Reviewerly/exHarmony huggingface dataset:
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks

Image classification is a difficult machine learning task, where Convolutional Neural Networks (CNNs) have been applied for over 20 years in order to solve the problem. In recent years, instead of the traditional way of only connecting the current layer with its next layer, shortcut connections have been proposed to connect the current layer with its forward layers apart from its next layer, which has been proved to be able to facilitate the training process of deep CNNs. However, there are various ways to build the shortcut connections, it is hard to manually design the best shortcut connections when solving a particular problem, especially given the design of the network architecture is already very challenging. In this paper, a hybrid evolutionary computation (EC) method is proposed to automatically evolve both the architecture of deep CNNs and the shortcut connections. Three major contributions of this work are: Firstly, a new encoding strategy is proposed to encode a CNN, where the architecture and the shortcut connections are encoded separately; Secondly, a hybrid two-level EC method, which combines particle swarm optimisation and genetic algorithms, is developed to search for the optimal CNNs; Lastly, an adjustable learning rate is introduced for the fitness evaluations, which provides a better learning rate for the training process given a fixed number of epochs. The proposed algorithm is evaluated on three widely used benchmark datasets of image classification and compared with 12 peer Non-EC based competitors and one EC based competitor. The experimental results demonstrate that the proposed method outperforms all of the peer competitors in terms of classification accuracy.
Better Exploration with Optimistic Actor Critic

Actor-critic methods, a type of model-free Reinforcement Learning, have been successfully applied to challenging tasks in continuous control, often achieving state-of-the art performance. However, wide-scale adoption of these methods in real-world domains is made difficult by their poor sample efficiency. We address this problem both theoretically and empirically. On the theoretical side, we identify two phenomena preventing efficient exploration in existing state-of-the-art algorithms such as Soft Actor Critic. First, combining a greedy actor update with a pessimistic estimate of the critic leads to the avoidance of actions that the agent does not know about, a phenomenon we call pessimistic underexploration. Second, current algorithms are directionally uninformed, sampling actions with equal probability in opposite directions from the current mean. This is wasteful, since we typically need actions taken along certain directions much more than others. To address both of these phenomena, we introduce a new algorithm, Optimistic Actor Critic, which approximates a lower and upper confidence bound on the state-action value function. This allows us to apply the principle of optimism in the face of uncertainty to perform directed exploration using the upper bound while still using the lower bound to avoid overestimation. We evaluate OAC in several challenging continuous control tasks, achieving state-of the art sample efficiency.
A literature survey on kinship verification through facial images

Kinship verification is an emerging task in computer vision which aims at finding out whether there is a kin relation between given identities through their facial images. Applications of kinship verification include image annotation, children adoption, and social media analysis, etc. However, kinship verification through facial images is challenging because facial images usually contain high intra-variances, which vary from genetic, age and gender difference. Over the past decade, more and more effective methods have emerged. This paper aims at categorizing and evaluating these methods systematically. We attach great importance to the difficulties in practical applications of kinship verification, and review the prominent algorithms from the perspective of learning more efficient models with more diverse kin relations. Then we further show how to develop an efficient and robust kinship verification system. Finally we present several potential directions for future research.
AI Coach

Accurate pose analysis in sport videos is beneficial to users to improve skills. In this paper, we propose an AI coach system to provide personalized athletic training experiences for posture-wise sports activities, in which the training quality largely depends on the correctness of human poses in a video sequence. we propose to design the system with several distinct features: (1) trajectory extraction for a single human instance by leveraging deep visual tracking, (2) human pose estimation by proposing a novel human joints relation model in spatial and temporal domains,(3) pose correction by abnormal detection, performance rating and exemplar-based visual suggestions. We build an online service of this AI coach system for sports enthusiasts and collect extensive feedbacks. Comparisons with some latest popular sport apps demonstrate the effectiveness of this AI coach system to improve skills for users.
The Effects of Viewing an Uplifting 360-Degree Video on Emotional Well-Being Among Elderly Adults and College Students Under Immersive Virtual Reality and Smartphone Conditions

Positive emotions are important for the physical and mental health of elderly adults. Previous research has suggested that the emotion processing mechanisms of elderly adults differ from those of young people. The current study used a mixed-methods approach to explore the emotional impacts of viewing an uplifting 360° videos under immersive (using a virtual reality [VR] headset) and nonimmersive (using a smartphone) conditions on elderly populations and young people. In Study 1, we conducted a pre–post treatment between-subject field experiment (smartphone vs. VR) among 58 seniors (aged 60 years and above) in a community center in Beijing, China. One-on-one semistructured interviews of each participant were conducted after the experiment. In Study 2, we conducted a between-subject laboratory experiment with the same design among college students. The results show that while both VR and smartphone viewing conditions led to a significant positive effect on the self-reported emotions of the college students, only the smartphone viewing condition had a significant impact on the emotional well-being of the elderly participants. For college students, immersive VR had a stronger effect on inducing positive emotions than smartphone, but for elderly people, the smartphone viewing condition was more effective in improving emotional wellbeing than immersive VR. An analysis of the qualitative data from interviewing the elderly participants also revealed a divergence in the acceptance of VR technologies among elderly adults, and the sense of telepresence had both positive and negative effects on the elderly participants' emotions and experiences.
An improved edge detection algorithm for X-Ray images based on the statistical range

Edge detection is the prior stage to object recognition and considered as a pillar for image processing task. It is a process to detect such locations from images in terms of pixels where their intensity changing is abruptly. There are many types of images such as medical images, satellite images, articular images, industrial images, general purpose images etc. X-Ray is a type of medical image in which electronic radiation is passed into the human body to capture image of inner parts for better disease diagnoses by orthopaedics or radiologist. In this research paper, we have proposed an improved method to detect edges from human being's X-Ray images based on Gaussian filter and statistical range. Gaussian filter is used for image preprocessing and enhancement. Whereas, Statistical range is used to calculate difference between maximum and minimum pixels from every 3X3 image matrix partition. These two can work to detect edges from X-Ray images. We have also presented a comprehensive comparison of our proposed method with four existing latest methods/algorithms of edge detection. Apart from X-Ray images, experiments have also been conducted on human X-Ray images to detect edges. Further, we have found that our proposed method is superior in terms of MSE, RMSE, PSNR and computation time to detect edges from X-Ray images of human being.
Blockchain-Based Distributed Energy Trading in Energy Internet: An SDN Approach

The new network paradigm of Software Defined Networking (SDN) has been widely adopted. Due to its inherent advantages, SDN has been widely used in various network fields such as data centers, WAN, enterprise, Optical Networks and energy Internet. Among them, SDN-based energy Internet systems are receiving more and more attention. But at the same time, some problems and challenges are gradually becoming more prominent. The SDN-based energy Internet is a distributed architecture for renewable energy, so the traditional centralized electric energy trading model will no longer apply. The blockchain has been rapidly developed and applied in various domains by virtue of its decentralization, coordinated autonomy, and non-tamperability. We propose an SDN-based energy Internet distributed energy trading scheme supported by blockchain technology. The proposed scheme achieves a reasonable match of the transaction objects under the premise of protecting privacy. Finally, we conducted a comprehensive, systematic security and applicability analysis of the proposed solution, further confirming that the system meets our design goals.
The regression Tsetlin machine: a novel approach to interpretable nonlinear regression

Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
Solar Energy Technology for Northern Cyprus: Assessment, Statistical Analysis, and Feasibility Study

Solar power is the fastest-growing energy source in the world. New technologies can help to generate more power from solar energy. The present paper aims to encourage people and the government to develop solar energy-based power projects to achieve sustainable energy infrastructures, especially in developing countries. In addition, this paper presents a solar energy road map to attract investors to invest in clean energy technology to help reduce the effect of global warming and enhance sustainable technological development. Therefore, the first objective of the paper is to analyze and compare the monthly global solar radiation for five different locations in Northern Cyprus using the measured data collected from the Meteorological Department and estimated values collected from the satellite imagery database. In addition, the mean hourly meteorological parameters including global solar radiation, air temperature, sunshine, and relative humidity are analyzed statistically and the type of distribution functions are selected based on skewness and kurtosis values. Accordingly, estimating global solar radiation improves solar power generation planning and reduces the cost of measuring. Therefore, models of a surface were analyzed by means of polynomial adjustments considering the values of R-squared. Finally, this study provides a comprehensive and integrated feasibility analysis of a 100 MW grid-connected solar plant project as an economic project in the selected region to reduce electricity tariffs and greenhouse gas (GHG) emissions. RETScreen Expert software was used to conduct the feasibility analysis in terms of energy production, GHG emissions, and financial parameters for the best location for the installation of a 100 MW grid-connected photovoltaic (PV) plant. Finally, the results concluded that the proposed solar system could be used for power generation in Northern Cyprus.
Comparative visual analytics for assessing medical records with sequence embedding

Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.
Progressive Unsupervised Person Re-Identification by Tracklet Association With Spatio-Temporal Regularization

Existing methods for person re-identification (Re-ID) are mostly based on supervised learning which requires numerous manually labeled samples across all camera views for training. Such a paradigm suffers the scalability issue since in real-world Re-ID application, it is difficult to exhaustively label abundant identities over multiple disjoint camera views. To this end, we propose a progressive deep learning method for unsupervised person Re-ID in the wild by Tracklet Association with Spatio-Temporal Regularization (TASTR). In our approach, we first collect tracklet data within each camera by automatic person detection and tracking. Then, an initial Re-ID model is trained based on within-camera triplet construction for person representation learning. After that, based on the person visual feature and spatio-temporal constraint, we associate cross-camera tracklets to generate cross-camera triplets and update the Re-ID model. Lastly, with the refined Re-ID model, better visual feature of person can be extracted, which further promote the association of cross-camera tracklets. The last two steps are iterated multiple times to progressively upgrade the Re-ID model. To facilitate the study, we have collected a new 4K UHD video dataset named Campus4K with full frames and full spatio-temporal information. Experimental results show that with the spatio-temporal constraint in the training phase, the proposed approach outperforms the state-of-the-art unsupervised methods by notable margins on DukeMTMC-reID, and achieves competitive performance to fully supervised methods on both DukeMTMC-reID and Campus4K datasets.
Expert‐level automated malaria diagnosis on routine blood films with deep neural networks

Abstract Over 200 million malaria cases globally lead to half a million deaths annually. Accurate malaria diagnosis remains a challenge. Automated imaging processing approaches to analyze Thick Blood Films (TBF) could provide scalable solutions, for urban healthcare providers in the holoendemic malaria sub‐Saharan region. Although several approaches have been attempted to identify malaria parasites in TBF, none have achieved negative and positive predictive performance suitable for clinical use in the west sub‐Saharan region. While malaria parasite object detection remains an intermediary step in achieving automatic patient diagnosis, training state‐of‐the‐art deep‐learning object detectors requires the human‐expert labor‐intensive process of labeling a large dataset of digitized TBF. To overcome these challenges and to achieve a clinically usable system, we show a novel approach. It leverages routine clinical‐microscopy labels from our quality‐controlled malaria clinics, to train a Deep Malaria Convolutional Neural Network classifier (DeepMCNN) for automated malaria diagnosis. Our system also provides total Malaria Parasite (MP) and White Blood Cell (WBC) counts allowing parasitemia estimation in MP/μL, as recommended by the WHO. Prospective validation of the DeepMCNN achieves sensitivity/specificity of 0.92/0.90 against expert‐level malaria diagnosis. Our approach PPV/NPV performance is of 0.92/0.90, which is clinically usable in our holoendemic settings in the densely populated metropolis of Ibadan. It is located within the most populous African country (Nigeria) and with one of the largest burdens of Plasmodium falciparum malaria. Our openly available method is of importance for strategies aimed to scale malaria diagnosis in urban regions where daily assessment of thousands of specimens is required.
A novel case adaptation method based on differential evolution algorithm for disaster emergency

When disasters happen, time is often very urgent. Case-based reasoning (CBR) is one of the most effective approaches to support disaster emergency management. CBR takes good use of historical case data, which is one of the typical data-driven decision-making methods. Among the steps of CBR, adaptation is the core. To improve the adaptation, a hybrid mutation operator is implemented, and a new differential evolution (DE) algorithm is developed. An adaptation method based on the proposed algorithm is put forward to achieve case adaptation in the CBR system. The comparison results have shown that the proposed algorithm is superior compared with the state-of-art algorithms. Then, experiments of CBR have revealed that the adaptation method can effectively generate appropriate solutions with the help of the proposed algorithm.
Agro-financing and food production in Nigeria

This study examines how agro-financing impacts on food production in Nigeria supporting Goal 2 of the 2030 Sustainable Development Goals (SDGs) which aims to “end hunger, achieve food security, improve nutrition, and promote sustainable agriculture”. The study covers the period 1981–2018 using annual data sourced from the World Development Indicators (WDI) of the World Bank, Central Bank of Nigeria (CBN) Statistical Bulletin. The Johansen and the Canonical Cointegration approaches are employed and findings reveal that agro-financing is statistically significant in explaining the level of food production in Nigeria.The result implies that a 1% increase in farmers' access to agricultural finance is associated with an increase in food production by 0.002%–0.006% depending on the model specification. This result aligns with the ‘a priori’ expectations as it is expected that more agro-funding at low-interest rates motivates farmers to secure high-yield seedlings, machinery and other farm implements, organic inputs that positively impact on total agricultural yield, leading to more food production. Therefore, the study recommends that more funding be allocated to the agrarian sector with less stringent credit conditions, and more arable land be allotted for farming purposes amongst others.
How Context Affects Language Models' Factual Predictions

When pre-trained on large unsupervised textual corpora, language models are able tostore and retrieve factual knowledge to some extent, making it possible to use them directly for zero-shot cloze-style question answering. However, storing factual knowledge in afixed number of weights of a language model clearly has limitations. Previous approacheshave successfully provided access to information outside the model weights using supervised architectures that combine an information retrieval system with a machine readingcomponent. In this paper, we go a step further and integrate information from a retrievalsystem with a pre-trained language model in a purely unsupervised way. We report thataugmenting pre-trained language models in this way dramatically improves performanceand that the resulting system, despite being unsupervised, is competitive with a supervised machine reading baseline. Furthermore, processing query and context with differentsegment tokens allows BERT to utilize its Next Sentence Prediction pre-trained classifierto determine whether the context is relevant or not, substantially improving BERT’s zeroshot cloze-style question-answering performance and making its predictions robust to noisycontexts.
XtremeDistil: Multi-stage Distillation for Massive Multilingual Models

Deep and large pre-trained language models are the state-of-the-art for various natural language processing tasks.However, the huge size of these models could be a deterrent to using them in practice.Some recent works use knowledge distillation to compress these huge models into shallow ones.In this work we study knowledge distillation with a focus on multilingual Named Entity Recognition (NER).In particular, we study several distillation strategies and propose a stage-wise optimization scheme leveraging teacher internal representations, that is agnostic of teacher architecture, and show that it outperforms strategies employed in prior works.Additionally, we investigate the role of several factors like the amount of unlabeled data, annotation resources, model architecture and inference latency to name a few.We show that our approach leads to massive compression of teacher models like mBERT by upto 35x in terms of parameters and 51x in terms of latency for batch inference while retaining 95% of its F 1 -score for NER over 41 languages.
End-Users’ Augmented Reality Utilization for Architectural Design Review

To effectively use augmented reality (AR) technology for end-user involved design collaboration, it is necessary to ensure the effectiveness of the AR system from the end-user’s perspective. However, most efforts have mainly focused on technological development, and as such, limited attention has been paid to the end-user’s application of the AR system. Therefore, this study investigates how the AR system affects architectural design review based on the user’s perspectives. Three different display systems presenting a 3D model including a 2D screen, VR, and AR were tested, and a total of 76 participants evaluated visual presentation quality, perceived acceptability, and user experience according to their usage of the visualization platform types during the design review activities. Compared to other systems, the results indicated that the AR system could be more effective in reviewing the visual elements of a building. Furthermore, AR showed the highest ratings for acceptance level and user experience. The innovation provided by AR created a positive user experience, despite its remaining challenges to be resolved in terms of functionality. Since it is expected that the use of AR can be promoted by overcoming certain technological limitations, this study contributes to guiding AR system applications for end-users involved in the design review process.