Reviewerly/exHarmony huggingface dataset:
Detection and recognition of batteries on X-Ray images of waste electrical and electronic equipment using deep learning

The trend of increased use of lithium-ion batteries, challenges the cost-effectiveness and safety of manual battery separation during the end-of-life treatment of Waste Electric and Electronic Equipment (WEEE). Therefore, the need for novel techniques to separate and sort batteries from WEEE is increasingly important. For this reason, the presented research investigates the potential to facilitate the development of novel techniques for battery extraction and sorting by examining the technical feasibility of predicting the presence, location, and type of batteries inside electronic devices with a deep learning object detection network using X-Ray images of the internal structure of WEEE. To determine the required X-ray imaging parameters, 532 electronic devices were arbitrarily collected from a recycling facility. From each product, two X-Ray Transmission (XRT) images were captured at two different X-Ray source configurations. Results obtained with the limited dataset are promising, demonstrating a 91% true positive rate and only a 6% false positive rate for classifying battery-containing devices. Moreover, a precision of 89% and a recall of 81% are demonstrated for battery detection, and an average precision of 85% and an average recall of 76% are demonstrated to distinguish amongst the following six battery technologies: cylindrical nickel-metal hydride or nickel-cadmium, cylindrical alkaline, cylindrical zinc-carbon, cylindrical lithium-ion, pouch lithium-ion, and button cell batteries. These results demonstrate the potential of using deep learning object detection on XRT-generated images for both automated battery extraction and sorting, regardless of the condition or shape of the products.
SOLOv2: Dynamic and Fast Instance Segmentation

In this work, we aim at building a simple, direct, and fast instance segmentation framework with strong performance. We follow the principle of the SOLO method of Wang et al. SOLO: segmenting objects by locations. Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location. Specifically, the mask branch is decoupled into a mask kernel branch and mask feature branch, which are responsible for learning the convolution kernel and the convolved features respectively. Moreover, we propose Matrix NMS (non maximum suppression) to significantly reduce the inference time overhead due to NMS of masks. Our Matrix NMS performs NMS with parallel matrix operations in one shot, and yields better results. We demonstrate a simple direct instance segmentation system, outperforming a few state-of-the-art methods in both speed and accuracy. A light-weight version of SOLOv2 executes at 31.3 FPS and yields 37.1% AP. Moreover, our state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation show the potential to serve as a new strong baseline for many instance-level recognition tasks besides instance segmentation. Code is available at: this https URL
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network algorithms.This study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution.
Sparse Instance Activation for Real-Time Instance Segmentation

In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers. In contrast, we propose a sparse set of instance activation maps, as a new object representation, to high-light informative regions for each foreground object. Then instance-level features are obtained by aggregating features according to the highlighted regions for recognition and segmentation. Moreover, based on bipartite matching, the instance activation maps can predict objects in a one-to-one style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to the simple yet effective designs with instance activation maps, SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark, which significantly out-performs the counterparts in terms of speed and accuracy. Code and models are available at https://github.com/hustvl/SparseInst.
A Blockchain-based decentralized system to ensure the transparency of organic food supply chain

Low quality agricultural products are added to the market daily. Over usage of chemicals in the production process, use of uncertified chemicals and mechanisms for preservation and ripening processes, are the major issues that impact on agricultural product's quality as well as overall health of the consumers. Mechanisms to identify the quality of the agricultural products are highly demanded due to the lack of transparency in the current process. Blockchain technology is emerging as a decentralized and secure infrastructure which can replace involvement of a third party to verify the transactions within the system. The purpose of the research was to implement a Blockchain based solution to verify the food quality and the origin of the agricultural supply chain. A public Blockchain concept was selected instead of a private Blockchain in this study to ensure transparency by allowing any person to access the network. Instances of the smart contract were created for each physical product and deployed to Blockchain network. A Quick Response code which contained the address of the instance, was a reference to the virtual product. All the actors who are involved in the supply chain must be able to interact with the system to achieve the transparency. Each transaction and events related to a product is validated by peers of the Blockchain system. Product ownership was changed for each relevant transaction. A token-based mechanism was used to indicate the farmers' reputation with their products. Farmers could place a certification request regarding their products and, they can gain reputation tokens for each certification done by peers. A unique Quick Response code was used to identify each product within the supply chain. The proposed system has been implemented as a prototype and validated within the study.
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings

Word embedding models have gained a lot of traction in the Natural Language Processing community, however, they suffer from unintended demographic biases. Most approaches to evaluate these biases rely on vector space based metrics like the Word Embedding Association Test (WEAT). While these approaches offer great geometric insights into unintended biases in the embedding vector space, they fail to offer an interpretable meaning for how the embeddings could cause discrimination in downstream NLP applications. In this work, we present a transparent framework and metric for evaluating discrimination across protected groups with respect to their word embedding bias. Our metric (Relative Negative Sentiment Bias, RNSB) measures fairness in word embeddings via the relative negative sentiment associated with demographic identity terms from various protected groups. We show that our framework and metric enable useful analysis into the bias in word embeddings.
Time-series Generative Adversarial Networks

A good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time. Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time-series data. At the same time, supervised models for sequence prediction - which allow finer control over network dynamics - are inherently deterministic. We propose a novel framework for generating realistic time-series data that combines the flexibility of the unsupervised paradigm with the control afforded by supervised training. Through a learned embedding space jointly optimized with both supervised and adversarial objectives, we encourage the network to adhere to the dynamics of the training data during sampling. Empirically, we evaluate the ability of our method to generate realistic samples using a variety of real and synthetic time-series datasets. Qualitatively and quantitatively, we find that the proposed framework consistently and significantly outperforms state-of-the-art benchmarks with respect to measures of similarity and predictive ability.
Reconciling meta-learning and continual learning with online mixtures of tasks

Learning-to-learn or meta-learning leverages data-driven inductive bias to increase the efficiency of learning on a novel task. This approach encounters difficulty when transfer is not advantageous, for instance, when tasks are considerably dissimilar or change over time. We use the connection between gradient-based meta-learning and hierarchical Bayes to propose a Dirichlet process mixture of hierarchical Bayesian models over the parameters of an arbitrary parametric model such as a neural network. In contrast to consolidating inductive biases into a single set of hyperparameters, our approach of task-dependent hyperparameter selection better handles latent distribution shift, as demonstrated on a set of evolving, image-based, few-shot learning benchmarks.
Incorporating Blockchain technology in construction management

Purpose This study explores the use of Blockchain technology as a new solution to many current problems in construction information management. The study shows that Blockchain has the potential to address several issues such as confidentiality, provenance tracking; monitoring channel and ledger metrics; disintermediation; non-repudiation; change tracing; multiparty aggregation; traceability inter-organizational recordkeeping; and data ownership. Design A systematic analysis of a paper published, “Potentials of Blockchain Technology for Construction Management,” is offered. The structured results are provided for the purpose of contributing to the discussion of the topic. Findings The results of this study shows that the suitable position for the integration of Blockchain is the interface-points of the transaction processing component of the Building Information Modeling server. This technology also can help in controlling and fingerprinting all information exchanges and communication. The conclusion drawn from the study provides a foundation from which further research can be developed. Originality and Value The findings of this study will help construction project managers and senior executives with a deeper understanding of Blockchain technology and its long-term implications for the construction industry; coupled with knowledge of its relationship to other emerging technologies such as BIM. Propositions for smart contracts deployment and further research are suggested.
Interactive Removal of Microphone Object in Facial Images

Removing a specific object from an image and replacing the hole left behind with visually plausible backgrounds is a very intriguing task. While recent deep learning based object removal methods have shown promising results on this task for some structured scenes, none of them have addressed the problem of object removal in facial images. The objective of this work is to remove microphone object in facial images and fill hole with correct facial semantics and fine details. To make our solution practically useful, we present an interactive method called MRGAN, where the user roughly provides the microphone region. For filling the hole, we employ a Generative Adversarial Network based image-to-image translation approach. We break the problem into two stages: inpainter and refiner. The inpainter estimates coarse prediction by roughly filling in the microphone region followed by the refiner which produces fine details under the microphone region. We unite perceptual loss, reconstruction loss and adversarial loss as joint loss function for generating a realistic face and similar structure to the ground truth. Because facial image pairs with or without microphone do not exist, we have trained our method on a synthetically generated microphone dataset from CelebA face images and evaluated on real world microphone images. Our extensive evaluation shows that MRGAN performs better than state-of-the-art image manipulation methods on real microphone images although we only train our method using the synthetic dataset created. Additionally, we provide ablation studies for the integrated loss function and for different network arrangements.
Graph temporal ensembling based semi-supervised convolutional neural network with noisy labels for histopathology image analysis

Although convolutional neural networks have achieved tremendous success on histopathology image classification, they usually require large-scale clean annotated data and are sensitive to noisy labels. Unfortunately, labeling large-scale images is laborious, expensive and lowly reliable for pathologists. To address these problems, in this paper, we propose a novel self-ensembling based deep architecture to leverage the semantic information of annotated images and explore the information hidden in unlabeled data, and meanwhile being robust to noisy labels. Specifically, the proposed architecture first creates ensemble targets for feature and label predictions of training samples, by using exponential moving average (EMA) to aggregate feature and label predictions within multiple previous training epochs. Then, the ensemble targets within the same class are mapped into a cluster so that they are further enhanced. Next, a consistency cost is utilized to form consensus predictions under different configurations. Finally, we validate the proposed method with extensive experiments on lung and breast cancer datasets that contain thousands of images. It can achieve 90.5% and 89.5% image classification accuracy using only 20% labeled patients on the two datasets, respectively. This performance is comparable to that of the baseline method with all labeled patients. Experiments also demonstrate its robustness to small percentage of noisy labels.
Multi-criteria decision support for geothermal resources exploration based on remote sensing, GIS and geophysical techniques along the Gulf of Suez coastal area, Egypt

The spatial prediction of geothermal sites along the Gulf of Suez coastal area, is an essential goal as an attempt for the development of renewable energy sources in Egypt. Remote sensing, geophysical methods and Geographic Information System (GIS) were integrated to appraise the promising geothermal zones. Hot-springs and wells data were collected initially in database for modeling the potential zones through the Bayesian statistical approach. Multi-source datasets were selected to derive factor layers indicating the distribution of geothermal energy such as topographic and geologic maps, Bouguer gravity anomaly and aeromagnetic reduced-to-the-pole maps, seismic data, satellite images, earthquake activities, and Bottom Hole Temperature (BHT) from oil and gas wells. Eight evidence layers were derived, analyzed, and integrated in a GIS platform to develop a Geothermal Potential Model (GPM). The representative ranks and weights were estimated to the spatial evidence layers and their classes respectively based on information value (IV) and weights-of-evidence (WoE) analyses. The eight evidence layers include proximity to major fault, lineaments density, land surface temperature, density of seismic activities, heat flow, temperature gradient at different depths, Curie point depth, and Bouguer anomaly. A geothermal potential map was created and classified the study area into five relatively zones, varied from very poor to very good potential. The very good potential sites are distributed mainly in the highly deep-structured and permeable lithologies. The new promising sites are expected to be in Ras Matarma, Ras Badran, Abu Rudies, Belayim, Abu Durba, El-Tur and some sites in southwestern shoreline of Gulf of Suez. The geothermal potential map was correlated and tested against the distribution of thermal wells and hot-springs. The holistic integrated approach suggests an innovative technique to design management plan and development actions along Gulf of Suez coastal area.
Evaluating Design Fiction

Design fiction has become so widely adopted that it regularly appears in contexts ranging from CEO speeches to dedicated tracks at academic conferences. However, evaluating this kind of work is difficult; it is not clear what good or bad design fiction is or what the judgment criteria should be. In this paper we assert that design fiction is a heterogeneous set of methods, and practices, able to produce a diversity of scholarly and design contributions. We argue locating these diverse practices under the single header of "design fiction" has resulted in epistemological confusion over the appropriate method of evaluation. We identify different traditions within the HCI literature-critical design; narratology and literary theory; studio-based design "crits"; user studies; scenarios and persona development; and thought experiments-to articulate a typology of evaluative frames. There is often a mismatch between the standards to which design fiction is held and the knowledge that speculative methods seek to produce. We argue that evaluating a given instance of design fiction requires us to properly select the right epistemological tool for the job.
FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems

Recommender systems are often biased toward popular items. In other words, few items are frequently recommended while the majority of items do not get proportionate attention. That leads to low coverage of items in recommendation lists across users (i.e. low aggregate diversity) and unfair distribution of recommended items. In this paper, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation for improving aggregate diversity. The algorithm iteratively finds items that are rarely recommended yet are high-quality and add them to the users' final recommendation lists. This is done by solving the maximum flow problem on the recommendation bipartite graph. While we focus on aggregate diversity and fair distribution of recommended items, the algorithm can be adapted to other recommendation scenarios using different underlying definitions of fairness. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, while significantly improving aggregate diversity, provides comparable recommendation accuracy.
A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control

A global path planning method is proposed based on improved ant colony optimization according to the slow convergence speed in mobile service robot path planning. The distribution of initial pheromone is determined by the critical obstacle influence factor. The influence factor is introduced into the heuristic information to improve the convergence speed of the algorithm at an early stage. A new pheromone update rule is presented using fuzzy control to change the value of pheromone heuristic factor and expectation heuristic factor, adjusting the evaporation rate in stages. The method achieves fast convergence and guarantees global search capability. Finally, the simulation results show that the improved algorithm not only shortens the running time of global path planning, but also has a higher probability of obtaining a global optimal solution. The convergence speed of the algorithm is better than the traditional ant colony algorithm.
Using Blockchain Technology to Enhance the Traceability of Original Achievements

The protection of original achievements including patents and copyrights is a significant and critical issue in today's knowledge-driven economy. In this article, we propose and develop a blockchain service architecture to enhance information traceability and protection effectiveness. Our proposed service architecture is enabled by the core functionalities of blockchain technology including distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Through this blockchain-based traceability system, we can record, secure, validate, and track original achievements’ registrations and other related transactions. The results of our simulation experiments further validate the effectiveness and robustness of the proposed traceability system design. Our experimental analysis shows that the proposed blockchain-based design can effectively enhance original achievements traceability with an embedded automatic incentive rewarding mechanism for both the creation and protection of original achievements. Moreover, our performance analysis and comparative analysis show that the proposed method has more advantages over several other methods. While our study provides an effective solution to address the existing challenges in intellectual property rights and achievements patents protection, the breadth of the blockchain concept and its applications require nuanced investigations in many different contexts. Thus, the proposed blockchain-based traceability system highlights the potential for broader applications of this disruptive technology in many other domains.