Reviewerly/exHarmony huggingface dataset:
Linear Convergence of Adaptive Stochastic Gradient Descent

We prove that the norm version of the adaptive stochastic gradient method (AdaGrad-Norm) achieves a linear convergence rate for a subset of either strongly convex functions or non-convex functions that satisfy the Polyak Lojasiewicz (PL) inequality. The paper introduces the notion of Restricted Uniform Inequality of Gradients (RUIG)---which is a measure of the balanced-ness of the stochastic gradient norms---to depict the landscape of a function. RUIG plays a key role in proving the robustness of AdaGrad-Norm to its hyper-parameter tuning in the stochastic setting. On top of RUIG, we develop a two-stage framework to prove the linear convergence of AdaGrad-Norm without knowing the parameters of the objective functions. This framework can likely be extended to other adaptive stepsize algorithms. The numerical experiments validate the theory and suggest future directions for improvement.
Multi-tree genetic programming for feature construction-based domain adaptation in symbolic regression with incomplete data

Nowadays, transfer learning has gained a rapid popularity in tasks with limited data available. While traditional learning limits the learning process to knowledge available in a specific (target) domain, transfer learning can use parts of knowledge extracted from learning in a different (source) domain to help learning in the target domain. This concept is of special importance when there is a lack of knowledge in the target domain. Consequently, since data incompleteness is a serious cause of knowledge shortage in real-world learning tasks, it can be typically addressed using transfer learning. One way to achieve that is feature construction-based domain adaptation. However, although it is considered as a powerful feature construction algorithm, Genetic Programming has not been fully utilized for domain adaptation. In this work, a multi-tree genetic programming method is proposed for feature construction-based domain adaptation. The main idea is to construct a transformation from the source feature space to the target feature space, which maps the source domain close to the target domain. This method is utilized for symbolic regression with missing values. The experimental work shows encouraging potential of the proposed approach when applied to real-world tasks considering different transfer learning scenarios.
Optical configuration of modified Fredkin gate using lithium-niobate-based Mach–Zehnder interferometer

The continuous quest for reversible computation that could be extensively used in applications such as digital signal processing, quantum computing, quantum-dot cellular automata, and nanotechnology has recently discovered its optical implementation as light tenders high-speed computing with the slightest information loss. The electro-optic effect of a lithium-niobate-based Mach–Zehnder interferometer is explored to configure a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>4</mml:mn> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> modified Fredkin gate, capable of furnishing as many as 16 logical combinations, and thus showing potential of curbing the area overhead. The optical design is carried out using the beam propagation method. We have also performed the mathematical modeling and analyzed the results in MATLAB.
Thermal Image Reconstruction Using Deep Learning

A high-resolution thermal camera is very expensive and is thus difficult to be used. Furthermore, thermal images become blurred in various cases of object motion, camera shaking, and camera defocusing. To solve these problems, a previous super-resolution restoration (SRR) technique converting a thermal image acquired by a low-resolution camera into a high-resolution one, and a thermal image deblurring method have been researched. However, existing studies were performed based on 1-channel (grayscale) images. In addition, a large-sized and whole image has been used in the existing thermal image deblurring methods, which causes lower deblurring performance. In this study, we propose novel SRR and deblurring methods. The proposed deblurring method is conducted based on small region images. The proposed methods are also conducted using 3-channel (color) thermal images and generative adversarial networks. In addition, the performances of this method are compared in various color spaces (RGB, Gray, HLS, HSV, Lab, Luv, XYZ, YCrCb), image sizes, and thermal databases. Through experiments using self-collected databases and open databases, it was confirmed that the proposed methods show better performance than the state-of-the-art methods.
A Novel Adaptive H-Infinity Cubature Kalman Filter Algorithm Based on Sage-Husa Estimator for Unmanned Underwater Vehicle

In the navigation of unmanned underwater vehicle (UUV), a filtering algorithm suitable for the working conditions is required. Due to the disturbance from the environment and maneuverability, outliers and noise with time-varying statistical properties always exist, which greatly affect the positioning accuracy and stability of the navigation system. In this paper, we present a novel nonlinear state estimation algorithm named AH∞CKF based on the combination of H∞CKF and Sage-Husa estimator. The recently developed H∞CKF provides nonlinear filtering good robustness, and Sage-Husa estimator could timely modify the statistical properties of noise. The novel algorithm is superior to H∞CKF in accuracy by combining Sage-Husa estimator with the H∞CKF while ensuring robustness. The effectiveness of the novel AH∞CKF is verified by lake experiment and simulation.
Decentralized genomics audit logging via permissioned blockchain ledgering

Abstract Background One of the tasks in the iDASH Secure Genome Analysis Competition in 2018 was to develop blockchain-based immutable logging and querying for a cross-site genomic dataset access audit trail. The specific challenge was to design a time/space efficient structure and mechanism of storing/retrieving genomic data access logs, based on MultiChain version 1.0.4 ( https://www.multichain.com/ ). Methods Our technique uses the MultiChain stream application programming interface (which affords treating MultiChain as a key value store) and employs a two-level index, which naturally supports efficient queries of the data for single clause constraints. The scheme also supports heuristic and binary search techniques for queries containing conjunctions of clause constraints, and timestamp range queries. Of note, all of our techniques have complexity independent of inserted data set size, other than the timestamp ranges, which logarithmically scale with input size. Results We implemented our insertion and querying techniques in Python, using the MultiChain library Savoir ( https://github.com/dxmarkets/savoir ), and comprehensively tested our implementation across a benchmark of datasets of varying sizes. We also tested a port of our challenge submission to a newer version of MultiChain (2.0 beta), which natively supports multiple indices. Conclusions We presented creative and efficient techniques for storing and querying log file data in MultiChain 1.0.4 and 2.0 beta. We demonstrated that it is feasible to use a permissioned blockchain ledger for genomic query log data when data volume is on the order of hundreds of megabytes and query times of dozens of minutes is acceptable. We demonstrated that evolution in the ledger platform (MultiChain 1 to 2) yielded a 30%-40% increase in insertion efficiency. All source code for this challenge has been made available under a BSD-3 license from https://github.com/sandialabs/idash2018task1/ .
Apache Mahout: Machine Learning on Distributed Dataflow Systems

Apache Mahout is a library for scalable machine learning (ML) on distributed dataflow systems, offering various implementations of classification, clustering, dimensionality reduction and recommendation algorithms. Mahout was a pioneer in large-scale machine learning in 2008, when it started and targeted MapReduce, which was the predominant abstraction for scalable computing in industry at that time. Mahout has been widely used by leading web companies and is part of several commercial cloud offerings. In recent years, Mahout migrated to a general framework enabling a mix of dataflow programming and linear algebraic computations on backends such as Apache Spark and Apache Flink. This design allows users to execute data preprocessing and model training in a single, unified dataflow system, instead of requiring a complex integration of several specialized systems. Mahout is maintained as a community-driven open source project at the Apache Software Foundation, and is available under https://mahout.apache.org.
Quantification and Risk Assessment of Heavy Metals in Groundwater and Soil of Residential Areas around Awotan Landfill, Ibadan, Southwest-Nigeria

Non-sanitary landfill poses enormous threats to lives. In this study, heavy metals in groundwater and soil of residential areas around Awotan landfill, Ibadan, Southwest-Nigeria were quantified and associated environmental and health risks were assessed within 1-150 m and 151-300 m radii. A total of 20 samples of groundwater and soil each were analysed for Pb, Cd, Cr, Ni, Cu, Fe and Zn using Atomic Absorption Spectrometry (ASS).
Hate Code Detection in Indonesian Tweets using Machine Learning Approach: A Dataset and Preliminary Study

The existence of social media causes side effects from freedom of speech to freedom to hate. People can spread hate speech with creative ways to avoid the hate speech detector. Implicit intends used using many codes. The purpose of using these codes is to disguise their hate speech targets. This paper presents an implementation of hate code detection for Indonesian tweets using machine learning and a classification explainer. First, we developed a dataset for hate codes ground truth. We generated hate codes from two scenarios i.e., hate code from hate speech classification and hate code from hate code classification. We used Logistic Regression (LR), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) as our classifier. We also used TF-IDF and word bigrams as the features. The codes consist of word and phrase form. The best f-measure score is 94.90% from hate code classification using Logistic Regression with abusive codes elimination. This number means the model can detect all tweets that have no hate codes. For tweets that annotated have hate code, the f-measure is 28.23% for recognized all the hate codes, and the recall is 56.91%.
Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak Supervision

Recently, recommender systems have been used in various fields.However, they are still plagued by many issues, including cold-start and sparsity problems.The cold-start problem occurs when users are unable to make recommendations to other users owing to a complete lack of information about certain items.This problem can exist both at the user side and the item side.User-side cold-start problems occur when new users access the systems; item-side cold-start problems occur when new items are added to databases.In this study, we addressed the item-side cold-start problem using the concept of weak supervision.First, a new process for identifying feature based representative reviewers in a rater group was designed.Then, we developed a method to predict the expected preferences for new items by combining content-based filtering and the preferences of representative users.Through extensive experiments, we first confirmed that in comparison to existing methods, the proposed approach provided enhanced accuracy, which was evaluated by determining a mean absolute error for the average ratings.Then, we compared the proposed scheme with the collaborative filtering (CF) and neural CF approaches (NCF).The estimation by the proposed approach was 21% and 38% more accurate than CF and NCF in terms of mean absolute error (MAE), respectively.In future, the proposed framework can be applied in various recommender systems as a core function.
Restoration of Fast Moving Objects

If an object is photographed at motion in front of a static background, the object will be blurred while the background sharp and partially occluded by the object. The goal is to recover the object appearance from such blurred image. We adopt the image formation model for fast moving objects and consider objects undergoing 2D translation and rotation. For this scenario we formulate the estimation of the object shape, appearance, and motion from a single image and known background as a constrained optimization problem with appropriate regularization terms. Both similarities and differences with blind deconvolution are discussed with the latter caused mainly by the coupling of the object appearance and shape in the acquisition model. Necessary conditions for solution uniqueness are derived and a numerical solution based on the alternating direction method of multipliers is presented. The proposed method is evaluated on a new dataset.
An Efficient Generation and Security Analysis of Substitution Box Using Fingerprint Patterns

Information and its security have attracted the research community in recent years with increasing usage of mobile applications.Mobile devices have different security options in data transmission such as reading some biometric values.The keystone of the modern block and stream ciphers is the use of a substitution box (s-box) that obscures correlation between plaintext and ciphertext.In this study, we proposed a novel s-box generation algorithm by using the fingerprint pattern of the person who transfers information to the target.We generated several s-boxes by using bifurcation and ridge ending features of the fingerprint.Proposed s-boxes are compared with several known s-boxes over nonlinearity, bijectiveness, strict avalanche criterion, bit independence criterion, linear probability, and differential probability.Along with these properties, we analyzed confidence interval and randomness properties of new s-boxes as well.Also, the execution time of the proposed s-box generation algorithm is calculated and examined.The results of the cryptographic properties have shown that the proposed s-boxes by using ridge ending of the fingerprint performs better.The performances analysis show that the proposed s-box has satisfactory results according to the results of chaotic-based s-boxes.On the other hand, the fingerprint s-boxes are much better than the existing biometric s-boxes according to the s-box security metrics.The results have shown that the execution time of the proposed s-box generation algorithm is more minimum than the existing biometric s-box generation algorithms.Resulting from applying fingerprint biometric data to generate an s-box, such a successful algorithm is promising to be used in mobile devices.
Genetic Programming Hyper-Heuristics with Probabilistic Prototype Tree Knowledge Transfer for Uncertain Capacitated Arc Routing Problems

The Uncertain Capacitated Arc Routing Problem (UCARP) is an important combinatorial optimisation problem with extensive real-world applications. Genetic Programming (GP) has shown effectiveness in automatically evolving routing policies to handle the uncertain environment in UCARP. However, whenever a UCARP scenario changes, e.g. when a new vehicle is bought, the previously trained routing policy may no longer work effectively, and one has to retrain a new policy. Retraining a new policy from scratch can be time-consuming but the transfer of knowledge gained from solving the previous similar scenarios may help improve the efficiency of the retraining process. In this paper, we propose a novel transfer learning method by learning the probability distribution of good solutions from source domains and modelling it as a probabilistic prototype tree. We demonstrate that this approach is capable of capturing more information about the source domain compared to transfer learning based on (sub-)tree transfers and even create good trees that are not seen in source domains. Our experimental results showed that our method made the retraining process more efficient and one can obtain an initial state for solving difficult problems that is significantly better than existing methods. The final performance of all algorithms, were comparable, implying that there was no negative transfer.
AIRec: Attentive intersection model for tag-aware recommendation

Tag-aware recommender systems (TRS) utilize rich tagging information to better depict user portraits and item features. Recently, many efforts have been done to improve TRS with neural networks. However, existing methods construct user representations through either explicit tagging behaviors or implicit interacted items, which is inadequate to capture multi-aspect user preferences. Besides, there are still lacks of investigation about the intersection between user and item tags, which is crucial for better recommendation. In this paper, we propose AIRec, an attentive intersection model for TRS, to address the above issues. More precisely, we first project the sparse tag vectors into a latent space through multi-layer perceptron (MLP). Then, the user representations are constructed with a hierarchical attention network, where the item-level attention differentiates the contributions of interacted items and the preference-level attention discriminates the saliencies between explicit and implicit preferences. After that, the intersection between user and item tags is exploited to enhance the learning of conjunct features. Finally, the user and item representations are concatenated and fed to factorization machines (FM) for score prediction. We conduct extensive experiments on two real-world datasets, demonstrating significant improvements of AIRec over state-of-the-art methods for tag-aware top-n recommendation.
Target redirected regression with dynamic neighborhood structure

Least squares regression (LSR) has attracted widespread attention in the fields of statistics, machine learning, and pattern recognition. However, it utilizes strict zero-one regression targets, which leads to inferior performance on classification tasks. Furthermore, LSR ignores the local manifold structures of data and lacks robustness. To address these issues, this paper proposes a general regression framework called RLRR, where a low-rank constraint is imposed on regression matrices to explore the underlying correlation structures of classes. Strict zero-one regression targets are redirected to more feasible variable matrices for the purpose of margin amplification of different classes. Additionally, rather than using a pre-constructed weighted graph, the proposed framework dynamically updates the neighborhood structures of data to preserve original manifold structures. By utilizing this framework as a general platform, we developed two dynamic neighborhood-structure-based regression models called RLRRM and RLRRP. RLRRM integrates a reconstruction error minimization term into the proposed RLRR framework, whereas RLRRP aims to preserve the local geometric structures of data in a low-dimensional subspace. Both RLRRM and RLRRP use theℓ2,1-norm penalty to replace the traditional F-norm penalty for the projection matrix for the sake of self-adaptive feature selection. Instead of directly solving the resultant optimization problems with non-convex constraints, we adopt the variable-splitting and penalty techniques to derive an equivalent solution. Analysis of the corresponding convergence and computational complexity characteristics is also presented. Extensive experiments on several well-known datasets demonstrate the promising performance of the proposed models.
A Talent Cultivation Model for Improving the Innovation Ability of College Students in Scientific Research

The innovation ability of college students in scientific research is constrained by multiple factors. What is worse, the current talent training model in colleges faces many defects. To solve these problems, this paper aims to develop a talent cultivation model that effectively enhances the innovation ability of college students in scientific research. Firstly, the problems with the current talent cultivation model were analyzed, and then the evaluation indicator system was improved for the innovation ability of college students in scientific research. Next, entropy weight method and gray system theory were integrated to create an evaluation model that quantifies the innovation ability of college students in scientific research. On this basis, several strategies were put forward to improve the said ability. The research results help to effectively enhance the innovation ability of college students in scientific research, and optimize the talent cultivation model in colleges and universities.
Comparing Traditional and Mixed Reality-Facilitated Apprenticeship Learning in a Wood-Frame Construction Lab

Mixed reality (MR) has the potential to accelerate construction workforce training and enhance college education. However, there is a lack of empirical evidence in the research literature on how effective MR may be in facilitating student apprenticeship learning in field construction activities compared to the traditional paper-based approach. This research designed an outdoor wood-frame construction lab with two parallel sessions involving paper drawings (the control) and MR mockups (the experiment) for conveying design information. The research team used video recordings and questionnaires to collect behavioral and perception data for comparative analysis. The results indicated that student teams in the two sessions exhibited comparable construction productivity. However, they demonstrated different behavioral patterns and time allocation for technology use and reported different apprenticeship learning gains. This research also identified and discussed potential contributing factors that limited the success of MR. The contribution of this work resides in presenting empirical evidence of the impact of using MR on student apprenticeship learning through outdoor construction activities. It also provides peer educational researchers with valuable insights on how to study pedagogical use of MR with consideration of potential challenges that are present in realistic construction environments.
Learning Surrogates via Deep Embedding

This paper proposes a technique for training a neural network by minimizing a surrogate loss that approximates the target evaluation metric, which may be non-differentiable. The surrogate is learned via a deep embedding where the Euclidean distance between the prediction and the ground truth corresponds to the value of the evaluation metric. The effectiveness of the proposed technique is demonstrated in a post-tuning setup, where a trained model is tuned using the learned surrogate. Without a significant computational overhead and any bells and whistles, improvements are demonstrated on challenging and practical tasks of scene-text recognition and detection. In the recognition task, the model is tuned using a surrogate approximating the edit distance metric and achieves up to \(39\%\) relative improvement in the total edit distance. In the detection task, the surrogate approximates the intersection over union metric for rotated bounding boxes and yields up to \(4.25\%\) relative improvement in the \(F_{1}\) score.
The blockchain technology conundrum: Quis custodiet ipsos custodes?

This paper considers how current voluntary policies, such as eco-labelling schemes could be affected by increased and immutable transparencies embedded in their business models as a result of blockchain transactions. The paper investigates the synergetic overlap between ecolabels and the uptake of blockchain technology (BCT). Furthermore, it points to current ecolabels' inefficiencies and discusses both the technology's potential for improvements of ecolabels' effectiveness, ecolabels' purpose on the market as well as BCT's readiness for its wide-spread adoption. The paper also presents numerous blockchain use cases with relevance to the enhanced purpose of eco-labelling schemes.