Reviewerly/exHarmony huggingface dataset:
A new Neuro-Fuzzy Inference System with Dynamic Neurons (NFIS-DN) for system identification and time series forecasting

A new Neuro-Fuzzy Inference System with Dynamic Neurons or NFIS-DN is presented here for discrete time dynamic system identification and time series forecasting problems. The proposed dynamic system based neuron, referred to as Dynamic Neuron (DN) is realized by a discrete-time nonlinear state-space model. The DN is designed such way, that the output considers only the effect of finite past instances, enabling the system with finite memory. The NFIS-DN model has five layers, and DNs are employed only in the layers handling crisp values. The antecedent and the consequent parameters of NFIS-DN are updated using a self-regulated backpropagation through time learning algorithm. The performance evaluation of NFIS-DN has been carried-out using benchmark problems in the areas of nonlinear system identification and time series forecasting. The results are compared with the state-of-the-art method on the neural fuzzy networks. The obtained results clearly suggest that the NFIS-DN performs significantly better while using a smaller or similar number of fuzzy rules. Finally the practical application of the NFIS-DN has been demonstrated using two real-world problems.
Co-designing AI Futures

As recent scholars have noted, there is little discourse amongst the HCI, interaction design, and UX communities on topics of AI and their relationship to design practice, a gap this workshop aims to address. Bringing together practitioners and researchers from a variety of backgrounds, this workshop sets out three goals: (1) identify case studies and projects at the intersection of HCI and AI, highlighting their ethical dimensions; (2) identify the chief challenges to HCI and AI collaborations and strategies to address them; and (3) foster a community for continued discourse and development on the intersections between AI Ethics, social computing, and design.
GPUGuard

Graphics processing units (GPUs) are moving towards supporting concurrent kernel execution where multiple kernels may be co-executed on the same GPU and even on the same streaming multiprocessor (SM) core. While concurrent kernel execution improves hardware resource utilization, it opens up vulnerabilities to covert-channel and side-channel attacks. These attacks exploit information leakage across kernels that results from contention on shared resources; they have been shown to be a dangerous threat on CPUs, and are starting to be demonstrated on GPUs. The unique micro-architectural features of GPUs, such as specialized cache structures and massive parallel thread support, create opportunities for GPU-specific channels to be formed. In this paper, we propose GPUGuard, a decision tree based detection and a hierarchical defense framework that can reliably close the covert channels. Our results show that GPUGuard can detect contention with 100% sensitivity and a small (8.5%) false positive rate. The timing channels are mitigated through Tangram, a GPU-specific contention channel elimination scheme, with only 8% to 23% overhead when there is an attack and zero performance overhead when no attacks are detected. Compared to temporal partitioning, GPUGuard is 69%-96% faster in various architectures even when active, showing that it is possible to gain substantial performance from executing concurrent kernels on a single SM while securing GPUs against these attacks.
Effective and Efficient Sports Play Retrieval with Deep Representation Learning

With the proliferation of commercial tracking systems, sports data is being generated at an unprecedented speed and the interest in sports play retrieval has grown dramatically as well. However, it is challenging to design an effective, efficient and robust similarity measure for sports play retrieval. To this end, we propose a deep learning approach to learn the representations of sports plays, called play2vec, which is robust against noise and takes only linear time to compute the similarity between two sports plays. We conduct experiments on real-world soccer match data, and the results show that our solution performs more effectively and efficiently compared with the state-of-the-art methods.
Safeguarding a formalized Blockchain-enabled identity-authentication protocol by applying security risk-oriented patterns

Designing government independent and secure identification- and authentication protocols is a challenging task. Design flaws and missing specifications as well as security- and privacy issues of such protocols pose considerable user risks. Formal methods, such as Colored Petri Nets (CPN), are utilised for the design, development and analysis of such new protocols in order to detect flaws and mitigate identified security risks before deployment. This paper fills the gap, by applying in a novel way a set of security risk-oriented patterns (SRP) to the so-called Authcoin protocol that we formalise using CPN. The initial formal model of Authcoin facilitates the detection and elimination of design flaws, missing specifications as well as security- and privacy issues. The additional risk- and threat analysis based on the Information Systems Security Risk Management (ISSRM) domain model we perform on the formal CPN models of the protocol. The identified risks are mitigated by applying SRPs to the formal model of the Authcoin protocol. SRPs are a means to mitigate common security- and privacy risks in a business-process context by applying thoroughly tested and proven best-practice solutions. The goal of this work is to test the utility of SRPs outside of the the usual application domain, to reduce the risks and vulnerabilities of the Authcoin protocol.
A Survey of Augmented Reality Applications in Cultural Heritage

Augmented reality is now a well-known concept, a technology that integrates digital content to the user's physical world. As more and more applications begin to reshape user's daily lives in multiple domains, cultural heritage sites have begun to focus on capitalizing augmented reality's potential and improve their visitor experience. This work provides an overview of the state-of-the-art techniques, methodologies, and technologies integrated with augmented reality applications for cultural heritage while also categorizing the current works according to the latest techniques as they integrate to enrich the user's experience. The survey presents an overview of the characteristics of an augmented reality system, and discusses the current challenges, issues, limitations, and future directions. Finally, this work examines the methods and techniques that are used in the cultural heritage and augmented reality integration, outlining benefits, and open issues.
Evolutionary optimized fuzzy reasoning with mined diagnostic patterns for classification of breast tumors in ultrasound

Computer-aided-diagnostic (CAD) techniques are of great help in facilitating the diagnosis of breast ultrasound (BUS) images. Conventional CAD approaches segment the region of interest (ROI), extract image features, and finally train a classifier for outputting the final diagnostic decision. Although some reported CAD systems have achieved high accuracy, their performance heavily depended on the source of training data. Meanwhile, the classification procedures formed by low-level features are hardly explicable. A novel CAD approach for BUS data with human-in-the-loop is therefore proposed in this paper. Differing from conventional CAD systems, user participation is involved to manually give the scores for selected Breast Imaging Reporting and Data System (BI-RADS) features. Three experienced radiologists obtained 1062 breast ultrasound data samples with BI-RADS feature scores, which formed a quantified score dataset for mining diagnostic patterns based on a constant column biclustering method. The discovered diagnostic patterns (i.e., the biclusters) are input into a fuzzy reasoning system to infer the final diagnostic decision (i.e., benign or malignant). In addition, a particle swarm optimization (PSO) algorithm is employed to optimize the parameters of the membership function and judgment threshold for the fuzzy reasoning. The proposed system skips the image processing by utilizing the artificial scoring datasets, and the BUS images from different sources can be classified. Meanwhile, more understandable and acceptable results can be given by the fuzzy reasoning. In the experiment, 10-fold cross-validation was used to evaluate system performance. The average accuracy reached 96.81%, specificity 95.45%, sensitivity 97.72%, positive predictive value (PPV) 97.03%, and negative predictive value (NPV) 96.57%, outperforming previously reported methods.
Earthquakes Within Earthquakes: Patterns in Rupture Complexity

Abstract Earthquake source time functions carry information about the complexity of seismic rupture. We explore databases of earthquake source time functions and find that they are composed of distinct peaks that we call subevents. We observe that earthquake complexity, as represented by the number of subevents, grows with earthquake magnitude. Patterns in rupture complexity arise from a scaling between subevent moment and main event moment. These results can be explained by simple 2‐D dynamic rupture simulations with self‐affine heterogeneity in fault prestress. Applying this to early magnitude estimates, we show that the main event magnitude can be estimated after observing only the first few subevents.
Enhanced Rotation-Equivariant U-Net for Nuclear Segmentation

Despite recent advances in deep learning, the crucial task of nuclear segmentation for computational pathology remains challenging. Recently, deep learning, and specifically U-Nets, have shown significant improvements for this task, but there is still room for improvement by further enhancing the design and training of U-Nets for nuclear segmentation. Specifically, we consider enforcing rotation equivariance in the network, the placement of residual blocks, and applying novel data augmentation designed specifically for histopathology images, and show the relative improvement and merit of each. Incorporating all of these enhancements in the design and training of a U-Net yields significantly improved segmentation results while still maintaining a speed of inference that is sufficient for real-world applications, in particular, analyzing whole-slide images (WSIs). Code for our enhanced U-Net is available at https://github.com/thatvinhton/G-U-Net.
Coupling Techniques for Nonlinear Ensemble Filtering

We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses transportation of measures, convex optimization, and ideas from probabilistic graphical models to yield robust ensemble approximations of the filtering distribution in high dimensions. Our approach can be understood as the natural generalization of the ensemble Kalman filter (EnKF) to nonlinear updates, using stochastic or deterministic couplings. The use of nonlinear updates can reduce the intrinsic bias of the EnKF at a marginal increase in computational cost. We avoid any form of importance sampling and introduce non-Gaussian localization approaches for dimension scalability. Our framework achieves state-of-the-art tracking performance on challenging configurations of the Lorenz-96 model in the chaotic regime.
Underwater Image High Definition Display Using the Multilayer Perceptron and Color Feature-Based SRCNN

High-definition display technology for underwater images is of great significance for many applications, such as marine animal observation, seabed mining, and marine fishery production. The traditional underwater visual display systems have problems, such as low visibility, poor real-time performance, and low resolution, and cannot meet the needs of real-time high-definition displays in extreme environments. To solve these issues, we propose an underwater image enhancement method and a corresponding image super-resolution algorithm. To improve the quality of underwater images, we modify the Retinex algorithm and combine it with a neural network. The Retinex algorithm is used to defog the underwater image, and then, the image brightness is improved by applying gamma correction. Then, by combining with the dark channel prior and multilayer perceptron, the transmission map is further refined to improve the dynamic range of the image. In the super-resolution process, the current convolutional neural network reconstruction algorithm is only trained on the Y channel, which will lead to problems due to the insufficient acquisition of the color feature. Therefore, an image super-resolution reconstruction algorithm that is based on color features is proposed. The experimental results show that the proposed method improves the reconstruction effect of the image edges and texture details, increases the image clarity, and enhances the image color recovery.
Creating student’s profile using blockchain technology

Blockchain technology offers advantages such as integrity, anonymity, credibility and independence of the institution and time because of its decentralized nature. One of the current areas for tackling challenges and potential application of blockchain technology is education. Higher education is a complex system with various challenges that could be potentially improved by introducing this technology. This paper shows a brief overview of current systems, their current state and a direction of their development, but also proposes specific guidelines and upgrades for current systems, that would enhance them and, consequently, the area of education. It also proposes blockchain integration levels in higher education that, combined with data extraction, can expand and deepen the area of education for the benefit of institutions, employers and students.
DARTSim: An Exemplar for Evaluation and Comparison of Self-Adaptation Approaches for Smart Cyber-Physical Systems

Motivated by the need for cyber-physical systems (CPS) to perform in dynamic and uncertain environments, smart CPS (sCPS) utilize self-adaptive capabilities to autonomously manage uncertainties at the intersection of the cyber and physical worlds. In this context, self-adaptation approaches face particular challenges, including (i) environment monitoring that is subject to sensing errors; (ii) adaptation actions that take time, sometimes due to physical movement; (iii) dire consequences for not adapting in a timely manner; and (iv) incomparable objectives that cannot be conflated into a single utility metric (e.g., avoiding an accident vs. providing good service). To enable researchers to evaluate and compare self-adaptation approaches aiming to address these unique challenges of sCPS, we introduce the DARTSim exemplar. DARTSim implements a high-level simulation of a team of unmanned air vehicles (UAVs) performing a reconnaissance mission in a hostile and unknown environment. Designed to be easily used by researchers, DARTSim provides a TCP-based interface for easy integration with external adaptation managers, documentation, and a fast simulation capability.
Review of Augmented Reality in Education: Situated Learning with Digital and Non-digital Resources

This chapter provides a current review of augmented reality in education. The scope of the review covers digital and non-digital technologies, such as mobile devices as well as paper-based systems. We hope the review can illuminate further discussion by addressing two key questions: (1) what are the learning theories that can be used to inform the design and to predict learning effectiveness in an augmented reality environment where the virtual and the real intertwined? And (2) how can AR be effectively used to promote intentional deep learning?
An Efficient Framework for Privacy-Preserving Computations on Encrypted IoT Data

There are two fundamental expectations from cloud-IoT applications using sensitive and personal data: 1) utility and 2) privacy. Due to the complex nature of cloud-IoT ecosystems, there is a growing concern about data utility at the cost of privacy. While the current state-of-the-art encryption schemes protect users' privacy, they preclude meaningful computations on encrypted data. Thus, the question remains “how can IoT device users benefit from cloud computing without worrying about privacy and security?” Cloud service providers (CSPs) can leverage fully homomorphic encryption (FHE) schemes to build privacy-preserving services. However, there are challenges in adopting them for cloud-IoT devices. Thus, to foster real-world adoption of FHE-based solutions, we propose a framework called proxy reciphering as a service. We leverage schemes, such as distributed servers, secret sharing, FHE, and chameleon hash functions to tailor a solution that enables long-term privacy-preserving computations for encrypted IoT-device data and is secure even after a device-key compromise. We evaluate the framework by developing a testbed and measuring the latencies with real-world ECG records from TELE ECG database. We also analyze the security properties against major cyber threats. We observe that: 1) the computation and communication latencies are acceptable, and the security gains outweigh the latencies introduced; 2) the throughput of the reciphering proxy servers can be increased with preprocessing; and 3) a key-refresh scheme can limit the postcompromise attack exposure window. We infer that proxy reciphering as a service is a practical, secure, scalable and an easy-to-adopt framework for long-term privacy-preserving cloud computations for cloud-IoT applications.
FAIR Principles and Digital Objects: Accelerating Convergence on a Data Infrastructure

As Moore's Law and associated technical advances continue to bulldoze their way through society, both exciting possibilities and severe challenges emerge. The upside is the explosive growth of data and compute resources that promise revolutionary modes of discovery and innovation not only within traditional knowledge disciplines, but especially between them. The challenge, however, is to build the large-scale, widely accessible, persistent and automated infrastructures that will be necessary for navigating and managing the unprecedented complexity of exponentially increasing quantities of distributed and heterogenous data. This will require innovations in both the technical and social domains. Inspired by the successful development of the Internet and leveraging the Digital Object Framework and FAIR Principles (for making data Findable, Accessible, Interoperable and Reusable by machines) the GO FAIR initiative works with voluntary stakeholders to accelerate convergence on minimal standards and working implementations leading to an Internet of FAIR Data and Services (IFDS). In close collaboration with GO FAIR and DONA, the RDA GEDE and C2CAMP initiatives will continue its FAIR DO implementation efforts..
Curbing Address Reuse in the IOTA Distributed Ledger: A Cuckoo-Filter-Based Approach

IOTA is a lightweight quantum-resistant distributed ledger protocol designed for Internet of Things devices. The protocol uses a one-time cryptographic signature scheme that is resistant against attacks by quantum computers. This one-time signature scheme results in a portion of an address's private key being revealed in the signature of each outgoing transaction from that address. If an address is used for multiple outgoing transfers, it is feasible for an adversary to forge a signature by using parts of the private key associated with the address that have been revealed. In this paper, we propose the use of the cuckoo filter in the IOTA core lightweight client in order to avoid address reuse. We validate our proposed approach by implementation and evaluation in the real IOTA architecture. The obtained performance results show that the cuckoo filter integration in the IOTA core lightweight client avoids address reuse and makes the process of new address generation much more efficient and secure. Additionally, our approach can be integrated directly into the current IOTA core lightweight client.
TrecTools

This paper introduces TrecTools, a Python library for assisting Information Retrieval (IR) practitioners with TREC-like campaigns. IR practitioners tasked with activities like building test collections, evaluating systems, or analysing results from empirical experiments commonly have to resort to use a number of different software tools and scripts that each perform an individual functionality - and at times they even have to implement ad-hoc scripts of their own. TrecTools aims to provide a unified environment for performing these common activities.
Emotion-Aware Human Attention Prediction

Despite the recent success in face recognition and object classification, in the field of human gaze prediction, computer models are still struggling to accurately mimic human attention. One main reason is that visual attention is a complex human behavior influenced by multiple factors, ranging from low-level features (e.g., color, contrast) to high-level human perception (e.g., objects interactions, object sentiment), making it difficult to model computationally. In this work, we investigate the relation between object sentiment and human attention. We first introduce a new evaluation metric (AttI) for measuring human attention that focuses on human fixation consensus. A series of empirical data analyses with AttI indicate that emotion-evoking objects receive attention favor, especially when they co-occur with emotionally-neutral objects, and this favor varies with different image complexity. Based on the empirical analyses, we design a deep neural network for human attention prediction which allows the attention bias on emotion-evoking objects to be encoded in its feature space. Experiments on two benchmark datasets demonstrate its superior performance, especially on metrics that evaluate relative importance of salient regions. This research provides the clearest picture to date on how object sentiments influence human attention, and it makes one of the first attempts to model this phenomenon computationally.
Hybrid biogeography-based optimization with shuffled frog leaping algorithm and its application to minimum spanning tree problems

Biogeography-Based Optimization (BBO), a good meta-heuristic optimization algorithm, has drawn much attention and been applied widely to many areas. However, BBO can not do well in solving some complex and diversified optimization problems. In order to obtain an algorithm with better optimization performance, this paper presents a hybrid BBO with Shuffled Frog Leaping Algorithm (SFLA), named HBBOS. Firstly, we improve BBO. BBO's mutation operator is got rid of and its migration operator is improved. Two novel updating mechanisms, i.e. a hybrid cross mechanism and a hybrid disturbance mechanism, are introduced instead of the original migration mechanism to update the immigration habitats' Suitability Index Variables (SIVs) and non-immigration habitats' SIVs, respectively. A differential mechanism is also introduced to prevent the algorithm from falling into local optima to some degree. These improvements can enhance exploration and exploitation and balance them. Secondly, we merge the improved migration operator into SFLA's group structure framework. This can balance exploration and exploitation further. So HBBOS is obtained. HBBOS can effectively maximize the two algorithms' advantages and minimize the defects so that it can obtain better optimization performance. A large number of experiments are made on benchmark functions with various types and complexities, such as a set of classic functions and CEC2014 test set. HBBOS is also applied to minimum spanning tree problems. The experimental results show that HBBOS outperforms quite a few state-of-the-art algorithms.