGA and existing methods, MF-LTGA outperforms in quality of the solution or in computation time.
LifeGraph: A Knowledge Graph for Lifelogs

The data produced by efforts such as life logging is commonly multi modal and can have manifold interrelations with itself as well as external information. Representing this data in such a way that these rich relations as well as all the different sources can be leveraged is a non-trivial undertaking. In this paper, we present the first iteration of LifeGraph, a Knowledge Graph for lifelogging data. LifeGraph aims at not only capturing all aspects of the data contained in a lifelog but also linking them to external, static knowledge bases in order to put the log as a whole as well as its individual entries into a broader context. In the Lifelog Search Challenge 2020, we show a first proof-of-concept implementation of LifeGraph as well as a retrieval system prototype which utilizes it to search the log for specific events.
GloDyNE: Global Topology Preserving Dynamic Network Embedding

Learning low-dimensional topological representation of a network in dynamic environments is attracting much attention due to the time-evolving nature of many real-world networks. The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step. The idea of most existing DNE methods is to capture the topological changes at or around the most affected nodes (instead of all nodes) and accordingly update node embeddings. Unfortunately, this kind of approximation, although can improve efficiency, cannot effectively preserve the global topology of a dynamic network at each time step, due to not considering the inactive sub-networks that receive accumulated topological changes propagated via the high-order proximity. To tackle this challenge, we propose a novel node selecting strategy to diversely select the representative nodes over a network, which is coordinated with a new incremental learning paradigm of Skip-Gram based embedding approach. The extensive experiments show GloDyNE, with a small fraction of nodes being selected, can already achieve the superior or comparable performance w.r.t. the state-of-the-art DNE methods in three typical downstream tasks. Particularly, GloDyNE significantly outperforms other methods in the graph reconstruction task, which demonstrates its ability of global topology preservation. The source code is available at https://github.com/houchengbin/GloDyNE
Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules

The paper proposes a methodology to online self-evolve direct fuzzy logic controllers (FLCs), to deal with unknown and time-varying dynamics. The proposed methodology self-designs the controller, where fuzzy control rules can be added or removed considering a predefined criterion. The proposed methodology aims to reach a control structure easily interpretable by human operators. The FLC is defined by univariate fuzzy control rules, where each input variable is represented by a set of fuzzy control rules, improving the interpretability ability of the learned controller. The proposed self-evolving methodology, when the process is under control (online stage), adds fuzzy control rules on the current FLC using a criterion based on the incremental estimated control error obtained using the system’s inverse function and deletes fuzzy control rules using a criterion that defines “less active” and “less informative” control rules. From the results on a nonlinear continuously stirred tank reactor (CSTR) plant, the proposed methodology shows the capability to online self-design the FLC by adding and removing fuzzy control rules in order to successfully control the CSTR plant.
Probabilistic solar irradiance forecasting via a deep learning‐based hybrid approach

Probabilistic solar irradiance forecasting has received widespread attention in recent years, as it provides more uncertainty information for the future photovoltaic generation. In this study, a hybrid probabilistic solar irradiance prediction method is proposed, which combines a deep recurrent neural network and residual modeling. Specifically, the long short‐term memory‐based point prediction using historical records and related features is applied to obtain deterministic forecasts. Next, these deterministic forecasts are employed as inputs to estimate the residual distributions. Furthermore, maximum likelihood estimation is utilized to compute the parameters of the residual distribution. Finally, the point prediction and residual distribution jointly generate the final probabilistic forecasting results. Compared with other deterministic and probabilistic forecasting models, the proposed method yields promising results on a publicly available dataset. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
A Day-Ahead Irradiance Forecasting Strategy for the Integration of Photovoltaic Systems in Virtual Power Plants

Encouraged by the considerable cost reduction, small-scale solar power deployment has become a reality during the last decade. However, grid integration of small-scale photovoltaic (PV) solar systems still remains unresolved. High penetration of Renewable Energy Sources (RESs) results in technical challenges for grid operators. To address this, Virtual Power Plants (VPPs) have been defined and developed to manage distributed energy resources with the aim of facilitating the integration of RESs. This paper introduces a hybrid irradiance forecasting approach aimed at facilitating the integration of PV systems into a VPP, especially when a historical irradiance dataset is exiguous or non-existent. This approach is based on Artificial Neural Networks (ANNs) and a novel similar hour-based selection algorithm, has been tested for a real PV installation, and has been validated also considering irradiance measurements from an aggregation of ground-based meteorological stations, which emulate the nodes of a VPP. Under a reduced historical dataset, the results show that the proposed similar hour-based method produces the best forecasts with regard to those obtained by the ANN-based approach. This is particularly true for one-month and two-month datasets minimizing the mean error by 16.32% and 9.07% respectively. Finally, to demonstrate the potential of the proposed approach, a comparative analysis has been carried out between the hybrid method and the most used benchmarks in the literature, namely, the persistence method and the method based on similar days. It has been demonstrated conclusively that the proposed model yields promising results regardless the length of the historical dataset.
SSCR: Iterative Language-Based Image Editing via Self-Supervised Counterfactual Reasoning

Iterative Language-Based Image Editing (IL-BIE) tasks follow iterative instructions to edit images step by step.Data scarcity is a significant issue for ILBIE as it is challenging to collect large-scale examples of images before and after instruction-based changes.However, humans still accomplish these editing tasks even when presented with an unfamiliar image-instruction pair.Such ability results from counterfactual thinking and the ability to think about alternatives to events that have happened already.In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates counterfactual thinking to overcome data scarcity.SSCR allows the model to consider out-ofdistribution instructions paired with previous images.With the help of cross-task consistency (CTC), we train these counterfactual instructions in a self-supervised scenario.Extensive results show that SSCR improves the correctness of ILBIE in terms of both object identity and position, establishing a new state of the art (SOTA) on two IBLIE datasets (i-CLEVR and CoDraw).Even with only 50% of the training data, SSCR achieves a comparable result to using complete data.
High fidelity dissipation engineering using parametric interactions

Established methods for dissipative state preparation typically rely on resolving resonances, limiting the target state fidelity due to competition between the stabilization mechanism and uncontrolled dissipation. We propose a protocol devoid of such constraints, using parametric couplings to engineer dissipation for preparation of any maximally entangled two-qubit state. Our scheme allows high-fidelity entanglement generation with short convergence time, continuous control of the target state in the stabilized manifold, and is realizable with state-of-the-art superconducting qubit technology.
ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization

Cherokee is a highly endangered Native American language spoken by the Cherokee people.The Cherokee culture is deeply embedded in its language.However, there are approximately only 2,000 fluent first language Cherokee speakers remaining in the world, and the number is declining every year.To help save this endangered language, we introduce ChrEn, a Cherokee-English parallel dataset, to facilitate machine translation research between Cherokee and English.Compared to some popular machine translation language pairs, ChrEn is extremely low-resource, only containing 14k sentence pairs in total.We split our parallel data in ways that facilitate both in-domain and out-of-domain evaluation.We also collect 5k Cherokee monolingual data to enable semi-supervised learning.Besides these datasets, we propose several Cherokee-English and English-Cherokee machine translation systems.We compare SMT (phrase-based) versus NMT (RNN-based and Transformer-based) systems; supervised versus semi-supervised (via language model, back-translation, and BERT/Multilingual-BERT) methods; as well as transfer learning versus multilingual joint training with 4 other languages.Our best results are 15.8/12.7 BLEU for in-domain and 6.5/5.0BLEU for out-of-domain Chr-En/En-Chr translations, respectively, and we hope that our dataset and systems will encourage future work by the community for Cherokee language revitalization. 1
Achievable Rates and Signal Detection for Photon-Level Photomultiplier Receiver Based on Statistical Non-Linear Model

We characterize the practical receiver in a wide range of signal intensity for optical wireless communication, from discrete pulse regime to continuous waveform regime. We first propose a statistical non-linear model based on the photomultiplier tube (PMT) multi-stage amplification and Poisson channel, and then derive the optimal and tractable suboptimal duty cycle with peak-power and average-power constraints for on-off key (OOK) modulation in linear regime. Subsequently, a threshold-based classifier is proposed to distinguish the PMT working regimes based on the non-linear model. Moreover, we derive the approximate performance of mean power detection with infinite sampling rate and finite over-sampling rate in the linear regime based on small dead time and central-limit theorem. We also fomulate a signal model in the non-linar regime. Furthermore, the performance of mean power detection and photon counting detection with maximum likelihood (ML) detection for different sampling rates is evaluated from both theoretical and numerical perspectives. We can conclude that the sample interval equivalent to dead time is a good choice, and lower sampling rate would significantly degrade the performance.
Human Pose Estimation on Privacy-Preserving Low-Resolution Depth Images

Human pose estimation (HPE) is a key building block for developing AI-based context-aware systems inside the operating room (OR). The 24/7 use of images coming from cameras mounted on the OR ceiling can however raise concerns for privacy, even in the case of depth images captured by RGB-D sensors. Being able to solely use low-resolution privacy-preserving images would address these concerns and help scale up the computer-assisted approaches that rely on such data to a larger number of ORs. In this paper, we introduce the problem of HPE on low-resolution depth images and propose an end-to-end solution that integrates a multi-scale super-resolution network with a 2D human pose estimation network. By exploiting intermediate feature-maps generated at different super-resolution, our approach achieves body pose results on low-resolution images (of size 64x48) that are on par with those of an approach trained and tested on full resolution images (of size 640x480).
Physical Deep Learning Based on Optimal Control of Dynamical Systems

Deep learning is the backbone of artificial intelligence technologies, and it can be regarded as a kind of multilayer feedforward neural network. An essence of deep learning is information propagation through layers. This suggests that there is a connection between deep neural networks and dynamical systems in the sense that information propagation is explicitly modeled by the time-evolution of dynamical systems. In this study, we perform pattern recognition based on the optimal control of continuous-time dynamical systems, which is suitable for physical hardware implementation. The learning is based on the adjoint method to optimally control dynamical systems, and the deep (virtual) network structures based on the time evolution of the systems are used for processing input information. As a key example, we apply the dynamics-based recognition approach to an optoelectronic delay system and demonstrate that the use of the delay system allows for image recognition and nonlinear classifications using only a few control signals. This is in contrast to conventional multilayer neural networks, which require a large number of weight parameters to be trained. The proposed approach provides insight into the mechanisms of deep network processing in the framework of an optimal control problem and presents a pathway for realizing physical computing hardware.
Exploring Multi-Task Multi-Lingual Learning of Transformer Models for Hate Speech and Offensive Speech Identification in Social Media

Hate Speech has become a major content moderation issue for online social media platforms. Given the volume and velocity of online content production, it is impossible to manually moderate hate speech related content on any platform. In this paper we utilize a multi-task and multi-lingual approach based on recently proposed Transformer Neural Networks to solve three sub-tasks for hate speech. These sub-tasks were part of the 2019 shared task on hate speech and offensive content (HASOC) identification in Indo-European languages. We expand on our submission to that competition by utilizing multi-task models which are trained using three approaches, a) multi-task learning with separate task heads, b) back-translation, and c) multi-lingual training. Finally, we investigate the performance of various models and identify instances where the Transformer based models perform differently and better. We show that it is possible to to utilize different combined approaches to obtain models that can generalize easily on different languages and tasks, while trading off slight accuracy (in some cases) for a much reduced inference time compute cost. We open source an updated version of our HASOC 2019 code with the new improvements at https://github.com/socialmediaie/MTML_HateSpeech.
Application of metaheuristic algorithms in interval type-2 fractional order fuzzy TID controller for nonlinear level control process under actuator and system component faults*

Purpose The two-tank level control system is one of the real-world's second-order system (SOS) widely used as the process control in industries. It is normally operated under the Proportional integral and derivative (PID) feedback control loop. The conventional PID controller performance degrades significantly in the existence of modeling uncertainty, faults and process disturbances. To overcome these limitations, the paper suggests an interval type-2 fuzzy logic based Tilt-Integral-Derivative Controller (IT2TID) which is modified structure of PID controller. Design/methodology/approach In this paper, an optimization IT2TID controller design for the conical, noninteracting level control system is presented. Regarding to modern optimization context, the flower pollination algorithm (FPA), among the most coherent population-based metaheuristic optimization techniques is applied to search for the appropriate IT2FTID's and IT2FPID's parameters. The proposed FPA-based IT2FTID/IT2FPID design framework is considered as the constrained optimization problem. System responses obtained by the IT2FTID controller designed by the FPA will be differentiated with those acquired by the IT2FPID controller also designed by the FPA. Findings As the results, it was found that the IT2FTID can provide the very satisfactory tracking and regulating responses of the conical two-tank noninteracting level control system superior as compared to IT2FPID significantly under the actuator and system component faults. Additionally, statistical Z -test carried out for both the controllers and an effectiveness of the proposed IT2FTID controller is proven as compared to IT2FPID and existing passive fault tolerant controller in recent literature. Originality/value Application of new metaheuristic algorithm to optimize interval type-2 fractional order TID controller for nonlinear level control system with two type of faults. Also, proposed method will compare with other method and statistical analysis will be presented.
RETRACTED: Design and implementation of multimedia teaching platform for situational teaching of music appreciation course based on virtual reality

The main goal of music appreciation teaching is to cultivate students' correct aesthetics and teach them to acquire basic knowledge about music history and musical expression methods. However, traditional music teaching is often limited in conditions and abstract in content, making it difficult to achieve this teaching goal. For the purpose of achieving the teaching goal and effect, this paper mainly studies how to use the new teaching aids, namely virtual reality technology, to design and implement the multimedia teaching platform for the music appreciation course scenario teaching. Through the statistics of the students' music appreciation course and the feedback data collected by the questionnaire, the results show that the platform in this paper is helpful to arouse students' interest in music learning and promote their efficient and independent learning. The innovation of this article lies in the full application of virtual reality technology to music teaching appreciation classes, which enables the effective combination of virtual reality technology and music courses to play the role of virtual reality technology.
On mitigating popularity bias in recommendations via variational autoencoders

Recommender systems are usually susceptible to Popularity Bias, in the sense that their training procedures have major influence of popular items. This promotes a long term unfairness: models end up adjusted to common options, associated to few popular items, and with restricted knowledge about unpopular ones. We propose a method that penalizes scores given to items according to historical popularity for mitigating the bias and promoting diversity in the results. A parameter is available for controlling the weight of the penalty, to be decided according to the necessity in the application. Our method is based on Variational Autoencoders, considered today as the state-of-the-art method for the task of Collaborative Filtering, and the price for removing the bias is paid by having its accuracy and ranking metrics reduced. We managed to reduce the bias by 8% while reducing accuracy in 3% in a movie consumption dataset.
Generative learning strategies do not diminish primary students’ attitudes towards augmented reality

Abstract Do generative learning strategies influence the motivational and affective factors of learning with augmented reality? This article explores this highly topical new question in the field of research on educational technology and reports the results of a value-added study that compares two mobile AR learning environments with or without additional learning strategies. A total of 56 primary school students participated and learned with augmented reality (AR) learning materials either in an experimental group or a control group. The experimental group learned with AR and additional learning strategies based on generative learning theory, namely, self-explanation and self-testing. The control group learned only with AR. It was investigated whether the addition of learning strategies would lead to a reduction in positive attitudes towards AR as a learning technology. The experimental group of students showed overall positive attitudes towards AR as a learning technology at approximately the same level as the control group. However, significant differences were found for the skepticism subscale, indicating that learners who learned with both AR and learning strategies were more skeptical about AR as a learning technology than those who learned only with AR. Gender differences were also found for the accessibility subscale, with male participants considering the AR technology to be easier to access outside of the classroom too. The impact of these results on theory and practice as well as further research directions is discussed.