Reviewerly/exHarmony huggingface dataset:
IoT-Based Smart Home Device Monitor Using Private Blockchain Technology and Localization

Internet of Things (IoT)-based smart home applications are rising in popularity. However, this trend attracts malicious activity, which causes cost-efficient security to be in high demand. This letter proposes a low-end design that reinforces the security of a home network. It uses private blockchain technology and localization via RSSI-based trilateration. We investigated the benefits of private blockchains over their public counterpart, and we improve the precision of the localization algorithm by testing it against different wireless technologies. The results conclude that using a private blockchain with a WiFi-based communication system produces the most efficient iteration of the proposed design.
Machine-learned Regularization and Polygonization of Building Segmentation Masks

We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building segmentation maps exploiting generic <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fully</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">convolutional</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(FCN)</i> . A <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generative</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adversarial</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(GAN)</i> is then involved to perform a regularization of building boundaries to make them more realistic, i.e., having more rectilinear outlines which construct right angles if required. This is achieved through the interplay between the discriminator which gives a probability of input image being true and generator that learns from discriminator's response to create more realistic images. Finally, we train the backbone <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">convolutional</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neural</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(CNN)</i> which is adapted to predict sparse outcomes corresponding to building corners out of regularized building segmentation results. Experiments on three building segmentation datasets demonstrate that the proposed method is not only capable of obtaining accurate results, but also of producing visually pleasing building outlines parameterized as polygons.
Reward tampering problems and solutions in reinforcement learning: a causal influence diagram perspective

Can humans get arbitrarily capable reinforcement learning (RL) agents to do their bidding? Or will sufficiently capable RL agents always find ways to bypass their intended objectives by shortcutting their reward signal? This question impacts how far RL can be scaled, and whether alternative paradigms must be developed in order to build safe artificial general intelligence. In this paper, we study when an RL agent has an instrumental goal to tamper with its reward process, and describe design principles that prevent instrumental goals for two different types of reward tampering (reward function tampering and RF-input tampering). Combined, the design principles can prevent reward tampering from being an instrumental goal. The analysis benefits from causal influence diagrams to provide intuitive yet precise formalizations.
Blockchain 1.0 to Blockchain 4.0—The Evolutionary Transformation of Blockchain Technology

Blockchain is a propitious technology that has gained immense popularity and tractions. It has tremendously revolutionized the peer-to-peer information exchange by combining cryptographic principles with decentralization, immutability and transparency. The term blockchain has been coined from its fundamental feature of being a distributed ledger where each record or block is secured and bound to its successive blocks through hash functions thus resulting in this chain of blocks. This chapter first gives the historical background of this expeditious technology. It then proffers a description of the basic terminologies in blockchain, it’s types, basic structure of block and different consensus models popularly known. The prime emphasis of this chapter is to bestow an extensive study of the chronological evolutions in Blockchain Technology by highlighting the nitty-gritty of each generation in detail. It also illustrates a parameter wise differences amidst the several generations in terms of their principle areas, consensus models used, utility of smart contracts, the energy and cost requirements and execution speed and scalability. In the end, a Blockchain in Supply Chain Management test case has also been elaborated in this chapter.
A Hybrid Approach for Solving Systems of Nonlinear Equations Using Harris Hawks Optimization and Newton’s Method

Systems of nonlinear equations are known as the basis for many models of engineering and data science, and their accurate solutions are very critical in achieving progress in these fields. However, solving a system with multiple nonlinear equations, usually, is not an easy task. Consequently, finding a robust and accurate solution can be a very challenging problem in complex systems. In this work, a novel hybrid method namely Newton-Harris hawks optimization (NHHO) for solving systems of nonlinear equations is proposed. The proposed NHHO combines Newton's method, with a second-order convergence where the correct digits roughly double in every step, and the Harris hawks optimization (HHO) to enhance the search mechanism, avoid local optima, improve convergence speed, and find more accurate solutions. We tested a group of six well-known benchmark systems of nonlinear equations to evaluate the efficiency of NHHO. Further, comparisons between NHHO and other optimization algorithms, including the original HHO algorithm, Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Butterfly Optimization Algorithm (BOA), and Equilibrium Optimization (EO) were performed. The norm of the equation system was calculated as a fitness function to measure the optimization algorithms' performance. A solution with less fitness value is considered a better solution. Furthermore, the experimental results confirmed the superiority of NHHO over the other optimization algorithms, in the comparisons, in different aspects, including best solution, average fitness value, and convergence speed. Accordingly, the proposed NHHO is powerful and more effective in all benchmark problems in solving systems of nonlinear equations compared to the other optimization algorithms. Finally, NHHO overcomes the limitations of Newton's method, including selecting the initial point and divergence problems.
Variable selection for Naïve Bayes classification

The Na\"ive Bayes has proven to be a tractable and efficient method for classification in multivariate analysis. However, features are usually correlated, a fact that violates the Na\"ive Bayes' assumption of conditional independence, and may deteriorate the method's performance. Moreover, datasets are often characterized by a large number of features, which may complicate the interpretation of the results as well as slow down the method's execution. In this paper we propose a sparse version of the Na\"ive Bayes classifier that is characterized by three properties. First, the sparsity is achieved taking into account the correlation structure of the covariates. Second, different performance measures can be used to guide the selection of features. Third, performance constraints on groups of higher interest can be included. Our proposal leads to a smart search, which yields competitive running times, whereas the flexibility in terms of performance measure for classification is integrated. Our findings show that, when compared against well-referenced feature selection approaches, the proposed sparse Na\"ive Bayes obtains competitive results regarding accuracy, sparsity and running times for balanced datasets. In the case of datasets with unbalanced (or with different importance) classes, a better compromise between classification rates for the different classes is achieved.
Partial Person Re-identification with Part-Part Correspondence Learning

Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID). Nonetheless, most of approaches assume that the input is given with the fulfillment of expectations, while imperfect input remains rarely explored to date, which is a non-trivial problem since directly apply existing methods without adjustment can cause significant performance degradation. In this paper, we focus on recognizing partial (flawed) input with the assistance of proposed Part-Part Correspondence Learning (PPCL), a self-supervised learning framework that learns correspondence between image patches without any additional part-level supervision. Accordingly, we propose Part-Part Cycle (PP-Cycle) constraint and Part-Part Triplet (PP-Triplet) constraint that exploit the duality and uniqueness between corresponding image patches respectively. We verify our proposed PPCL on several partial person re-ID benchmarks. Experimental results demonstrate that our approach can surpass previous methods in terms of the standard evaluation metric.
The Digital Network of Networks: Regulatory Risk and Policy Challenges of Vaccine Passports

The extensive disruption to and digital transformation of travel administration across borders largely due to COVID-19 mean that digital vaccine passports are being developed to resume international travel and kick-start the global economy. Currently, a wide range of actors are using a variety of different approaches and technologies to develop such a system. This paper considers the techno-ethical issues raised by the digital nature of vaccine passports and the application of leading-edge technologies such as blockchain in developing and deploying them. We briefly analyse four of the most advanced systems – IBM’s Digital Health Passport “Common Pass,” the International Air Transport Association’s Travel Pass, the Linux Foundation Public Health’s COVID-19 Credentials Initiative and the Vaccination Credential Initiative (Microsoft and Oracle) – and then consider the approach being taken for the EU Digital COVID Certificate. Each of these raises a range of issues, particularly relating to the General Data Protection Regulation (GDPR) and the need for standards and due diligence in the application of innovative technologies (eg blockchain) that will directly challenge policymakers when attempting to regulate within the network of networks.
Bag-of-Words Technique in Natural Language Processing: A Primer for Radiologists

Natural language processing (NLP) is a methodology designed to extract concepts and meaning from human-generated unstructured (free-form) text. It is intended to be implemented by using computer algorithms so that it can be run on a corpus of documents quickly and reliably. To enable machine learning (ML) techniques in NLP, free-form text must be converted to a numerical representation. After several stages of preprocessing including tokenization, removal of stop words, token normalization, and creation of a master dictionary, the bag-of-words (BOW) technique can be used to represent each remaining word as a feature of the document. The preprocessing steps simplify the documents but also potentially degrade meaning. The values of the features in BOW can be modified by using techniques such as term count, term frequency, and term frequency–inverse document frequency. Experience and experimentation will guide decisions on which specific techniques will optimize ML performance. These and other NLP techniques are being applied in radiology. Radiologists’ understanding of the strengths and limitations of these techniques will help in communication with data scientists and in implementation for specific tasks. Online supplemental material is available for this article. ©RSNA, 2021
BiconNet: An edge-preserved connectivity-based approach for salient object detection

Salient object detection (SOD) is viewed as a pixel-wise saliency modeling task by traditional deep learning-based methods. A limitation of current SOD models is insufficient utilization of inter-pixel information, which usually results in imperfect segmentation near edge regions and low spatial coherence. As we demonstrate, using a saliency mask as the only label is suboptimal. To address this limitation, we propose a connectivity-based approach called bilateral connectivity network (BiconNet), which uses connectivity masks together with saliency masks as labels for effective modeling of inter-pixel relationships and object saliency. Moreover, we propose a bilateral voting module to enhance the output connectivity map, and a novel edge feature enhancement method that efficiently utilizes edge-specific features. Through comprehensive experiments on five benchmark datasets, we demonstrate that our proposed method can be plugged into any existing state-of-the-art saliency-based SOD framework to improve its performance with negligible parameter increase.
Enriching Mobile Learning Software with Interactive Activities and Motivational Feedback for Advancing Users’ High-Level Cognitive Skills

Mobile learning is a promising form of digital education to access learning content through modern handheld devices. Through mobile learning, students can learn using smartphones, connected to the Internet, without having restrictions posed by time and place. However, such environments should be enriched with sophisticated techniques so that the learners can achieve their learning goals and have an optimized learning experience. To this direction, in this paper, presents a mobile learning software which delivers interactive activities and motivational feedback to learners with the aim of advancing their higher level cognitive skills. In more detail, the mobile application employs two theories, namely Bloom’s taxonomy and the taxonomy of intrinsic motivations by Malone and Lepper. Bloom’s taxonomy is used for the design of interactive activities that belong to varying levels of complexity, i.e., remembering, understanding, applying, analyzing, evaluating, and creating. Concerning motivational feedback, the taxonomy of intrinsic motivations by Malone and Lepper is used, which identifies four major factors, namely challenge, curiosity, control, and fantasy, and renders the learning environment intrinsically motivating. As a testbed for our research, the presented mobile learning system was designed for the teaching of a primary school course; however, the incorporated taxonomies could be adapted to the tutoring of any course. The mobile application was evaluated by school students with very promising results.
Coordinated Backdoor Attacks against Federated Learning with Model-Dependent Triggers

Federated learning enables distributed training of deep learning models among user equipment (UE) to obtain a high-quality global model. A centralized server aggregates the updates submitted by UEs without knowledge of the local training data or process. Despite its privacy preserving merit, we reveal a severe security concern. Malicious UEs can manipulate their training data by injecting a back-door trigger. Thus, the global model that aggregates those malicious updates may make false predictions on the samples with the backdoor trigger. However, the effect of a single backdoor trigger will quickly be diluted by subsequent benign updates. In this work, we present an effective coordinated backdoor attack against federated learning using multiple local triggers; the global trigger consists of various separate local triggers. Moreover, in contrast to using random triggers, we propose using model-dependent triggers (i.e., generated based on local models of attackers) to conduct backdoor attacks. We conduct extensive experiments to assess the effectiveness of our proposed backdoor attacks on MNIST and CIFAR-10 datasets. Experimental results show that our proposed methodology outperforms both coordinated attacks using random triggers and single trigger backdoor attacks in terms of attack success rate. We also show that Byzantine-resilient aggregation methodologies are not robust to our proposed attacks.
Weak and strong convergence analysis of Elman neural networks via weight decay regularization

In this paper, we propose a novel variant of the algorithm to improve the generalization performance for Elman neural networks (ENN). Here, the weight decay term, also called L2 regularization, which can effectively control the value of weights excessive growth, also over-fitting phenomenon can be effectively prevented. The main contribution of this work lies in that we have conducted a rigorous theoretical analysis of the proposed approach, i.e. the weak and strong convergence results are obtained. The comparison experiments to the problems of function approximation and classification on the real-world data have been performed to verify the theoretical results.
Toward an Interactive Model of Reading

The purpose of this chapter is to develop a formalism within which psychologists can develop detailed information processing models of the reading process. I argue that such a formalism is necessary because the usual formalisms tend to lead most naturally to bottom-up, serial, stage-by-stage models of reading. Moreover, I argue that there is a good deal of evidence suggesting that reading is best characterized as a process of applying simultaneous constraints at all levels and thereby coming up with the most probable interpretation of the input string. Although it is probably not impossible to use the usual flow chart formalisms to represent such models (have arrows pointing back from higher levels to lower levels) it is not especially natural and when carried to the extreme of a completely interacting system is not very informative (two way arrows between every pair of levels). I suggest that the formalisms designed for parallel computing applications are the best substitutions. Finally, I develop a model based on HEARSAY II and GSP and argue that such a model has many very promising features.
BANet: A Blur-Aware Attention Network for Dynamic Scene Deblurring

Image motion blur results from a combination of object motions and camera shakes, and such blurring effect is generally directional and non-uniform. Previous research attempted to solve non-uniform blurs using self-recurrent multi-scale, multi-patch, or multi-temporal architectures with self-attention to obtain decent results. However, using self-recurrent frameworks typically leads to a longer inference time, while inter-pixel or inter-channel self-attention may cause excessive memory usage. This paper proposes a Blur-aware Attention Network (BANet), that accomplishes accurate and efficient deblurring via a single forward pass. Our BANet utilizes region-based self-attention with multi-kernel strip pooling to disentangle blur patterns of different magnitudes and orientations and cascaded parallel dilated convolution to aggregate multi-scale content features. Extensive experimental results on the GoPro and RealBlur benchmarks demonstrate that the proposed BANet performs favorably against the state-of-the-arts in blurred image restoration and can provide deblurred results in real-time.
FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation

Robust visual recognition under adverse weather conditions is of great importance in real-world applications. In this context, we propose a new method for learning semantic segmentation models robust against fog. Its key idea is to consider the fog condition of an image as its style and close the gap between images with different fog conditions in neural style spaces of a segmentation model. In particular, since the neural style of an image is in general affected by other factors as well as fog, we introduce a fog-pass filter module that learns to extract a fog-relevant factor from the style. Optimizing the fog-pass filter and the segmentation model alternately gradually closes the style gap between different fog conditions and allows to learn fog-invariant features in consequence. Our method substantially outperforms previous work on three real foggy image datasets. Moreover, it improves performance on both foggy and clear weather images, while existing methods often degrade performance on clear scenes.
An Agent Based Model to Analyze the Bitcoin Mining Activity and a Comparison with the Gold Mining Industry

In this paper, we present an analysis of the mining process of two popular assets, Bitcoin and gold. The analysis highlights that Bitcoin, more specifically its underlying technology, is a “safe haven” that allows facing the modern environmental challenges better than gold. Our analysis emphasizes that crypto-currencies systems have a social and economic impact much smaller than that of the traditional financial systems. We present an analysis of the several stages needed to produce an ounce of gold and an artificial agent-based market model simulating the Bitcoin mining process and allowing the quantification of Bitcoin mining costs. In this market model, miners validate the Bitcoin transactions using the proof of work as the consensus mechanism, get a reward in Bitcoins, sell a fraction of them to cover their expenses, and stay competitive in the market by buying and divesting hardware units and adjusting their expenses by turning off/on their machines according to the signals provided by a technical analysis indicator, the so-called relative strength index.
Federated Reinforcement Learning

In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited. Despite the success of previous transfer learning approaches in deep reinforcement learning, directly transferring data or models from an agent to another agent is often not allowed due to the privacy of data and/or models in many privacy-aware applications. In this paper, we propose a novel deep reinforcement learning framework to federatively build models of high-quality for agents with consideration of their privacies, namely Federated deep Reinforcement Learning (FedRL). To protect the privacy of data and models, we exploit Gausian differentials on the information shared with each other when updating their local models. In the experiment, we evaluate our FedRL framework in two diverse domains, Grid-world and Text2Action domains, by comparing to various baselines.