Reviewerly/exHarmony huggingface dataset:
Construction of Cryptographic S-Boxes Based on Mobius Transformation and Chaotic Tent-Sine System

Over the last few decades, different mediums of secure communication use chaos which is demonstrated by some nonlinear dynamical systems. Chaos shows unpredictable behavior and this characteristic is quite helpful in different encryption techniques and for multimedia security. In this work, the chaotic behavior of the improved Tent-Sine map is conferred and ultimately a new method to construct substitution-boxes is proposed. This new method explores the features of chaos through TSS map and algebraic Mobius transformation to generate strong S-boxes. The S-boxes are assessed using standard tests suit which includes nonlinearity, strict avalanche criterion, bit independence criterion, linear approximation probability and differential uniformity. Moreover, the proposed S-boxes show excellent statistical properties under majority logic criterions such as correlation, homogeneity, energy, entropy, contrast. The statistical encryption results are demonstrate the better performance of the proposed S-boxes when compared with some of state of the art S-boxes including AES, Gray, APA S8 AES, Skipjack and validate the suitability of anticipated method.
Advanced social media sentiment analysis for short‐term cryptocurrency price prediction

Abstract In recent years, the scrutiny of bitcoin and other cryptocurrencies as legal and regulated components of financial systems has been increasing. Bitcoin is currently one of the largest cryptocurrencies in terms of capital market share. Therefore, this study proposes that sentiment analysis can be used as a computational tool to predict the prices of bitcoin and other cryptocurrencies for different time intervals. A key characteristic of the cryptocurrency market is that the fluctuation of currency prices depends on people's perceptions and opinions, not institutional money regulation. Therefore, analysing the relationship between social media and web search is crucial for cryptocurrency price prediction. This study uses Twitter and Google Trends to forecast the short‐term prices of the primary cryptocurrencies, as these social media platforms are used to influence purchasing decisions. The study adopts and interpolates a unique multimodel approach to analyse the impact of social media on cryptocurrency prices. Our results prove that people's psychological and behavioural attitudes have a significant impact on the highly speculative cryptocurrency prices.
Three types of integrated course designs for using mobile technologies to support creativity in higher education

Research shows a disconnection between creativity and education. In addition, studies rarely describe how instructors in higher education make use of mobile devices to foster student creativity. In order to obtain deeper knowledge of teaching practices, instructors in higher education were interviewed and 24 courses were analyzed using the constitutive elements of integrated course designs: teaching goals, learning activities, assessments, teacher-student interaction, and integration of mobile devices. The results show that instructors do not explicitly design for student creativity. However, an in-depth analysis that applied the Creativity Facets approach illustrates three types of implicitly integrated creativity that enhance meaningful learning with mobile technologies. The three design types are (a) Design of Interlinked Places, (b) Connected Communication Design, and (c) Process Design.
Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification

Person re-identification (re-ID) aims at matching images of the same identity across camera views. Due to varying distances between cameras and persons of interest, resolution mismatch can be expected, which would degrade person re-ID performance in real-world scenarios. To overcome this problem, we propose a novel generative adversarial network to address cross-resolution person re-ID, allowing query images with varying resolutions. By advancing adversarial learning techniques, our proposed model learns resolution-invariant image representations while being able to recover the missing details in low-resolution input images. The resulting features can be jointly applied for improving person re-ID performance due to preserving resolution invariance and recovering re-ID oriented discriminative details. Our experiments on five benchmark datasets confirm the effectiveness of our approach and its superiority over the state-of-the-art methods, especially when the input resolutions are unseen during training.
BlockSim: Blockchain Simulator

A blockchain is a distributed ledger in which participants that do not fully trust each other agree on the ledger's content by running a consensus algorithm. This technology is raising a lot of interest both in academia and industry, but the lack of tools to evaluate design and implementation decisions may hamper fast progress. To address this issue, this paper presents a discrete-event simulator that is flexible enough to evaluate different blockchain implementations. These blockchains can be rapidly modeled and simulated by extending existing models. Running Bitcoin and Ethereum simulations allowed us to change conditions and answer different questions about their performance. For example, we concluded that doubling the number of transactions per block has a low impact on the block propagation delay (10ms) and that encrypting communication has a high impact in that delay (more than 25%).
Deep Transfer Convolutional Neural Network and Extreme Learning Machine for lung nodule diagnosis on CT images

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Blockchain technology: Business, strategy, the environment, and sustainability

Business Strategy and the EnvironmentVolume 29, Issue 1 p. 321-322 CALL FOR PAPERS Blockchain technology: Business, strategy, the environment, and sustainability Chunguang April Bai, Chunguang April BaiSearch for more papers by this authorJames Cordeiro, James CordeiroSearch for more papers by this authorJoseph Sarkis, Joseph SarkisSearch for more papers by this author Chunguang April Bai, Chunguang April BaiSearch for more papers by this authorJames Cordeiro, James CordeiroSearch for more papers by this authorJoseph Sarkis, Joseph SarkisSearch for more papers by this author First published: 23 December 2019 https://doi.org/10.1002/bse.2431Citations: 40Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume29, Issue1January 2020Pages 321-322 RelatedInformation
Multiobject Fusion With Minimum Information Loss

The linear opinion pool (LinOP) provides a potential solution to the problem of information fusion. However, the LinOP cannot be directly applied to multi-object fusion since the resulting fused multi-object density, in general, no longer belongs to the same family of the local ones, thus it cannot be utilized as prior information for the next recursion in Bayesian multi-object filtering. In this letter, by showing that the LinOP is actually the one that leads to minimum information loss (MIL), we propose to find the fused multi-object density that has the same form as the local ones and, at the same time, leads to MIL. The performance of MIL fusion is then compared with the one of the well-known generalized covariance intersection (GCI) fusion via simulations.
A wavelet transform-based image segmentation method

In order to retain the details of image and automatically determine the segmentation threshold of image segmentation, an image segmentation method based on wavelet transform is proposed in this paper. The detailed information of horizontal, vertical and diagonal directions can be obtained by using image decomposition based on wavelet transform theory. The proposed method is tested on different images and it has better visual effect than other traditional methods, such as maximum variance segmentation, bi-modal segmentation and histogram valley threshold segmentation. Among the three image quality evaluation indicators, the proposed method also has obvious advantages, especially in medical image segmentation. This will provide a new method for medical image segmentation in the future, and also provide a direction for further research of medical image segmentation methods.
MAN: Mutual Attention Neural Networks Model for Aspect-Level Sentiment Classification in SIoT

Text sentiment classification is of critical importance to improve the autonomous decision making and communication ability among object peers in the Social Internet of Things (SIoT). To classify sentiment polarity on a fine-grained level, aspect-level sentiment classification has become a promising direction in recent years. However, the existing solutions typically ignore the mutual information between sentences and their respective aspect terms while generally performing sentiment classification by using the simple attention mechanism. Thus, the relevant results seem to be unpromising. We aim to develop a novel neural-network-based model, by relying on the natural language processing model for rich feature extraction, called mutual attention neural networks (MANs), to conduct the aspect-level sentiment classification tasks in this article. Compared with the previous work, our proposed MAN model takes advantage of the bidirectional long short-term memory (Bi-LSTM) networks to obtain semantic dependence of sentences and their respective aspect terms, while learning the sentiment polarities of aspect terms in sentences by proposing the mutual attention mechanism. To evaluate the performance of MAN, we conduct our experiments on three real-world data sets, i.e., LAPTOP, REST, and TWITTER. The experimental results show that our proposed MAN model has significant performance improvements when compared to several existing models, in terms of aspect-level sentiment classification.
A robust approach to reading recognition of pointer meters based on improved mask-RCNN

In this paper, we address a challenging task in real-word applications, i.e., automatic reading recognition for pointer meters, called PRM.1 This application is valuable in the fields of military, industry, and aerospace. However, the accuracy of recognizing the readings of pointer meters by machine vision is, oftentimes, affected by several factors, such as uneven illumination in each image, large range variation of illumination in different images, complex backgrounds, tilting of pointer meters, image blur, and scale change, resulting in the recognized readings with unacceptable accuracy. In this paper, a new robust approach to reading recognition of pointer meters is proposed. The proposed method consists of three main contributions: (1) constructing a novel deep learning algorithm in which the PrRoIPooling is used in lieu of the RoiAlign in the existing Mask-RCNN, (2) classifying the type of pointer meters while fitting the pointer binary mask, and (3) calculating the readings of pointer meters by the proposed angle method. In addition, we also report and release a new dataset for the community. Experiments show that the new algorithm can significantly improve the accuracy of the recognized readings of pointer meters, meanwhile, the proposed approach is also robust to the natural environments and computationally efficient.
Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier

The emerging integration of technology in daily lives has increased the need for more convenient methods for human-computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man-machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstrate radar signals as three-dimensional image patterns is presented and then, the inception module-based variant of GoogLeNet is used to analyze the pattern within the images for the recognition of different hand gestures. The proposed framework is exploited for eight different hand gestures with a promising classification accuracy of 95%. To verify the robustness of the proposed algorithm, multiple human subjects were involved in data acquisition.
Facial-recognition algorithms: A literature review

The face is an important part of the human body, distinguishing individuals in large groups of people. Thus, because of its universality and uniqueness, it has become the most widely used and accepted biometric method. The domain of face recognition has gained the attention of many scientists, and hence it has become a standard benchmark in the area of human recognition. It has turned out to be the most deeply studied area in computer vision for more than four decades. It has a wide array of applications, including security monitoring, automated surveillance systems, victim and missing-person identification and so on. This review presents the broad range of methods used for face recognition and attempts to discuss their advantages and disadvantages. Initially, we present the basics of face-recognition technology, its standard workflow, background and problems, and the potential applications. Then, face-recognition methods with their advantages and limitations are discussed. The concluding section presents the possibilities and future implications for further advancing the field.
Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization

Multitask learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multitask optimization applies multitask learning an optimization to study how effectively and efficiently tackle the multiple optimization problems, simultaneously. Evolutionary multitasking, or multifactorial optimization, is an emerging subfield of multitask optimization, which integrates evolutionary computation and multitask learning. This article proposes a novel and easy-to-implement multitasking genetic algorithm (MTGA), which copes well with significantly different optimization tasks by estimating and using the bias among them. Comparative studies with eight state-of-the-art single-task and multitask approaches in the literature on nine benchmarks demonstrated that, on average, the MTGA outperformed all of them and had lower computational cost than six of them. Based on the MTGA, a simultaneous optimization strategy for fuzzy system design is also proposed. Experiments on simultaneous optimization of type-1 and interval type-2 fuzzy logic controllers for couple-tank water level control demonstrated that the MTGA can find better fuzzy logic controllers than other approaches.
DAGIoV: A Framework for Vehicle to Vehicle Communication Using Directed Acyclic Graph and Game Theory

Data sharing and content offloading among vehicles is an imperative part of the Internet of Vehicles (IoV). A peer-to-peer connection among vehicles in a distributed manner is a highly promising solution for fast communication among vehicles. To ensure security and data tracking, existing studies use blockchain as a solution. The Blockchain-enabled Internet of Vehicles (BIoV) requires high computation power for the miners to mine the blocks and let the chain grow. Over and above, the blockchain consensus is probabilistic and the block generated today can be eventually declared as a fork and can be pruned from the chain. This reduces the overall efficiency of the protocol because the correct work done initially is eventually not used if it becomes a fork. To address these challenges, in this paper, we propose a Directed Acyclic Graph enabled IoV (DAGIoV) framework. We make use of a tangle data structure where each node acts as a miner and eventually the network achieves consensus among the nodes. A game-theoretic approach is used to model the interactions between the vehicles providing and consuming offloading services. The proposed model is proven to be highly scalable and well suited for microtransactions or frequent data transfer among the nodes in the vehicular network.
Conversational Recommender Systems and natural language:

Digital Assistants (DA) such as Amazon Alexa, Siri, or Google Assistant are now gaining great diffusion, since they allow users to execute a wide range of actions through messages in natural language. Even though DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they do not yet implement recommendation facilities. In this paper, we investigate the combination of Digital Assistants and Conversational Recommender Systems (CoRSs) by designing and implementing a framework named ConveRSE (Conversational Recommender System framEwork), for building chatbots that can recommend items from different domains and interact with the user through natural language. Since a CoRS architecture is generally composed of different elements, we performed an in-vitro experiment with two synthetic datasets, to investigate the impact that each component has on the CoRS in terms of recommendation accuracy. Additionally, an in-vivo experiment was carried out to understand how natural language influences both the cost of interaction and recommendation accuracy of a CoRS. Experimental results have revealed the most critical components in a CoRS architecture, especially in cold-start situations, and the main issues of the natural-language-based interaction. All the dialogues have been collected in a public available dataset.
Five Years at the Edge: Watching Internet From the ISP Network

The Internet and the way people use it are constantly changing.Knowing traffic is crucial for operating the network, understanding users' needs, and ultimately improving applications.Here, we provide an in-depth longitudinal view of Internet traffic during 5 years (from 2013 to 2017).We take the point of the view of a national-wide ISP and analyze rich flow-level measurements to pinpoint and quantify changes.We observe the traffic, both from a point of view of users and services.We show that an ordinary broadband subscriber downloaded in 2017 more than twice as much as they used to do 5 years before.Bandwidth hungry video services drove this change at the beginning, while recently social messaging applications contribute to increase of data consumption.We study how protocols and service infrastructures evolve over time, highlighting events that may challenge traffic management policies.In the rush to bring servers closer and closer to users, we witness the birth of the sub-millisecond Internet, with caches located directly at ISP edges.The picture we take shows a lively Internet that always evolves and suddenly changes.To support new analyses, we make anonymized data available at https://smartdata.polito.it/five-years-at-the-edge/.
Semantic-aware scene recognition

Scene recognition is currently one of the top-challenging research fields in computer vision. This may be due to the ambiguity between classes: images of several scene classes may share similar objects, which causes confusion among them. The problem is aggravated when images of a particular scene class are notably different. Convolutional Neural Networks (CNNs) have significantly boosted performance in scene recognition, albeit it is still far below from other recognition tasks (e.g., object or image recognition). In this paper, we describe a novel approach for scene recognition based on an end-to-end multi-modal CNN that combines image and context information by means of an attention module. Context information, in the shape of a semantic segmentation, is used to gate features extracted from the RGB image by leveraging on information encoded in the semantic representation: the set of scene objects and stuff, and their relative locations. This gating process reinforces the learning of indicative scene content and enhances scene disambiguation by refocusing the receptive fields of the CNN towards them. Experimental results on three publicly available datasets show that the proposed approach outperforms every other state-of-the-art method while significantly reducing the number of network parameters. All the code and data used along this paper is available at: https://github.com/vpulab/Semantic-Aware-Scene-Recognition
Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality?

As a result of the worldwide health care system digitalization trend, the produced healthcare data is estimated to reach as much as 2314 Exabytes of new data generated in 2020. The ongoing development of intelligent systems aims to provide better reasoning and to more efficiently use the data collected. This use is not restricted retrospective interpretation, that is, to provide diagnostic conclusions. It can also be extended to prospective interpretation providing early prognosis. That said, physicians who could be assisted by these systems find themselves standing in the gap between clinical case and deep technical reviews. What they lack is a clear starting point from which to approach the world of machine learning in medicine. This article aims at providing interested physicians with an easy-to-follow insight of Artificial Intelligence (AI) and Machine Learning (ML) use in the medical field, primarily over the last few years. To this end, we first discuss the general developmental paths concerning AI and ML concept usage in healthcare systems. We then list fields where these technologies are already being put to the test or even applied such as in Hematology, Neurology, Cardiology, Oncology, Radiology, Ophthalmology, Cell Biology and Cell Therapy.