Reviewerly/exHarmony huggingface dataset:
Time Series Modeling with Fuzzy Cognitive Maps based on Partitioning Strategies

The change of amplitude and frequency result in a variety of variation modality of time series in the universe. It is difficult to describe the variation features of time series exactly relying solely on a single simulating model. To overcome this limitation, a new prediction model using fuzzy cognitive maps is proposed based on partitioning strategies. Initially, fuzzy c-mean clustering is adopted to partition time series into several sub-sequences. Consequently, each partition has its corresponding sequences. Subsequently these sub-sequences are used to constructed fuzzy cognitive maps models respectively. Finally, the fuzzy cognitive maps models are merged by fuzzy rules. The constructed model is not only performing well in numerical prediction but also has interpretability. The experimental results show that the model based on partition strategy is superior to the single.
Effective sample size, dimensionality, and generalization in covariate shift adaptation

In supervised learning, training and test datasets are often sampled from distinct distributions. Domain adaptation techniques are thus required. Covariate shift adaptation yields good generalization performance when domains differ only by the marginal distribution of features. Covariate shift adaptation is usually implemented using importance weighting, which may fail, according to common wisdom, due to small effective sample sizes (ESS). Previous research argues this scenario is more common in high-dimensional settings. However, how effective sample size, dimensionality, and model performance/generalization are formally related in supervised learning, considering the context of covariate shift adaptation, is still somewhat obscure in the literature. Thus, a main challenge is presenting a unified theory connecting those points. Hence, in this paper, we focus on building a unified view connecting the ESS, data dimensionality, and generalization in the context of covariate shift adaptation. Moreover, we also demonstrate how dimensionality reduction or feature selection can increase the ESS and argue that our results support dimensionality reduction before covariate shift adaptation as a good practice.
Modeling and Evaluating Personas with Software Explainability Requirements

This work focuses on the context of software explainability, which is the production of software capable of explaining to users the dynamics that govern its internal functioning. User models that include information about their requirements and their perceptions of explainability are fundamental when building software with such capability. This study investigates the process of creating personas that include information about users' explainability perceptions and needs. The proposed approach is based on data collection with questionnaires, modeling of empathy maps, grouping the maps, generating personas from them and evaluation employing the Persona Perception Scale method. In an empirical study, personas are created from 61 users' response data to a questionnaire. The generated personas are evaluated by 60 users and 38 designers considering attributes of the Persona Perception Scale method. The results include a set of 5 distinct personas that users rate as representative of them at an average level of 3.7 out of 5, and designers rate as having quality 3.5 out of 5. The median rate is 4 out of 5 in the majority of criteria judged by users and designers. Both the personas and their creation and evaluation approach are contributions of this study to the design of software that satisfies the explainability requirement.
Entropy Regularization for Mean Field Games with Learning

Entropy regularization has been extensively adopted to improve the efficiency, the stability, and the convergence of algorithms in reinforcement learning. This paper analyzes both quantitatively and qualitatively the impact of entropy regularization for Mean Field Game (MFG) with learning in a finite time horizon. Our study provides a theoretical justification that entropy regularization yields time-dependent policies and, furthermore, helps stabilizing and accelerating convergence to the game equilibrium. In addition, this study leads to a policy-gradient algorithm for exploration in MFG. Under this algorithm, agents are able to learn the optimal exploration scheduling, with stable and fast convergence to the game equilibrium.
RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining

Most movie recommendation methods use hard-clustering and simple collaborative filtering techniques in order to achieve their end results. However, these methods tend to overlook crucial aspects of both users and items. When these methods hard cluster a movie item into a cluster, they turn a blind eye to the fact that the item also exhibits some properties of another cluster's items. Recommender systems facilitate users and relevant things expeditiously supported their requests and historic communications with alternative customers. Recommendation systems are a crucial portion of signifying things particularly in streaming amenities. For streaming motion-picture show services like Netflix, recommendation methods are vital for serving to users notice fresh movies to get pleasure from. However, massive amounts of information will turn out restrictions in recommendations due to accuracy as a result of diversity and meagerness problems. Our work proposes a unique hybrid technique that mixes collaborative filtering and characteristics of demographic filtering technique to point the close users, and associate against one another. This technique has been established over associate in tending analysis of the way to cut back the blunders in grading estimates supported users' earlier communications that ends up in improved prediction accuracy in among completely different algorithms. Additionally, a feature combination technique is utilized that progresses the expectation accuracy and to check our method, using MovieLens 1M dataset, we contended an offline assessment, already available assessment tactics, and compared the same with the output factors to support authenticating the proposed procedure.
A Semantic-Embedding Model-Driven Seq2Seq Method for Domain-Oriented Entity Linking on Resource-Restricted Devices

General entity linking systems usually leverage global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches. However, graph-based approaches are computationally expensive for open-domain datasets. In this paper, the authors overcome these problems by presenting an RDF embedding-based seq2seq entity linking method in specific domains. They filter candidate entities of mentions having similar meanings by using the domain information of the annotated pairs. They resolve high ambiguous pairs by using Bi-directional long short-term memory (Bi-LSTM) and attention mechanism for the entity disambiguation. To evaluate the system with baseline methods, they generate a dataset including book, music, and movie categories. They achieved 0.55 (Mi-F1), 0.586 (Ma-F1), 0.846 (Mi-F1), and 0.87 (Ma-F1) scores for high and low ambiguous datasets. They compare the method by using recent (WNED-CWEB) datasets with existing methods. Considering the domain-specificity of the proposed method, it tends to achieve competitive results while using the domain-oriented datasets.
Deep Learning Based Automated tool for cancer diagnosis from bone histopathology images

Histopathology procedure is conventional in nature. Moreover, manual procedure of Pathologists can handle only limited cases due to prolonged steps. This manual procedure may mislead the doctor if there are bulk cases to investigate due to time constraint and nature of complicated disease like bone cancer. To deal this research in digital histopathology is vital by developing computer assisted tool for diagnosis. Bone architecture complexity is the main reason to remain as gray area for research. Understanding and exploration of different magnitude of bone anatomy will cater the need for constructing research in automation. This research work framed with three major bone cancer namely Ewing sarcoma, Osteosarcoma and Chondrosarcoma. Deep learning method with the concepts of image processing techniques are adopted for bone cancer detection. Deep learning method studies profound features from lower level to higher level without human intervention. Here, database contains total of 1000 images which are resized into $350 \times 350$ without loss of information. In this study dataset of 600 malignant,200 normal and 200 benign images of same size and resolution are used. The Artificial neural networks model(CNN) is a supervised learning used to analyze abstract features in the training set. Deep Learning demands large number of images and its strength was increased as per requirement by augmentation technique. Contribution of this work is classification of images into categories such as malignant, benign and normal and success rate is 92%.
An Overview of Geometrical Optics Restricted Quantum Key Distribution

Quantum key distribution (QKD) assures the theoretical information security from the physical layer by safely distributing true random numbers to the communication parties as secret keys while assuming an omnipotent eavesdropper (Eve). In recent years, with the growing applications of QKD in realistic channels such as satellite-based free-space communications, certain conditions such as the unlimited power collection ability of Eve become too strict for security analysis. Thus, in this invited paper, we give a brief overview of the quantum key distribution with a geometrical optics restricted power collection ability of Eve with its potential applications.
On Smoother Attributions using Neural Stochastic Differential Equations

Several methods have recently been developed for computing attributions of a neural network's prediction over the input features. However, these existing approaches for computing attributions are noisy and not robust to small perturbations of the input. This paper uses the recently identified connection between dynamical systems and residual neural networks to show that the attributions computed over neural stochastic differential equations (SDEs) are less noisy, visually sharper, and quantitatively more robust. Using dynamical systems theory, we theoretically analyze the robustness of these attributions. We also experimentally demonstrate the efficacy of our approach in providing smoother, visually sharper and quantitatively robust attributions by computing attributions for ImageNet images using ResNet-50, WideResNet-101 models and ResNeXt-101 models.
A map point on the role of the telemedicine and e-Health in the digital contact tracing during the COVID-19 pandemic

Abstract: In public health, independently by the technology use, contact tracing (CT) is the process of identifying people who may have met an infected person and subsequent collection of further information on these contacts. The differences between the potential methods of carrying out CT in 2003, during the SARS epidemic, and the current one SARS-CoV-2 are considerable. During the previous pandemic, current mobile technologies were not available (in particular the smartphone as we know it today). The role of the mobile technology—and therefore of the mobile Health (mHealth)—was and is basic during this pandemic for the digital contact tracing (DCT). The review, starting from the introduction of contact tracing performed manually, faced the potentialities and the technologies used for DCT based on dedicated APPs, interrogating on the state of development and on the aspects affecting the effectiveness of the DCT. From this review, various phases of the dissemination of medical knowledge around these Apps emerged. In a first phase, the novelty was high as well as the consequent difficulty on the part of epidemiology to set a concrete approach on them. Subsequently, scientific knowledge has spread, publications have increased and even the great IT giants have moved in the development of solutions. It was highlighted that hundreds of Apps have been/proposed and/or are under development in the World according to different approaches in terms of the (I) technologies, (II) protocols (Bluetooth and Global Positioning System), (III) centralized governmental choice. The review in a first part extracted some important experiences in this Area captured during the first period; In a second part extracted some important outcomes from research of the next phases. The review ends pointing out the reasons for success/failure of the DCT and the lessons for the future for the epidemiologist.
Incremental Cluster Validity Index-Guided Online Learning for Performance and Robustness to Presentation Order

In streaming data applications, the incoming samples are processed and discarded, and therefore, intelligent decision-making is crucial for the performance of lifelong learning systems. In addition, the order in which the samples arrive may heavily affect the performance of incremental learners. The recently introduced incremental cluster validity indices (iCVIs) provide valuable aid in addressing such class of problems. Their primary use case has been cluster quality monitoring; nonetheless, they have been recently integrated in a streaming clustering method. In this context, the work presented, here, introduces the first adaptive resonance theory (ART)-based model that uses iCVIs for unsupervised and semi-supervised online learning. Moreover, it shows how to use iCVIs to regulate ART vigilance via an iCVI-based match tracking mechanism. The model achieves improved accuracy and robustness to ordering effects by integrating an online iCVI module as module B of a topological ART predictive mapping (TopoARTMAP)—thereby being named iCVI-TopoARTMAP—and using iCVI-driven postprocessing heuristics at the end of each learning step. The online iCVI module provides assignments of input samples to clusters at each iteration in accordance to any of the several iCVIs. The iCVI-TopoARTMAP maintains useful properties shared by the ART predictive mapping (ARTMAP) models, such as stability, immunity to catastrophic forgetting, and the many-to-one mapping capability via the map field module. The performance and robustness to the presentation order of iCVI-TopoARTMAP were evaluated via experiments with synthetic and real-world datasets.
Post-quantum Cryptography in 6G

The quantum computing paradigm is fundamentally different from the classical one. There are computational problems we are not able to solve on a contemporary computer, but which we can efficiently solve on a quantum one. One of these problems is the discrete logarithm problem (DLP) which is the basis of modern asymmetric cryptography. Once large-scale quantum computing becomes a reality, these cryptographic primitives need to be replaced with quantum-secure ones. While we are still in the early stages of quantum computing, steps have been taken to prepare for the shift to cryptography that is secure in the post-quantum world. According to the current knowledge, contemporary symmetric cryptography remains secure for the most part even after the advent of quantum computing. Asymmetric primitives based on integer factorization and the DLP need to be replaced. In this chapter, we take a look at the post-quantum secure alternatives for key establishment, public-key encryption and digital signatures. We also discuss their properties and the effect on the performance of the future 6G networks.
Differential cell counts using center-point networks achieves human-level accuracy and efficiency over segmentation

Abstract Differential cell counts is a challenging task when applying computer vision algorithms to pathology. Existing approaches to train cell recognition require high availability of multi-class segmentation and/or bounding box annotations and suffer in performance when objects are tightly clustered. We present differential count network (“DCNet”), an annotation efficient modality that utilises keypoint detection to locate in brightfield images the centre points of cells (not nuclei) and their cell class. The single centre point annotation for DCNet lowered burden for experts to generate ground truth data by 77.1% compared to bounding box labeling. Yet centre point annotation still enabled high accuracy when training DCNet on a multi-class algorithm on whole cell features, matching human experts in all 5 object classes in average precision and outperforming humans in consistency. The efficacy and efficiency of the DCNet end-to-end system represents a significant progress toward an open source, fully computationally approach to differential cell count based diagnosis that can be adapted to any pathology need.
Path planning for UAV based on improved dynamic step RRT algorithm

Abstract Path planning is an important problem in the field of unmanned aerial vehicles (UAVs). However, it is inefficient for many rapidly-exploring random tree (RRT) based methods to rapidly find a feasible solution in a complex environment. To solve the path planning problem for the UVA in a complex environment, an improved dynamic step size RRT algorithm combined with a new path length control strategy is proposed. Firstly, the algorithm adopts a biased-goal sampling strategy to guide the growth of the tree, and an expansion direction constraint strategy is adopted to limit the expansion direction of the tree. Secondly, an improved dynamic step size strategy is proposed to speed up the path searching process. Thirdly, a path length constraint strategy is designed, which is used to constrain the path length during the searching process for a solution. Finally, simulation results demonstrate that the proposed method achieves improvement in both computational time and the path quality.
Adaptive Template and Transition Map for Real-Time Video Object Segmentation

Semi-supervised video object segmentation (semi-VOS) is required for many visual applications. This task is tracking class-agnostic objects from a given segmentation mask. Various approaches have been developed and achieved high accuracy in this field, but these previous models are hard to be utilized in real-world applications due to slow inference time and tremendous complexity. To significantly speed up inference while reducing performance gaps from those previous models, we introduce a fast segmentation model based on a template matching method and auxiliary loss with a transition map. Our template matching method consists of short-term and long-term matching. The short-term matching enhances target object localization by focusing on neighboring frames, while long-term matching improves fine details and handles object shape-changing by considering long-range frames. However, since both matching processes generate each template based on the previously estimated masks, this incurs error propagation for tracking objects in the next frames. To mitigate this problem, we add auxiliary loss with a newly proposed transition map for encouraging correction power to create accurate masks of the target object. Our model obtains <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$81.1\%~J\&amp;F$ </tex-math></inline-formula> score at the speed of 78.3 FPS on the DAVIS16 benchmark and achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4\times $ </tex-math></inline-formula> faster speed and 11.3% higher accuracy than SiamMask, one of the fast semi-VOS models.
From Deep Learning to Deep Reasoning

The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of constructing large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything once we have sufficient data and computational resources. However, neural networks are fast to exploit surface statistics but fail miserably to generalize to novel combinations. This is because they are not designed for deliberate reasoning -- the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to "learning-to-reason'' from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary compositional querying without the need of predefining a narrow set of tasks. The tutorial consists of four parts. The first part covers the learning-to-reason framework, and explains how neural networks can serve as a strong backbone for reasoning through its natural operations such as binding, attention & dynamic computational graphs. The second part goes into more detail on how neural networks perform reasoning over unstructured and structured data, and across modalities. The third part reviews neural memories and their role in reasoning. The last part discusses generalization to novel combinations, under less supervision and with more knowledge.
Classification of Care Assistive Technology Based on the Relationship Between Users and Technologies

While an aging population in Japan and other countries is resulting in an increased demand for nursing care services, there is a shortage of care workers due to a decrease in the working population. The use of care assistive technologies such as robots, artificial intelligence, and information technology is expected to compensate for this shortage, and many care assistive technologies such as power assist devices and communication robots have been developed. However, their introduction and widespread use in actual service have not progressed. The aim of this study is to address the problem on less technology introduction in nursing care by surveying the attitudes of potential users to care assistive technologies, clarify the factors affecting the use of such technologies, and propose a new design method for the technologies. We surveyed attitudes to the use of care assistive technology from the perspective of function and information utilization for three groups: active seniors, informal carers, and formal carers. The survey was conducted in Japan and Finland, which have high aging populations. Based on the survey results, we proposed, as a design method, a classification of care assistive technologies according to their interaction with users as well as the nursing care process.
Spontaneous Communicative Conventions through Virtual Bargaining

Human-like computing will require machines to communicate interactively with people, to take in human knowledge and preferences, to explain the machine’s actions, and to generally coordinate human and machine behaviour. But even the simplest communicative interactions raise deep theoretical challenges. Human and machine spontaneously have to “agree” on the mapping between possible signals and messages. Such mappings are extremely flexible and influenced by large numbers of contextual factors in real-world interactions. Typically, there are many candidate signal-message mappings. Successful communication requires that both parties agree on the same mapping. We outline experimental and theoretical work exploring an approach to these problems based on “virtual bargaining,” according to which each communicative party attempts to infer which mapping would be agreed were prior communication possible. This chapter explores how such problems are solved by humans, and suggests directions for building computational models of these processes.