Reviewerly/exHarmony huggingface dataset:
Interpretable Machine Learning Models for Clinical Decision-Making in a High-Need, Value-Based Primary Care Setting

SummaryWhile health care organizations are increasingly interested in using artificial intelligence (AI), there is a significant lack of literature on the real-world application of a risk stratification AI tool in primary care. Oak Street Health, a network of more than 80 primary care centers in medically underserved communities, successfully implemented a machine learning–based risk stratification tool across their organization that outperformed prior backward-looking approaches in identifying high-risk patients. The data science team collaborated with an interdisciplinary set of stakeholders to test, iterate, and implement the tool into clinical practice. Early feedback from Oak Street Health's primary care providers (physicians and nurse practitioners) and nonproviders (social workers) suggests that the display of top risk factors based on model predictions created a broadly interpretable and actionable risk stratification tool in caring for the highest-risk patients.
Sequential Instance Refinement for Cross-Domain Object Detection in Images

Cross-domain object detection in images has attracted increasing attention in the past few years, which aims at adapting the detection model learned from existing labeled images (source domain) to newly collected unlabeled ones (target domain). Existing methods usually deal with the cross-domain object detection problem through direct feature alignment between the source and target domains at the image level, the instance level ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , region proposals) or both. However, we have observed that directly aligning features of all object instances from the two domains often results in the problem of negative transfer, due to the existence of (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">outlier target instances</i> that contain confusing objects not belonging to any category of the source domain and thus are hard to be captured by detectors and (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low-relevance source instances</i> that are considerably statistically different from target instances although their contained objects are from the same category. With this in mind, we propose a reinforcement learning based method, coined as sequential instance refinement, where two agents are learned to progressively refine both source and target instances by taking sequential actions to remove both outlier target instances and low-relevance source instances step by step. Extensive experiments on several benchmark datasets demonstrate the superior performance of our method over existing state-of-the-art baselines for cross-domain object detection.
Machine Learning Techniques for Recommender Systems – A Comparative Case Analysis

Abstract Recommender System (RS) is one of the most popular applications of Artificial Intelligence which attracted researchers all around the world. Many machine learning algorithms are used to develop RSs. Choosing the best machine learning algorithm to provide users with a product or service is the most challenging task in the area of RSs. Now we are witnessing a paradigm shift in the purchase habits of people from in-shop to online resulting in the availability of online information exponentially growing every day. The ever-increasing online information and the number of online users create new avenues in RS. In an online shopping scenario, these systems must be able to recommend relevant items to the users. The RSs have to deal with the huge amount of information by filtering the relevant information based on the analysis made on the inputs made by the users during their online sessions. These systems can recommend appropriate items to users based on their interest and previous preference which can lead to increased sales. The three major techniques used to build a RS are content-based, collaborative based and hybrid-based. This paper presents the various applications of RSs and makes a detailed comparative study of different machine learning approaches used. The methodologies used for identifying research articles for analysis, the merits and demerits of different techniques in RSs and domain-specific applications of these techniques are well explained here with scientific review analysis.
High-Capacity Reversible Data Hiding in Encrypted Images Based on Hierarchical Quad-Tree Coding and Multi-MSB Prediction

Nowadays, more and more researchers are interested in reversible data hiding in encrypted images (RDHEI), which can be applied in privacy protection and cloud storage. In this paper, a new RDHEI method on the basis of hierarchical quad-tree coding and multi-MSB (most significant bit) prediction is proposed. The content owner performs pixel prediction to obtain a prediction error image and explores the maximum embedding capacity of the prediction error image by hierarchical quad-tree coding before image encryption. According to the marked bits of vacated room capacity, the data hider can embed additional data into the room-vacated image without knowing the content of original image. Through the data hiding key and the encryption key, the legal receiver is able to conduct data extraction and image recovery separately. Experimental results show that the average embedding rates of the proposed method can separately reach 3.504 bpp (bits per pixel), 3.394 bpp, and 2.746 bpp on three well-known databases, BOSSBase, BOWS-2, and UCID, which are higher than some state-of-the-art methods.
Path planning of surgical needle: A new adaptive intelligent particle swarm optimization method

Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, arteries, bones and so on in the puncture path, it is a challenging work to design the optimal path for surgical needle. In this paper, we propose a new path planning method based on the adaptive intelligent particle swarm optimization (PSO) algorithm with parameter adjustment mechanism. First, force and motion analysis are carried out on the bevel-tip flexible needle after piercing into human tissues, the motion model of the needle and the spatial transformation model of puncture route in three-dimensional space are obtained, respectively. Then, a multi-objective function is established, which includes puncture path length function, puncture error function and collision detection function. Finally, the optimal puncture path is obtained based on the adaptive intelligent PSO algorithm. The simulation results show that the newly proposed path planning method has higher efficiency, better adaptability to complex environments and higher accuracy than other path planning methods in literature.
Should Bitcoin Be Classified as Money?

Abstract The advent of virtual currencies such as bitcoin raises a pressing question for lawmakers, regulators, and judges: should bitcoin and other virtual currencies be classified as money or currency for legal and regulatory purposes? I examine two different approaches to answering this question—a descriptive approach and a normative approach. The descriptive approach says that bitcoin and other virtual currencies should be classified as money or currency just in case they really are money or currency, whereas the normative approach says that this question of classification should be answered on the basis of substantive normative considerations. I argue against the descriptive approach and in favor of the normative approach.
Model checking agent-based communities against uncertain group commitments and knowledge

In recent years, the use of Multi-Agent Systems (MASs) to solve complex problems has grown rapidly. Social communicative commitments have been widely employed in such systems as a means of communication allowing heterogeneous agents to cooperate. However, to prevent undesirable outcomes, communicative commitments and their interactions with agents' knowledge need to be verified. This paper aims at verifying MASs where agents have knowledge and communicate through manipulating uncertain social commitments, especially when the scope of commitments moves beyond the common agent-to-agent scheme. We introduce a model checking method for verifying those systems and capitalize on the interaction between not only individual but also group communicative commitments and knowledge in the presence of uncertainty. System's properties are expressed using the Probabilistic Computation Tree Logic of Knowledge and Commitment (PCTLkc+). In the proposed approach, model checking PCTLkc+ is reduced to model checking the probabilistic branching-time logic PCTL. This is achieved by transforming PCTLkc+ model to a Markov Decision Process (MDP), and reducing PCTLkc+ formulae into PCTL formulae compatible with PRISM, a reference model checking tool for probabilistic temporal systems. Thereafter, we provide the soundness and completeness proofs of the reduction technique, and compute its time complexity. The effectiveness of the proposed approach is evaluated by implementing it on top of PRISM using two concrete applications, namely Online Shopping System from the business domain, and Insurance Claim Processing from the industrial domain. The obtained results of the two case studies underscore the scientific value of our proposed framework and confirm that verifying commitment-based probabilistic epistemic MASs has become attainable by utilizing this approach. The presented work outperforms existing proposals because it considers the problem of modeling and verifying MASs where group social commitments are interacting with participating agents' knowledge in the presence of uncertainty, which has not been addressed yet in the literature.
Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels

In semi-supervised learning for classification, i t is assumed that every ground truth class of data is present in the small labelled dataset. In many real-world sparsely-labelled datasets, it is possible that not all ground-truth classes are captured in the labelled dataset: a biased data collection process could result in some classes of data to be found only in the unlabelled dataset. We call this regime `semi-unsupervised learning', an extreme case of semi-supervised learning, where some classes have no labelled exemplars. First, we outline the pitfalls associated with trying to apply deep generative model (DGM)-based semi-supervised learning algorithms to datasets of this type. We then show how a combination of clustering and semi-supervised learning, using DGMs, can be brought to bear on this problem. We study several different datasets, showing how one can still learn effectively when half of the ground truth classes are entirely unlabelled and the other half are sparsely labelled.
A new structure of decision tree based on oriented edges gradient map for circles detection and the analysis of nano-particles

In this paper, we propose a new approach to detect circles and nano-particles based on an oriented-edges gradient map and a decision tree. The decision tree is calculated from geometric constraints based on particular right triangles inscribed in a circle. Use of the proposed accumulator and dynamic storage matrix radii shows the robustness of our algorithm in terms of results and execution time. This robustness can also be enhanced in the event of prior knowledge. Indeed, we can enable or disable intermediate nodes or a part of nodes of the proposed decision tree to strengthen both the detection results and the execution time of the algorithm. Our approach makes it possible to detect circles and analyse the distribution of the nano-particles which is evaluated using four databases which include TEM, synthetic, real and complex images.
Conversational Futures: Emancipating Conversational Interactions for Futures Worth Wanting

We present a vision for conversational user interfaces (CUIs) as probes for speculating with, rather than as objects to speculate about. Popular CUIs, e.g., Alexa, are changing the way we converse, narrate, and imagine the world(s) to come. Yet, current conversational interactions normatively may promote non-desirable ends, delivering a restricted range of request-response interactions with sexist and digital colonialist tendencies. Our critical design approach envisions alternatives by considering how future voices can reside in CUIs as enabling probes. We present novel explorations that illustrate the potential of CUIs as critical design material, by critiquing present norms and conversing with imaginary species. As micro-level interventions, we show that conversations with diverse futures through CUIs can persuade us to critically shape our discourse on macro-scale concerns of the present, e.g., sustainability. We reflect on how conversational interactions with pluralistic, imagined futures can contribute to how being human stands to change.
Use of augmented reality-enabled prototyping of cyber-physical systems for improving cyber-security education

Abstract The use of augmented reality-enabled scenarios in cybersecurity teaching is proposed in the article to respond to new requirements for the rapid adoption of new technologies and profound knowledge of cybersecurity issues by professionals. Implementation of project-type activities based on real cybersecurity issues in application fields of cyber-physical systems is suggested to improve the competence forming. A use-case of agricultural cyber-physical system of systems is discussed as a viable example of augmented reality-enabled prototyping of cybersecurity risk-aware architecture. The necessary steps are analysis of general and business-specific tasks on cybersecurity, creation of a list of competencies, formalized in educational standards and curricula, development of gaming scenarios for the formation of hard and soft skills, development of the scenario management system for AR interfaces. The system using AR tools can be easily adapted to different cybersecurity training activities. Industrial cyber-physical systems may be vulnerable due to insecure wireless connectivity, lack of encryption, inadequate access policy. The project-based learning complex is focused on the implementation of a data acquisition, storage and processing platform for new sensor networks and instruments. Representing all the diverse information on different layers will be greatly improved by use of the developed holographic projection AR tools.
An autonomous model for self‐optimizing virtual machine selection by learning automata in cloud environment

Abstract In recent years, cloud computing has become more popular because of advancements in virtualization technology. By increasing the number of servers in cloud computing environment, cloud data centers have expanded and consumed much energy. Virtual machine consolidation is a solution for energy management in cloud environment. On the other hand, by increasing resource utilization in virtual machine consolidation, service level agreement assurance is difficult to obtain. Two main challenges in virtual machine consolidation are timely detection of overloaded servers and proper immigrant virtual machine selection from detected servers. In this paper, a new model is proposed based on MAPE‐k loop for autonomous virtual machine selection. The presented model uses a proposed ensemble prediction algorithm in the analysis phase. Also, in the planning phase, a new multi‐heuristics algorithm with flexible weights using learning automata is proposed. The effectiveness of the proposed model is evaluated by CloudSim simulator under real workload as compared with well‐known algorithms in this domain. The experimental results indicate that, the proposed approach has averagely improved the balance between service level agreement violations, energy and migration counts by 47.39% compared to other methods.
Privacy-Preserving Reverse Nearest Neighbor Query Over Encrypted Spatial Data

With the advent of cloud computing, it has become more and more popular to outsource various services to the cloud for releasing the burden of local data storage and maintenance. However, it may cause serious privacy problems because the cloud may be untrusted. In this article, we study the privacy-preserving reverse nearest neighbor (PPRNN) query over encrypted spatial data. First, we introduce the concept of reference-locked order-preserving encryption (RL-OPE) with its construction and security proof, which reveals less information than traditional order-preserving encryption (OPE). Then, we present a novel PPRNN scheme in static setting based on structured encryption (SE) and the proposed RL-OPE, called sPPRNN. After that, we design a generic method that extends a PPRNN scheme in static setting to the counterpart in dynamic setting, called dPPRNN. Furthermore, we present a thorough privacy analysis of our proposal. Finally, we demonstrate its efficiency and effectiveness for practical deployment through extensive experiments.
An efficient reasoning method on logic programming using partial evaluation in vector spaces

Abstract In this paper, we introduce methods of encoding propositional logic programs in vector spaces. Interpretations are represented by vectors and programs are represented by matrices. The least model of a definite program is computed by multiplying an interpretation vector and a program matrix. To optimize computation in vector spaces, we provide a method of partial evaluation of programs using linear algebra. Partial evaluation is done by unfolding rules in a program, and it is realized in a vector space by multiplying program matrices. We perform experiments using artificial data and real data, and show that partial evaluation has the potential for realizing efficient computation of huge scale of programs in vector spaces.
Real-Time Detection of Fake-Shops through Machine Learning

E-commerce fraud has been surging at an alarming rate, reaching an all-time high in Austria in 2019, with an increase of 32.3% since the previous year. Illegitimate subscription services, counterfeit goods and fake-shops cause consistent and enormous financial damage. Manual preventive measures fall short due to the sheer number and increasing volume of cases reported for evaluation. Reducing the window of opportunity and increasing the efficiency in flagging fake-shops is therefore key. This paper presents an approach to classify fraudulent online shops solely on the basis of the similarity of their source code structure using machine learning processes. The trained models show an Accuracy of up to 97% and a very high degree of certainty in classifying fraudulent e-commerce, with 61% of all absolute prediction values being nearly identical to the ones made by human experts. Additionally, an aggregated model was developed that plays an important factor in balancing the overall quality of predictions. The open source software components developed include the Fake-Shop Detection API and Middleware, which enables a risk assessment of any website based on the trained models. By using the developed models, the system was able to issue warnings for 48% of fake-shops at the highest prediction confidence level, meaning that these could have immediately been blacklisted by consumer protection agencies, without a single false-classification and an error rate of zero.
Uncorrelated feature encoding for faster image style transfer

Recent image style transfer methods use a pre-trained convolutional neural network as their feature encoder. However, the pre-trained network is not optimal for image style transfer but rather for image classification. Furthermore, they require time-consuming feature alignment to consider the existing correlation among channels of the encoded feature map. In this paper, we propose an end-to-end learning method that optimizes both encoder and decoder networks for style transfer task and relieves the computational complexity of the existing correlation-aware feature alignment. First, we performed end-to-end learning that updates not only decoder but also encoder parameters for the task of image style transfer in the network training phase. Second, in addition to the previous style and content losses, we use uncorrelation loss, i.e., the total correlation coefficient among responses of encoder channels. Our uncorrelation loss allows the encoder network to generate a feature map of channels without correlation. Subsequently, our method results in faster forward processing with only a light-weighted transformer of correlation-unaware feature alignment. Moreover, our method drastically reduced the channel redundancy of the encoded feature during the network training process. This provides us a possibility to perform channel elimination with negligible degradation in generated style quality. Our method is applicable to multiple scaled style transfer by using the cascade network scheme and allows a user to control style strength through the usage of a content-style trade-off parameter.
Veteran teachers and digital technologies: myths, beliefs and professional development

According to the Teaching and Learning International Survey (2019), the average age for a teacher, across OECD countries, is 44 years old, and the teacher workforce has aged in a number of countries, over the past 5 to 10 years. Given the demands expected from the contemporary teaching profession, the study of possible implications of ageing on the use of digital technologies in education is even more pertinent and relevant. This study aims to analyse veteran teachers’ perceptions about digital technologies and outline possible pathways for their professional development. Data collected through literature review and content analysis of veteran teachers’ perceptions, expressed in a continuing education training programme, show there are positive and negative thoughts and dilemmas related to the role of digital technology in education, and that there are myths regarding veteran teachers’ digital skills, competences and motivation to change practices, as well as misconceptions about the relationship between technologies and educational reforms. The results also indicate possible pathways to successful training practice with this target group.
A Relational Adaptive Neural Model for Joint Entity and Relation Extraction

Relation extraction is a popular subtask in natural language processing (NLP). In the task of entity relation joint extraction, overlapping entities and multi-type relation extraction in overlapping triplets remain a challenging problem. The classification of relations by sharing the same probability space will ignore the correlation information among multiple relations. A relational-adaptive entity relation joint extraction model based on multi-head self-attention and densely connected graph convolution network (which is called MA-DCGCN) is proposed in the paper. In the model, the multi-head attention mechanism is specifically used to assign weights to multiple relation types among entities so as to ensure that the probability space of multiple relation is not mutually exclusive. This mechanism also predicts the strength of the relationship between various relationship types and entity pairs flexibly. The structure information of deeper level in the text graph is extracted by the densely connected graph convolution network, and the interaction information of entity relation is captured. To demonstrate the superior performance of our model, we conducted a variety of experiments on two widely used public datasets, NYT and WebNLG. Extensive results show that our model achieves state-of-the-art performance. Especially, the detection effect of overlapping triplets is significantly improved compared with the several existing mainstream methods.