Reviewerly/exHarmony huggingface dataset:
Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?

Minimal Learning Machine (MLM) is a recently popularized supervised learning method, which is composed of distance-regression and multilateration steps. The computational complexity of MLM is dominated by the solution of an ordinary least-squares problem. Several different solvers can be applied to the resulting linear problem. In this paper, a thorough comparison of possible and recently proposed, especially randomized, algorithms is carried out for this problem with a representative set of regression datasets. In addition, we compare MLM with shallow and deep feedforward neural network models and study the effects of the number of observations and the number of features with a special dataset. To our knowledge, this is the first time that both scalability and accuracy of such a distance-regression model are being compared to this extent. We expect our results to be useful on shedding light on the capabilities of MLM and in assessing what solution algorithms can improve the efficiency of MLM. We conclude that (i) randomized solvers are an attractive option when the computing time or resources are limited and (ii) MLM can be used as an out-of-the-box tool especially for high-dimensional problems.
Research on Blockchain-Based Identity Authentication Scheme in Social Networks

In service-oriented social network, aiming at the unreliability problems of reviews submitted by users to the service vendors, this paper puts forword a blockchain-based identity authentication scheme. By considering the features of openness and immutability of blockchain, the proposed scheme constructs an identity registration process and an authentication process based on a blockchain network. In the proposed scheme, the identity authentication function is implemented by combining the cryptography confidentiality and the non-modifiability of the blockchain network. Therefore, only the users who have successfully been authenticated as the legitimate users can submit reviews to the service vendors. Besides, the registration and authentication records of the users’ identity are all stored in the blockchain network, as well as the reviews submitted by the users to the service vendors. As a result, this scheme can ensure the authenticity and credibility of users’ reviews of service vendors so that other users can obtain the credible reviews of the service vendors.
The topology of shapes made with points

In architecture, city planning, visual arts, and other design areas, shapes are often made with points, or with structural representations based on point-sets. Shapes made with points can be understood more generally as finite arrangements formed with elements (i.e. points) of the algebra of shapes U i , for i = 0. This paper examines the kind of topology that is applicable to such shapes. From a mathematical standpoint, any “shape made with points” is equivalent to a finite space, so that topology on a shape made with points is no different than topology on a finite space: the study of topological structure naturally coincides with the study of preorder relations on the points of the shape. After establishing this fact, some connections between the topology of shapes made with points and the topology of “point-free” pictorial shapes (when i &gt; 0) are defined and the main differences between the two are summarized.
On Edgeworth models for count time series

Inspired by the Edgeworth–Portnoy model for Gaussian time series, a family of randomized moving window (RMW) and randomized moving sum (RMS) models for stationary count time series is proposed. For the RMW process, we derive Markov properties which, in turn, allow to conclude on a connection of the RMS model to the Hidden-Markov model. This connection is used to develop an efficient scheme for maximum likelihood estimation. Then, we derive marginal and serial moment properties of the RMS process. It commonly exhibits an autoregressive autocorrelation structure, but also forms of a long memory are possible. The latter holds in particular for the proposed extended RMS model, which also satisfies certain Markov properties.
CRSG: a serious game for teaching code review

The application of code review in a development environment is essential, but this skill is not taught very often in an educational context despite its wide usage. To streamline the teaching process of code review, we propose a browser based "Code Review Serious Game" (CRSG) with high accessibility, progressive level difficulty and an evolvable foundation for prospective improvements or changes. The application is built as a serious game to reinforce the learning experience of its users by immersing them in its story and theme, helping them learn while having fun. The effectiveness of the game components are measured with a case study of 132 students of 2 software engineering courses. The promising result of this case study suggests CRSG can indeed be used effectively to teach code review. The demo video for the game can be accessed at https://youtu.be/FLnr3p4bhOg, and CRSG itself at: https://github.com/barisardic/crsg.
MinIsoClust

With the advent of next-generation sequencing technologies, computational transcriptome assembly of RNA-Seq data has become a critical step in many biological and biomedical studies. The accuracy of these transcriptome assembly methods is hindered by the presence of alternatively spliced transcripts (isoforms). Identifying and quantifying isoforms is also essential in understanding complex biological functions, many of which are often associated with various diseases. However, clustering of isoform sequences using only sequence identities when quality reference genomes are not available is often difficult due to heterogeneous exon composition among isoforms. Clustering of a large number of transcript sequences also requires a scalable technique. In this study, we propose a minwise-hashing based method, MinIsoClust, for fast and accurate clustering of transcript sequences that can be used to identify groups of isoforms. We tested this new method using simulated datasets. The results demonstrated that MinIso-Clust was more accurate than CD-HIT-EST, isONclust, and MM-seqs2/Linclust. MinIsoClust also performed better than isONclust and MMseqs2/Linclust in terms of computational time and space efficiency. The source codes of MinIsoClust is freely available at https://github.com/srbehera/MinIsoClust.
The Improvement Impact Performance of Face Detection Using YOLO Algorithm

Image data augmentation is a way that makes it possible to increase the diversity of available data without actually collecting new data. In this study, researchers have evaluated the application of image manipulation with the Thatcher effect, double illusion, and inversion on the performance of face detection for data augmentation needs where the data obtained has a weakness that is the limited amount of data to create a training model. The purpose of this research is to increase the diversity of the data so that it can make predictions correctly if given other similar datasets. To perform face detection on images, it is done using YOLOv3 then comparing the accuracy results from the dataset after and before adding data augmentation.
Repairing confusion and bias errors for DNN-based image classifiers

Recent works in DNN testing show that DNN based image classifiers are susceptible to confusion and bias errors. A DNN model, even robust trained model can be highly confused between certain pair of objects or highly bias towards some object than others. In this paper, we propose a differentiable distance metric, which is highly correlated with confusion errors. We propose a repairing approach by increasing the distance between two classes during retraining the model to reduce the confusion errors. We evaluate our approaches on both single-label and multi-label classification models and datasets. Our results show that our approach effectively reduce confusion errors with very slight accuracy reduce.
Edge Enhancement in Monocular Depth Prediction

Although many monocular depth prediction methods have achieved very high prediction accuracy, the preference of using high-level features of images makes these methods wrongly predict the depth of edge regions. This shortage does not decrease prediction accuracy seriously but will bring difficulties to subsequent works like three-dimension recognition and semantic segmentation. To enhance the performance of restoring the depth of edge regions, we apply modification on network structure and design a new loss function to strengthen the network's ability to extract, store, and utilize low-level features of images. We test our method on NYU Depth V2 Dataset, and the experiment results show that our method has a better performance on predicting the depth of edge regions than the state-of-the-art method and outperforms most of the current method on prediction accuracy.
A Framework for Understanding the Dynamics of Science: A Case Study on AI

The Science of Science is an emerging field that enables tracking the dynamics of science in the form of birth and death of scientific fields and research trends in them. This study proposes a framework to analyze the content of academic publications to extract research trends and explore their temporal evolution. In this proposed framework, state-of-the-art embedding techniques are reviewed and utilized to consider semantic vectors of publications’ keywords, which have been used to produce semantic-based clusters reflecting research trends or sub-fields. We compare our proposed method with LDA, as a baseline method, to demonstrate the explainability of the clusters, applying them to the field of "Artificial Intelligence" (AI) as a case study.
Clustering Ensemble Selection with Analytic Hierarchy Process

Existing clustering ensemble selection methods adopt internal and external evaluation indexes to measure the quality and diversity of base clusterings. The significance of base clustering is quantified by the average or weighted average of multiple evaluation indexes. However, there exist two limitations in these methods. First, the evaluation of base clusterings in the form of linear combination of multiple indexes lacks the structural analysis and relative comparison between clusterings and measures. Second, the consistency between the final evaluation and the multiple evaluations from different measures cannot be guaranteed. To tackle these problems, we propose a clustering ensemble selection method with Analytic Hierarchy Process (AHPCES). Experimental results validate the effectiveness of the proposed method.
ES2Vec: Earth Science Metadata Keyword Assignment using Domain-Specific Word Embeddings

Earth science metadata keyword assignment is a challenging problem. Dataset curators select appropriate keywords from the Global Change Master Directory (GCMD) set of keywords. The keywords are integral part of search and discovery of these datasets. Hence, selection of keywords are crucial in increasing the discoverability of datasets. Utilizing machine learning techniques, we provide users with automated keyword suggestions as an improved approach to complement manual selection. We trained a machine learning model that leverages the semantic embedding ability of Word2Vec models to process abstracts and suggest relevant keywords. A user interface tool we built to assist data curators in assignment of such keywords is also described.
Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image

Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining unoccluded instances by decoding a deep encoder becomes difficult, due to the translation invariance of convolutional layers and the lack of complexity in the decoder. We therefore propose a multicameral design composed of subtask-specific lightweight decoder and encoder-decoder units, coupled in cascade to encourage subtask-specific feature reuse and enforce a learning path within the decoding process. Furthermore, the state-of-the-art datasets for occlusion-aware instance segmentation contain real images with few instances and occlusions mostly due to objects occluding the background, unlike dense object layouts. We thus also introduce a synthetic dataset of dense homogeneous object layouts, namely Mikado, which extensibly contains more instances and inter-instance occlusions per image than these public datasets. Our extensive experiments on Mikado and public datasets show that ordinal multiscale units within the decoding process prove more effective than state-of-the-art design patterns for capturing position-sensitive representations. We also show that Mikado is plausible with respect to real-world problems, in the sense that it enables the learning of performance-enhancing representations transferable to real images, while drastically reducing the need of hand-made annotations for finetuning. The proposed dataset will be made publicly available.
Tensor-Train Decomposed Synaptic Interconnections for Compact and Scalable Photonic Neural Networks

We propose a compact and scalable photonic neural network architecture based on tensor-train decomposed synaptic interconnections with Mach-Zehnder interferometers as building blocks. At the scale of 1024×1024, our architecture exhibits 1164× fewer MZIs and 10.2× fewer cascaded stages.
Mean Decision Rule Method for Consrtucting Nonlinear Boundaries in Large Binary SVM Problems

In the previous work, we proposed the Mean Decision Rule method for fast approximate solution of the two- class SVM problem in a feature space. This paper generalized the proposed approach to the case of training in a space generated by a kernel function (Kernel-based Mean Decision Rule method), which allows to construct nonlinear boundaries separating objects of two classes. Experiments show that even for large training sets Kernel-based MDR, like the initial MDR method, allows to quickly find a solution that does not differ much from the exact one. Besides it is economical in memory and has a high degree of data parallelism and so it can be effectively implemented using parallel and distributed computing technologies.
Asset-Centric Analysis and Visualisation of Attack Trees

Attack trees are an established concept in threat and risk analysis. They build the basis for numerous frameworks aiming to determine the risk of attack scenarios or to identify critical attacks or attack paths. However, existing frameworks do not provide systematic analyses on the asset-level like the probability of successful or near-successful attacks on specific assets. But these insights are important to enable decision-makers to make more informed decisions. Therefore, a generic approach is presented that extends classical attack tree approaches by asset-specific analyses. For this purpose, the attack steps in the attack trees are annotated with corresponding assets. This allows identifying the attack paths each asset is exposed to. In combination with the standard attack tree parameter "probability of attack success", a set of complementary attack success and protection metrics can be applied on each step of the paths. Furthermore, an integrated visualisation scheme is proposed that illustrates the results in a comprehensible way so that decision-makers can intuitively understand what the metrics indicate. It also includes several features improving usability and scalability. As proof of concept, we have implemented a prototype of our proposed method.
PSM$$^2$$: A Privacy-Preserving Self-sovereign Match-Making Platform

The scale of matchmaking market continues to grow rapidly. In the current fast pace of life, an online matchmaking platform is getting more popular, and users are only required to provide their personal information and preferences to match with others, which is accurate and efficient. However, as data privacy laws such as European Union(EU)’s General Data Protection Regulations(GDPR) aims to give control back to consumers over their personal data, current matchmaking platforms failed to fulfil the data transparency and data processing requirements, which raise privacy concerns by the society. To address this issue, in this paper, we proposed a self-sovereign blockchain-based privacy-preserving matchmaking platform namely PSM $$^2$$ , which enables its users to treat their personal data as a digital asset and trade it according to the matching score with other users. By leveraging smart contract, we carefully designed a trading contract to ensure a fair and transparent trading process. In addition, we build a matching score calculation algorithm, according to which PSM $$^2$$ can determine the price for purchasing someone’s contact information. Finally, we develop a proof of concept prototype on Hyperledger Fabric and conduct several experiments to demonstrate the performance of the matching algorithm and the feasibility of PSM $$^2$$ .
Service Contract Definition Analysis

The integration of new technologies and industry digitalization concerns service interoperability and automation of engineering processes, which involve new and emerging requirements. The description of service contracts and the capability of machines to determine interoperability mismatches become significant. New approaches require a complete and structured definition of service contracts, and the service description can be used to automate the engineering of service translations to enable seamless interoperability.Nevertheless, current interface description languages remain unaffected and do not evolve according to the new specifications. This work presents an analysis of the commonly used description languages, WSDL, WADL, and OpenAPI, and a systematic comparison of them.The languages have been analyzed and compared based on key requirements for the development of new tools such as interface generators and service contract translators. The results of the comparison highlight the language limitations to fully describe the service contract. The misalignment between the available interface description languages and the service contract requirements is outlined to help extend these languages to fully describe the service contract in future improvements.
Tracking of Clinical Documentation Based on the Blockchain Technology—A Polish Case Study

The article presents the concept of application DLT (distributed ledger technologies) for building the electronic clinical documentation tracking system. After a short introduction to block chain issues, and discussion about the attempts of its application on various fields of everyday human life, including healthcare, basic requirements for tracking of clinical documentation system are presented, followed by the proposition of its architecture leveraging the distributed ledger technologies. The paper is concluded with a discussion about the possibilities of running such a system, regarding constraints coming from local legal regulations and general data protection regulation (GDPR), but also economic and social conditions, including ecological ones, which are part of the sustainable development trend.
Network completion via deep metric learning

Abstract Completing networks is often a necessary step when dealing with problems arising from applications in incomplete network data mining. This paper investigates the network completion problem with node attributes. We proposed a new method called DeepMetricNC by exploiting the correlation between node attributes and the underlying network structure. In DeepMetricNC, the correlation is modeled as a nonlinear mapping from node attributes to the probability of edge existence. To obtain the mapping, deep metric learning is applied with batch training and random negative sampling. DeepMetricNC has linear training time complexity and can adapt to large-scale network completion tasks. Experiments of real networks show that DeepMetricNC completes network structures better than other methods, and is more suitable when the portion of the observed part is small.
Dynamical System Identification by Bayesian Inference

Fluid flow systems provide physical expressions of dynamical systems, typically written ẋx x = f (x x x), where x x x is the state vector and f is the system function or model.The identification of the dynamical system f from time-series data {x 1 , ..., x n }, an inverse problem, is a long-held challenge.Historically, this has been examined by linear or nonlinear regression, convolution methods, neural networks or evolutionary computation, but these mostly lie outside the rigorous framework of Bayesian inference.Here we examine the maximum a-posteriori (MAP) Bayesian method for system identification, which is shown to be equivalent to Tikhonov regularization, and in fact provides sound theoretical justifications for the choices of residual and regularization terms.The joint maximum a-posteriori (JMAP) and variational Bayesian approximation (VBA) are demonstrated by comparison to the popular SINDy regularization method, by application to the Rössler dynamical system.
Effects of Font Size, Line Spacing, and Font Style on Legibility of Chinese Characters on Consumer-Based Virtual Reality Displays

This study investigated the effects of font size, line spacing, and font style on legibility of Chinese characters on consumer-based virtual reality devices. We employed a three-factor within-subjects design experiment. The three independent variables were: font size, line spacing, and font style. There were three font sizes: 10pt, 12pt, and 14pt, all measured in unit of points. Also, three levels of line spacing were employed in this study: single line, 1.5 lines, and double lines. Two font styles were selected: Hei and Song style. There were 18 (3 × 3 × 2) experimental conditions in total. Thirty-two subjects participated in the experiment and completed the character searching tasks in the pseudo-texts environment under virtual reality scenario. Both objective and subjective measurements were employed to evaluate the legibility of reading Chinese fonts on consumer-based virtual reality displays. Data was collected on search time, accuracy, and subjective preferences of searching. Hopefully the results can provide some recommendations to designing legible and user-friendly Chinese texts on consumer-based virtual reality devices.
A Sign Language Interaction System Based on Pneumatic Soft Hand

The interaction with the deaf-mute is an important part of human-computer interaction. In response to the application requirements of human-computer interaction technology in the deaf-mute field, this paper proposes a kind of human hand movement system that can control the soft hand to interact with human according to the sign language action. The system proposed a way to simplify the definition of human hand movements and then implemented the movement detection of the human hand, the creation of a human hand shape, and also the mechanical structure capable of sign language actions. Through various experiments, the tracking ability and interaction ability of the soft hand to the human hand movement were demonstrated, and the information of the human hand movement was collected and analyzed. The system has extensive engineering potentials in the application of sign language interactive terminals and other related aspects.