Reviewerly/exHarmony huggingface dataset:
Collaborative Confusion among DIY Makers

Eco-oriented makers and grassroots subcultures experimenting with digital fabrication technologies, and other activists designing sustainable futures, are increasingly the subject of research. As they address problems of environmental sustainability beyond institutional contexts, their work may appear vague, even confused, yet their activities are underpinned by intense and principled commitment. Working through their confusion, many maker communities build new understandings about what ‘sustainability’ could mean. We argue that herein lie important resources for new knowledge, and further that ethnography is the ideal way to track these processes of learning and knowledge production. The ethnographer participates in local confusion, over values and the definitions of sustainability, but also about what constitutes useful knowledge. Supported by STS (and other) literature on environmental expertise, we argue that maker communities' own acknowledgement of this vagueness actually makes possible a position from which epistemological authority can be reasserted.
Design and Implementation of a Computerized User Authentication System for E-Learning

The system based on formalized teaching with the help of electronic resources is called as E-learning. Using E-Learning in the university education systems has been growing rapidly based on the application of Information Technology (IT) designed for the substitute of traditional based learning approach. In terms of the E-learning based examinations, security constraints such as, identification of the user, misuse the passwords, unauthorized user instead of the authenticated one are available. Because of these disadvantages, the need of an authentication system is very important. The main challenge facing the security of E-learning environment is how to identify the unauthorized users from accessing and managing any platform. Therefore solving the problem of authenticating the valid user is very important. So, an intelligent examination system along with user authentication based on facial expression recognition is proposed in this paper. The proposed system can be classified in to three phases. The first phase is the design of facial database from the captured images. The second phase is the design of Artificial Intelligence (AI) based system for facial feature extraction and its classification using intelligent Agents. The final phase is the authentication of respective users by automatic identification of facial images. The results obtained shows that the proposed methodology has managed to validate the authenticated users and to provide an effective security in the E-learning platform.
Risk Minimization Against Transmission Failures of Federated Learning in Mobile Edge Networks

A variety of modern AI products essentially require raw user data for training diverse machine learning models. With the increasing concern on data privacy, federated learning, a decentralized learning framework, enables privacy-preserving training of models by iteratively aggregating model updates from participants, instead of aggregating raw data. Since all the participants, i.e., mobile devices, need to transfer their local model updates concurrently and iteratively over mobile edge networks, the network is easily overloaded, leading to a high risk of transmission failures. Although previous works on transmission protocols have already tried their best to avoid transmission collisions, the number of iterative concurrent transmissions should be fundamentally decreased. Inspired by the fact that raw data are often generated unevenly among devices, those devices with a small proportion of data could be properly excluded since they have little effect on the convergence of models. To further guarantee the accuracy of models, we propose to properly select a subset of devices as participants to ensure the given proportion of involved data. Correspondingly, we propose to minimize the risk against the transmission failures during model updates. Afterwards, we design a randomized algorithm ($ran$ RFL) to choose suitable participants by using a series of delicately calculated probabilities, and prove that the result is concentrated on its optimum with high probability. Extensive simulations show that through delicate participant selection, $ran$ RFL decreases the maximal error rate of model updates by up to 38.3% compared with the state-of-the-art schemas.
An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount

To improve the optimization quality, stability, and speed of convergence of wolf pack algorithm, an adaptive shrinking grid search chaotic wolf optimization algorithm using standard deviation updating amount (ASGS-CWOA) was proposed. First of all, a strategy of adaptive shrinking grid search (ASGS) was designed for wolf pack algorithm to enhance its searching capability through which all wolves in the pack are allowed to compete as the leader wolf in order to improve the probability of finding the global optimization. Furthermore, opposite-middle raid method (OMR) is used in the wolf pack algorithm to accelerate its convergence rate. Finally, “Standard Deviation Updating Amount” (SDUA) is adopted for the process of population regeneration, aimed at enhancing biodiversity of the population. The experimental results indicate that compared with traditional genetic algorithm (GA), particle swarm optimization (PSO), leading wolf pack algorithm (LWPS), and chaos wolf optimization algorithm (CWOA), ASGS-CWOA has a faster convergence speed, better global search accuracy, and high robustness under the same conditions.
A new integrated MCDM approach for lecturers’ research productivity evaluation

Evaluating research productivity of lecturer is a vital process of universities for teaching improvement and administrative decision making.Lecturers' research productivity evaluation is a difficult and sensitive problem which has many objective and subjective criteria, complexity and imprecision.Therefore, the evaluation of lecturers' research productivity can be viewed as a multi-criteria decision making (MCDM) problem in vague environment.This study develops an integrated MCDM approach for evaluating the lecturers' research productivity.In the proposed approach, a fuzzy analytic hierarchy process (AHP) is applied to determine the weights of criteria.A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method using interval neutrosophic sets is further adopted for showing the preference order of the lecturers' research productivity in an educational organization.Finally, the proposed approach is applied to solve the lecturers' research productivity evaluation problem in the case of University of Economics and Business, Vietnam National University, Hanoi (UEB-VNU)..
Mortality prediction enhancement in end-stage renal disease: A machine learning approach

In this work, we propose to combine massive variables collected during the evolution of patients in end-stage renal disease (ESRD), along with machine learning techniques to improve mortality prediction in ESRD. This work was carried out with a retrospective cohort of 261 patients, their evolution from diagnoses, laboratory tests, and variables recorded during haemodialysis sessions was combined. Random forest (RF) was used to explore the inference of the variables and define a base performance for long short-term memory (LSTM) recurrent neural networks. Then, LSTMs were trained with several groups of variables chosen by expert staff, the ones found by RF and all the available ones. The best performance was obtained using all the variables, but the ones found by RF had better predictive capacity than those chosen with expert knowledge. Integrating the three sources of information supposes an improvement in more than 4% in the area under the receiver operating characteristic curve. The approach is sufficiently robust to predict mortality at different time ranges. The massive integration of variables from patients in ESRD, together with the use of LSMTs, supposes an exceptional improvement in the predictive models of mortality. In conclusion, the machine learning approach can lead to a change in the paradigm in the analysis of predictive factors in mortality in ESRD.
Words in Pairs Neural Networks for Text Classification

Existing methods utilized single words as text features. Some words contain multiple meanings, and it is difficult to distinguish its specific classification according to a single word, which probably affects the accuracy of the text classification. Propose a framework based on Words in pairs neural networks (WPNN) for text classification. Words in pairs include all single word combinations which have a high mutual association. Mine the crucial explicit and implicit Words in pairs as text features. These words in pairs as a text feature are easily classified. The words in pairs are utilized as the input of the neural network, which provides a better classification ability to the model, because they are more recognizable than the single word. Experimental results show that our model outperforms five benchmark algorithms.
A substrate for modular, extensible data-visualization

As the scope of scientific questions increase and datasets grow larger, the visualization of relevant information correspondingly becomes more difficult and complex. Sharing visualizations amongst collaborators and with the public can be especially onerous, as it is challenging to reconcile software dependencies, data formats, and specific user needs in an easily accessible package.We present substrate, a data-visualization framework designed to simplify communication and code reuse across diverse research teams. Our platform provides a simple, powerful, browser-based interface for scientists to rapidly build effective three-dimensional scenes and visualizations. We aim to reduce the limitations of existing systems, which commonly prescribe a limited set of high-level components, that are rarely optimized for arbitrarily large data visualization or for custom data types.To further engage the broader scientific community and enable seamless integration with existing scientific workflows, we also present pytri, a Python library that bridges the use of substrate with the ubiquitous scientific computing platform, Jupyter. Our intention is to lower the activation energy required to transition between exploratory data analysis, data visualization, and publication-quality interactive scenes.
Attack-Resilient TLS Certificate Transparency

The security of Public-Key Infrastructure (PKI) for Internet-based communications has lately attracted researchers' attention because of Certification Authorities (CAs) crashes and consequent attacks. Google Certificate Transparency and subsequent log-based PKI proposals (e.g., AKI and ARPKI) have succeeded in making certificate-management processes more transparent, accountable, and verifiable. However, those proposals failed to solve the root CA generous delegation of trust to intermediate CAs, non-conformant certificate-issuance by them, and lack of rigorous authentication of domain ownership during certificate-issuance problems. This study presents Attack-Resilient TLS Certificate Transparency (ARCT) based on log servers to address these problems. ARCT enables root CA to enforce intermediate CAs to follow community standards through leveraging a log server at each root level. It also introduces a collaborative domain ownership verification method that deters false certificate-issuance and ensures that a set of CAs validates every certificate before any client will accept it. A certificate collectively approved by a set of CAs assures users that the certificate has been seen, and not instantly detected malicious, by a group of CAs. Finally, formal security and performance evaluations prove the reliability and effectiveness of ARCT.
Interpretable Batch IRL to Extract Clinician Goals in ICU Hypotension Management.

Exposing and understanding the motivations of clinicians is an important step for building robust assistive agents as well as improving care. In this work, we focus on understanding the motivations for clinicians managing hypotension in the ICU. We model the ICU interventions as a batch, sequential decision making problem and develop a novel interpretable batch variant of Adversarial Inverse Reinforcement Learning algorithm that not only learns rewards which induce treatment policies similar to clinical treatments, but also ensure that the learned functional form of rewards is consistent with the decision mechanisms of clinicians in the ICU. We apply our approach to understanding vasopressor and IVfluid administration in the ICU and posit that this interpretability enables inspection and validation of the rewards robustly.
Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification

Deep learning has been successful for many computer vision tasks due to the availability of shared and centralised large-scale training data. However, increasing awareness of privacy concerns poses new challenges to deep learning, especially for human subject related recognition such as person re-identification (Re-ID). In this work, we solve the Re-ID problem by decentralised learning from non-shared private training data distributed at multiple user sites of independent multi-domain label spaces. We propose a novel paradigm called Federated Person Re-Identification (FedReID) to construct a generalisable global model (a central server) by simultaneously learning with multiple privacy-preserved local models (local clients). Specifically, each local client receives global model updates from the server and trains a local model using its local data independent from all the other clients. Then, the central server aggregates transferrable local model updates to construct a generalisable global feature embedding model without accessing local data so to preserve local privacy. This client-server collaborative learning process is iteratively performed under privacy control, enabling FedReID to realise decentralised learning without sharing distributed data nor collecting any centralised data. Extensive experiments on ten Re-ID benchmarks show that FedReID achieves compelling generalisation performance beyond any locally trained models without using shared training data, whilst inherently protects the privacy of each local client. This is uniquely advantageous over contemporary Re-ID methods.
Application of Artificial Intelligence for the Estimation of Concrete and Reinforcement Consumption in the Construction of Integral Bridges

Estimation of basic material consumption in civil engineering is very important in the initial phases of project implementation. Its importance is reflected in the impact of material quantities on forming the prices of individual positions, hence on forming the total cost of construction. The construction companies use the estimate of material quantity, among other things, as a base to make a bid on the market. The precision of the offer, taking into account the overall conditions of the business realization, directly influences the profit that the company can make on a specific project. In the early stages of project implementation, there are not enough available data, especially when it comes to the data needed to estimate material consumption, and therefore, the accuracy of material consumption estimation in the early stages of project realization is smaller. The paper presents the research on the use of artificial intelligence for the estimation of concrete and reinforcement consumption and the selection of optimal models for estimation. The estimation model was developed by using artificial neural networks. The best artificial neural network model showed high accuracy in material consumption estimation expressed as the mean absolute percentage error, 8.56% for concrete consumption estimate and 17.31% for reinforcement consumption estimate.
Distributed Ledgers for Spectrum Authorization

The rising demand for wireless services can only be met if the available spectrum is shared effectively, and spectrum underutilization is avoided.This paper examines how a DLT-based spectrum authorization system may be designed considering its protocol, data, and network operational layers.Decentralization, automation, and verifiable trust achieved by this system facilitate spectrum sharing and trading, catering to business objectives in addition to the influences from regulatory and technological developments.To this end, we elaborate on the technical feasibility, scalability and business incentives for such a system to stimulate the multiple cross-industry stakeholders to use the radio spectrum more efficiently for 5G and beyond.
Survey of Signature Verification Databases

Signature verification is an important emerging field.New results get published both in the online and offline areas.It's often hard to compare the results because researchers use different databases.Although there are some publicly available databases their quality and size may vary.This paper aims to deliver a comprehensive list of the most significant databases available in the offline and online field.Using our results researchers around the world can chose the best common database that fits to their needs.
Unsupervised Transfer Learning via Relative Distance Comparisons

Primitive machine learning method such as Support Vector Machine (SVM) or k-Nearest Neighbor (k-NN) faces a major challenge when its training and test data is distributed with large-scale variations in lighting conditions, color, backgrounds, size, etc. The variation may be because training and testing data can come from related but some other domains. Considerable efforts have been made in the development of transfer learning methods. However, most current work focuses only on the following goals or objectives: i)Preservation of source domain discriminative information with Linear Discriminant Analysis (LDA); ii)Maximization of target domain variance; iii) Subspace alignment; iv) Minimization of marginal and conditional distribution by using the Maximum Mean Discrepancy (MMD) criterion;v)Preservation of original similarity of the data samples. Current approaches to preserve source domain discriminant information can easily misclassify the target domain samples which are distributed near the edge of the cluster. In order to overcome the limitations of existing transfer learning methods, we propose a novel Unsupervised Transfer Learning Via Relative Distance Comparisons (UTRDC) method. UTRDC optimizes all the aforementioned objectives jointly with a common projection vector matrix for both domains as well as uses the relative distance constraints for better inter-class separability and intra-class compactness. Furthermore, we extend our proposed method UTRDC to a kernelized version to deal with non-linear separable datasets. Extensive experimentation on two real-world problems datasets (PIE face and Office+Caltech) has proven that the proposed methods outperform several approaches to non-transfer learning and transfer learning.
Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change

The choice of hyper-parameters affects the performance of neural models.While much previous research (Sutskever et al., 2013;Duchi et al., 2011;Kingma and Ba, 2015) focuses on accelerating convergence and reducing the effects of the learning rate, comparatively few papers concentrate on the effect of batch size.In this paper, we analyze how increasing batch size affects gradient direction, and propose to evaluate the stability of gradients with their angle change.Based on our observations, the angle change of gradient direction first tends to stabilize (i.e.gradually decrease) while accumulating mini-batches, and then starts to fluctuate.We propose to automatically and dynamically determine batch sizes by accumulating gradients of mini-batches and performing an optimization step at just the time when the direction of gradients starts to fluctuate.To improve the efficiency of our approach for large models, we propose a sampling approach to select gradients of parameters sensitive to the batch size.Our approach dynamically determines proper and efficient batch sizes during training.In our experiments on the WMT 14 English to German and English to French tasks, our approach improves the Transformer with a fixed 25k batch size by +0.73 and +0.82 BLEU respectively.
JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search

An accurate understanding of a user's query intent can help improve the performance of downstream tasks such as query scoping and ranking. In the e-commerce domain, recent work in query understanding focuses on the query to product-category mapping. But, a small yet significant percentage of queries (in our website 1.5% or 33M queries in 2019) have non-commercial intent associated with them. These intents are usually associated with non-commercial information seeking needs such as discounts, store hours, installation guides, etc. In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query. JointMap model works by leveraging the transfer bias that exists between these two related tasks through a joint-learning process. As curating a labeled data set for these tasks can be expensive and time-consuming, we propose a distant supervision approach in conjunction with an active learning model to generate high-quality training data sets. To demonstrate the effectiveness of JointMap, we use search queries collected from a large commercial website. Our results show that JointMap significantly improves both "commercial vs. non-commercial" intent prediction and product category mapping by 2.3% and 10% on average over state-of-the-art deep learning methods. Our findings suggest a promising direction to model the intent hierarchies in an e-commerce search engine.
Class Prior Estimation in Active Positive and Unlabeled Learning

Estimating the proportion of positive examples (i.e., the class prior) from positive and unlabeled (PU) data is an important task that facilitates learning a classifier from such data. In this paper, we explore how to tackle this problem when the observed labels were acquired via active learning. This introduces the challenge that the observed labels were not selected completely at random, which is the primary assumption underpinning existing approaches to estimating the class prior from PU data. We analyze this new setting and design an algorithm that is able to estimate the class prior for a given active learning strategy. Empirically, we show that our approach accurately recovers the true class prior on a benchmark of anomaly detection datasets and that it does so more accurately than existing methods.