Reviewerly/exHarmony huggingface dataset:
A novel semi-supervised ensemble algorithm using a performance-based selection metric to non-stationary data streams

In this article, we consider the semi-supervised data stream classification problems. Most of the semi-supervised learning algorithms suffer from a proper selection metric to select from the newly-labeled data points through the training procedure. These approaches mainly employ the probability estimation of the underlying base learners to their predictions as a selection metric, which is not optimal in many cases. Handling different kinds of concept drifts is another issue in data streams. Considering these issues, we propose a novel Semi-Supervised Ensemble algorithm using a Performance-Based Selection metric to data streams, named SSE-PBS. The proposed selection metric is based on a pseudo-accuracy and energy regularization factor. We show that SSE-PBS improves classification performance and handles different kinds of concept drifts. The proposed algorithm can also employ any kind of incremental base learners. In the experiments, we report the results of two base learners on synthetic and real-world datasets. The experiments show that SSE-PBS significantly improves the classification performance of the used underlying base learners. Furthermore, we compare the results to the state-of-the-art supervised and semi-supervised approaches in data streams. The results further show that SSE-PBS outperforms the other methods when there is a small portion of labeled instances.
Nonparametric machine learning for precision medicine with longitudinal clinical trials and Bayesian additive regression trees with mixed models

Precision medicine is an active area of research that could offer an analytic paradigm shift for clinical trials and the subsequent treatment decisions based on them. Clinical trials are typically analyzed with the intent of discovering beneficial treatments if the same treatment is applied to the entire population under study. But, such a treatment strategy could be suboptimal if subsets of the population exhibit varying treatment effects. Identifying subsets of the population experiencing differential treatment effect and forming individualized treatment rules is a task well-suited to modern machine learning methods such as tree-based ensemble predictive models. Specifically, Bayesian additive regression trees (BART) has shown promise in this regard because of its exceptional performance in out-of-sample prediction. Due to the unique inferential needs of precision medicine for clinical trials, we have proposed the BART extensions explicated here. We incorporate random effects for longitudinal repeated measures and subject clustering within medical centers. The addition of a novel interaction detection prior to identify treatment heterogeneity among clinical trial patients and its association with patient characteristics. These extensions are unified under a framework that we call mixedBART. Simulation studies and applications of precision medicine based on real randomized clinical trials data examples are presented.
Role model of model-based systems engineering application

Abstract The transition to model-based systems engineering (MBSE) is essential to meet the demands of increasing system complexity, productivity, quality and shorter design cycles. Necessary perspectives, associated roles and competencies for the application of MBSE are not sufficiently defined. The paper at hand presents a role model for the practical application of MBSE based on three major steps: (1) identification, (2) categorization and (3) association of stakeholders, which implement and apply MBSE within an enterprise. The use of the role model is demonstrated and evaluated in a test case based on personas within a fictional scenario which illustrates the implementation of MBSE. The roles, which are adapted to today’s conditions, serve as a practical guide comprising all basic conditions that have to be considered when applying MBSE. Practitioners can easily tailor the illustrated results to their own use case.
Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve

Abstract Named entity recognition systems achieve remarkable performance on domains such as English news. It is natural to ask: What are these models actually learning to achieve this? Are they merely memorizing the names themselves? Or are they capable of interpreting the text and inferring the correct entity type from the linguistic context? We examine these questions by contrasting the performance of several variants of architectures for named entity recognition, with some provided only representations of the context as features. We experiment with GloVe-based BiLSTM-CRF as well as BERT. We find that context does influence predictions, but the main factor driving high performance is learning the named tokens themselves. Furthermore, we find that BERT is not always better at recognizing predictive contexts compared to a BiLSTM-CRF model. We enlist human annotators to evaluate the feasibility of inferring entity types from context alone and find that humans are also mostly unable to infer entity types for the majority of examples on which the context-only system made errors. However, there is room for improvement: A system should be able to recognize any named entity in a predictive context correctly and our experiments indicate that current systems may be improved by such capability. Our human study also revealed that systems and humans do not always learn the same contextual clues, and context-only systems are sometimes correct even when humans fail to recognize the entity type from the context. Finally, we find that one issue contributing to model errors is the use of “entangled” representations that encode both contextual and local token information into a single vector, which can obscure clues. Our results suggest that designing models that explicitly operate over representations of local inputs and context, respectively, may in some cases improve performance. In light of these and related findings, we highlight directions for future work.
A generative framework for the study of delusions

Despite the ubiquity of delusional information processing in psychopathology and everyday life, formal characterizations of such inferences are lacking. In this article, we propose a generative framework that entails a computational mechanism which, when implemented in a virtual agent and given new information, generates belief updates (i.e., inferences about the hidden causes of the information) that resemble those seen in individuals with delusions. We introduce a particular form of Dirichlet process mixture model with a sampling-based Bayesian inference algorithm. This procedure, depending on the setting of a single parameter, preferentially generates highly precise (i.e. over-fitting) explanations, which are compartmentalized and thus can co-exist despite being inconsistent with each other. Especially in ambiguous situations, this can provide the seed for delusional ideation. Further, we show by simulation how the excessive generation of such over-precise explanations leads to new information being integrated in a way that does not lead to a revision of established beliefs. In all configurations, whether delusional or not, the inference generated by our algorithm corresponds to Bayesian inference. Furthermore, the algorithm is fully compatible with hierarchical predictive coding. By virtue of these properties, the proposed model provides a basis for the empirical study and a step toward the characterization of the aberrant inferential processes underlying delusions.
An efficient three-dimensional face recognition system based random forest and geodesic curves

3D Face recognition is being extensively recognized as a biometric performance refers to its non-intrusive environment. In spite of large research on 2-D face recognition, it suffers from low recognition rate due to illumination variations, pose changes, poor image quality, occlusions and facial expression variations, while 3D face models are insensitive to all these conditions. In this paper, we present an efficient 3D face recognition approach based on Geodesic Distance (GD) of Riemannian geometry and Random Forest (RF), named GD-FM+RF. Therefore, to compute the geodesic distance between the specified pairs of the points of 3D faces, we applied Fast Marching (FM) algorithm, in order to solve the Eikonal equation. Then, these extracted features presented by the geodesic facial curves are used by Principal Component Analysis (PCA) algorithm to analyze class separability. Afterwards, these features were utilized as input of RF classifier. In order to test our approach and assess its effectiveness, simulated series of tests were implemented on 3D SHape REtrieval Contest 2008 database (SHREC'08). As a result, our proposed approach enhances the recognition rate and achieves promising results compared to state of the art methods, getting 99.11% in terms of recognition rate.
A graph-based semi-supervised reject inference framework considering imbalanced data distribution for consumer credit scoring

Credit scoring has been attracting increasing attention in the Chinese consumer financial industry. Traditional approaches are easily influenced by sample selection bias because they use accepted applicant samples only, while the applicant population also includes rejected applicants. Reject inference is a technique to infer good/bad labels for rejected applicants, which can overcome biases in credit scoring. However, previously proposed reject inference methods usually ignore the imbalanced distribution in accepted data, which means that good applicants are much more than bad ones in most practical consumer loan applications. Both the neglect of rejected data and the imbalanced distribution in accepted data weaken the performance of current credit scoring models. In this paper, we propose a novel reject inference framework that takes into account the imbalanced data distribution for consumer credit scoring. First, we use an advanced graph-based semi-supervised learning algorithm to solve the reject inference problem, which is called label spreading. Second, faced with an imbalanced distribution of good and bad samples in accepted applicants, we conduct imbalanced learning using a modified Synthetic Minority Over-sampling Technique before reject inference. Then, six binary classifiers are studied in our proposed framework for credit scoring modeling. Finally, we present the results of four exact experiments as well as online A/B tests for performance evaluation using data provided by a leading Chinese fintech company. Empirical results indicate that the proposed framework performs better than traditional scoring models across different evaluation metrics, representing a progressive method that promotes credit scoring research as well as improving fintech practices.
On-Device Learning Systems for Edge Intelligence: A Software and Hardware Synergy Perspective

Modern machine learning (ML) applications are often deployed in the cloud environment to exploit the computational power of clusters. However, this in-cloud computing scheme cannot satisfy the demands of emerging edge intelligence scenarios, including providing personalized models, protecting user privacy, adapting to real-time tasks, and saving resource cost. In order to conquer the limitations of conventional in-cloud computing, there comes the rise of on-device learning, which makes the end-to-end ML procedure totally on user devices, without unnecessary involvement of the cloud. In spite of the promising advantages of on-device learning, implementing a high-performance on-device learning system still faces with many severe challenges, such as insufficient user training data, backward propagation (BP) blocking, and limited peak processing speed. Observing the substantial improvement space in the implementation and acceleration of on-device learning systems, we intend to present a comprehensive analysis of the latest research progress and point out potential optimization directions from the system perspective. This survey presents a software and hardware synergy of on-device learning techniques, covering the scope of model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration. We hope this survey could bring fruitful discussions and inspire the researchers to further promote the field of edge intelligence.
Multiple criteria analysis of the popularity and growth of research and practice of visual analytics, and a forecast of the future trajectory

Abstract This study employs Google trends and data from the web of science to evaluate the popularity, growth, and impacts of visual analytics (VA) as a research field and data science technique. The paper undertakes quantitative analyses and visualization of the temporal trends, from VA's emergence in 2000 to the end of 2019. The trend analysis helps to forecast future growth in the research and practice of VA. The study highlights four outcomes. First, there is a robust direct relationship among the variables, including VA's growth on the Google trends, the scientific literature production (SLP), and usage of the published documents. Second, the SLP's growth pattern highlights VA's popularity as an emerging field with an overall annual increase of 17.4%. The high citation counts of the published scholarship indicate a significant impact and a continuous growth of the VA field. Third, VA contributes to diverse disciplines other than computer science and information systems, from business and economics to engineering, healthcare, biomedical and chemical sciences, and arts and humanities. VA helps researchers and practitioners in multidisciplinary fields analyze multidimensional data, enhance data visualization, knowledge discovery, generating insights, and make informed decisions. On the reverse, other disciplines contribute to propelling VA's popularity through research productivity, usage, and citation impacts. Finally, a trend analysis predicts sustained future growth of VA technology in research and practice to dissect and sensemaking of the increasingly massive and complex data structures, which is now the norm in many fields.
A Novel Revocable and Identity-Based Conditional Proxy Re-Encryption Scheme With Ciphertext Evolution for Secure Cloud Data Sharing

Proxy re-encryption (PRE), with the unique ciphertext transformation ability, enables various ciphertext authorization applications to be implemented efficiently.However, most existing PRE schemes mainly focus on access authorization while ignoring the situation where the key needs to be changed and the ciphertext needs to be evolved, making the scheme's practicability and security inadequate.Moreover, the few schemes that simultaneously combine ciphertext authorization, key update, and ciphertext evolution are not satisfactory in terms of security.For solving this problem, based on Xiong et al.'s scheme, this paper proposes an improved revocable and identity-based conditional proxy re-encryption scheme with ciphertext evolution (RIB-CPRE-CE) for secure and efficient cloud data sharing.The proposed scheme inherits the characteristics of multi-use, constant ciphertext length, fine-grained authorization, collisionresistance security, and chosen ciphertext attack (CCA) security from the original method.Also, it supports updating ciphertext to adapt to the new key after changing the identity (key) or achieves authorization revocation by evolving ciphertext.Two new algorithms, URKeyGen and UpReEnc, have been integrated into the original delegation scheme to support ciphertext evolution.The formal definition, security model, concrete construction, and security analysis of RIB-CPRE-CE have been presented.The comparison and analysis show that the proposed scheme is practical and secure.Although it adds a ciphertext evolution function for supporting key update and delegation revocation, its efficiency and security are not reduced.The proposed scheme can also be used in other access authorization systems that need to change the key or revoke the authorization.It has certain practicability and security.
WITHDRAWN: A survey on applications and security issues of blockchain technology in business sectors

A measured structure to hold all kinds of valuable data in a way which is impossible to forge is called a blockchain. It is fundamentally some blocks connected as a chain which contains evidence of information. This blockchain is initially used to time-stamp the documents to get rid of backdating. The most important thing is once the data is recorded can't be changed further. Each block consists of a hash, information and a hash of the block which is available previously. The data which is stored inside the block depends on the type of blockchain. A Hash distinguishes a block and every one of its substance and which is extraordinary consistently like an iris or unique finger impression. When the block is made, then the Hash is determined. Hash will change if something is changed in the block. Along these lines, hashes are utilized to identify the adjustments in the squares. The chain of the block is created with the previous block's Hash. The principal block is known as the beginning block, which doesn't hold the location of the past block. Verification of-work is to the uncommon method in the blockchain, which is utilized to moderate the gatecrashers who are changing the messages and hashes. The main aim of this chapter is to introduce and present the concept of blockchain, its current applications in the business sector and the various threats and security issues of blockchain technology. Blockchain is emerging technology which promises uncontrollable trust issues by allowing secure and authenticated system for many fields.
Unlocking the payment experience: Future imaginaries in the case of digital payments

This article explores sociotechnical imaginaries of digital payments. Drawing on a decade of reports from industry consultants, the analysis of stories on digital payments identifies three sociotechnical imaginaries that shape the banking and payment industry: data monetization, the growth of digital payments, and the payment experience. The article argues that these imaginaries have contributed to the banking industry’s move toward becoming payment platforms, which restructure financial services based on a “re-personalization of money.” In effect, digital payments play a central part in the current economic transformation, led and promoted by global tech giants, that builds on the tracking, production, categorization, and classification of digital data. The article zooms in on recent and current expectations and imaginaries in the banking industry that, in the short run, shape economic decisions, while, in the long run, are set out to change how people interact, and how they are tracked, scored, and categorized.
Reconfiguration Strategies with Composite Data Physicalizations

Composite data physicalizations allow for the physical reconfiguration of data points, creating new opportunities for interaction and engagement. However, there is a lack of understanding of people’s strategies and behaviors when directly manipulating physical data objects. In this paper, we systematically characterize different reconfiguration strategies using six exemplar physicalizations. We asked 20 participants to reorganize these exemplars with two levels of restriction: changing a single data object versus changing multiple data objects. Our findings show that there were two main reconfiguration strategies used: changes in proximity and changes in atomic orientation. We further characterize these using concrete examples of participant actions in relation to the structure of the physicalizations. We contribute an overview of reconfiguration strategies, which informs the design of future manually reconfigurable and dynamic composite physicalizations.
Participatory design to create a VR therapy for psychosis

This paper describes how participatory design was employed in the design of an automated Virtual Reality (VR) psychological therapy (gameChange), putting people with lived experience of psychosis at the heart of the process. Solutions to complex challenges invariably need to include the expertise and ideas of specialists from a broad variety of disciplines and experiences. The design of gameChange relied on the insights of clinical psychologists, programmers, animators, designers, product managers, producers, writers, researchers, 3 D artists, mental health advocates, and people with lived experience of psychosis. This involved a considerable diversity of working cultures, professional disciplines, and vocabulary. A transdisciplinary, participatory design process was established during the project. It allowed for rapid iteration, meaningful input from people with lived experience of psychosis, and delivered a VR psychological therapy with robust cognitive therapeutic principles. The structures put in place to support the different disciplines working together on the design, particularly people with lived experience of psychosis, are detailed in this paper, with examples of how decisions were made and their outcomes. The clinical effectiveness of the gameChange VR therapy is now being tested in a randomized controlled trial with several hundred patients with psychosis. https://gamechangevr.com/intro_video/
Wireless Fingerprinting via Deep Learning: The Impact of Confounding Factors

Can we distinguish between two wireless transmitters sending exactly the same message, using the same protocol? The opportunity for doing so arises due to subtle nonlinear variations across transmitters, even those made by the same manufacturer. Since these effects are difficult to model explicitly, we investigate learning device fingerprints using complex-valued deep neural networks (DNNs) that take as input the complex baseband signal at the receiver. We ask whether such fingerprints can be made robust to distribution shifts across time and locations due to clock drift and variations in the wireless channel. In this paper, we point out that, unless proactively discouraged from doing so, DNNs learn these strong confounding features rather than the nonlinear device-specific characteristics that we seek to learn. We propose and evaluate strategies, based on augmentation and estimation, to promote generalization across realizations of these confounding factors, using WiFi data. We conclude that, while DNN training has the advantage of not requiring explicit signal models, significant modeling insights are required to focus the learning on the effects we wish to capture.
Conditional Text Generation for Harmonious Human-Machine Interaction

In recent years, with the development of deep learning, text-generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text-generation technology, that is, the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional Text Generation (CTG) has thus become a research hotspot. As a promising research field, we find that much attention has been paid to exploring it. Therefore, we aim to give a comprehensive review of the new research trends of CTG. We first summarize several key techniques and illustrate the technical evolution route in the field of neural text generation, based on the concept model of CTG. We further make an investigation of existing CTG fields and propose several general learning models for CTG. Finally, we discuss the open issues and promising research directions of CTG.