Reviewerly/exHarmony huggingface dataset:
Product and Content Management Through QR Codes as an Efficient Strategy in E-commerce

New technologies have changed the way in how products are offered today. In this sense, the Internet and social networks have played a preponderant role in spreading any product more efficiently and dynamically. Simultaneously, having a smartphone and a high-speed wireless network is no longer a luxury or a temporary fad but rather necessary for new generations. These technological advances and new marketing trends have not gone unnoticed by medium and large stores. In this context, e-commerce is a modality that allows adding these new technologies to sell products and services through the Internet, making it a tool that allows discovering new uses, forms, and consumption habits. E-commerce allows customers to provide products and services with the highest quality; for this, it is necessary to implement QR codes capable of creating a differentiating user experience where products are displayed in the best possible way and accompany them of content valuable to consumers. In this way, it is intended to promote the efficient and rationalized use of all the resources that companies have and at the same time promote the use of new technologies as a strategic enabler and competitive differentiator so that they exceed their business objectives.
Analysis of Texture Feature Extraction Technique in Image Processing

Texture feature is one of the most common image segmentation, classification, extraction, and surface analysis techniques. In many image processing tasks, texture plays a vital role. The texture is defined by a neighborhood’s spatial distribution of the gray level. For a point, however, texture cannot be described. The resolution at which an image is presented defines the perceived size of the texture. Image texture provides us with details on the color or intensity spatial arrangement in a picture or area chosen for a picture. Textures of images can be created artificially or found in natural scenes captured in an image. Nowadays, Gabor filtering has been widely used for texture feature extraction. In our paper, we presented a well-ordered extraction method of two-dimensional texture features and a comparison study between Gabor filer and Log Gabor filter. First, we converted the image to the gray level. Then on each converted part of the gray-level image, a two-dimensional Log Gabor filter with different frequencies decomposed with the SVD algorithm is applied to extract suitable distinctive texture information. We used SVD’s unique values as a function vector to test the output of the proposed model. We used the Naïve Bayes classifier to train and test my experimental dataset for classifiers. We did the same things with Gabor filter and found a lower accuracy rate than Log Gabor filter.
Recognizer Cloning Attack on Image Recognition Services and Its Defending Method

Nowadays, several image recognition services are provided on the cloud, where users send an image as a query while the server recognizes the image and returns its recognition result. These services might suffer from “recognizer cloning attacks (RCA)” in the future. The overview of the RCA is as follows. First, an attacker sends a lot of unlabeled images to the server and receives their recognition results, which are given as a form of class labels. Next, the attacker exploits the class labels together with the unlabeled images as a training dataset, which allows the attacker to train a new recognizer. This means he can maliciously steal or clone the server’s original recognition model because the new recognizer has the almost same function as the server’s one. In this sense, we refer to the new recognizer as a “cloned recognizer (CR)”. In this chapter, we first show that RCA is performable even by the attacker who has less knowledge of the server’s recognition model. Then we propose a defending method against RCA; more specifically, we propose a method to detect CRs by analyzing their characteristics. We achieved a CR detection accuracy of more than 85% in our experiments.
Artificial intelligence and blockchain: Implementation perspectives for healthcare beyond 5G

Due to its efficiency in a wide range of domains, blockchain has become an emerging technology in the last decade. Artificial intelligence (AI) as well as the Internet of Things are also rising at a rapid pace. The convergence of both these technologies can open the door to numerous possibilities. In this chapter, we present an in-depth survey of the implementation perspectives of beyond 5G integrated blockchain and AI applications in healthcare. Several possibilities for overcoming present constraints in healthcare systems utilizing blockchain technology, including frameworks and methods to measure such systems’ effectiveness, are addressed in this study. Various applications of blockchain in the healthcare sector along with a recent scenario-based case study have been highlighted. A probable future direction to further research in the area of AI technique-induced blockchain beyond 5G has been presented also.
Intelligent agents

Continuous advances in biological sequences, transcriptional and structural data, interactions, and genetics have led to the production of massive amounts of data that require the adoption of applications and integrated tools for unsupervised automated analysis of this continuous growing flow of biological data. Intelligent agents can support bioinformaticians in simulating and modeling biological systems, as well as supporting the automation of information gathering and information analysis processes. In this sense, an agent is an intelligent, flexible, and autonomous software tool with problem-solving skills. Agents are high-level software abstractions providing a practical and powerful way to describe a complex software entity regarding its behavior within a contextual computational environment. Thus, agents can perform actions without user mediation by maintaining a description of their processing state and the outer environment in which they are situated. This chapter introduces how to define an intelligent agent according to the FIPA (Percepts, Actions, Goals, and Environment) specifications and how to provide it with the necessary independence to deal with various problems through perception and action.
Convolutional Transformer Network for Fine-Grained Action Recognition

Fine-grained action recognition is one of the critical problems in video processing, which aims to recognize similar actions of subtle interactions between humans and objects. Inspired by the remarkable performance of Transformer in natural language processing, Transformer has been applied to the fine-grained action recognition task. However, Transformer needs abundant training data and extra supervision to achieve comparable results with convolutional neural networks (CNNs). To address these issues, we propose a Convolutional Transformer Network (CTN), which integrates the merits of CNN ( e.g. , sharing weights, capturing low-level features in videos and locality) and the benefits of Transformer ( e.g ., dynamic attention and learning long-range dependencies). In this paper, we propose two modifications to the original Transformer: (i) we propose a video-to-tokens module that can extract tokens from extracted spatial-temporal features in videos by 3D convolutions instead of the direct token embedding from raw input video clips; (ii) we completely replace the linear projection in multi-head self-attention layer with 3D depth-wise convolutional projection, which applies a 3D depth-wise separable convolution operation on 3D embedded token maps. With these two modifications, our approach can extract the effective spatial-temporal features from videos and process the long sequences of tokens encountered in videos. Experimental results demonstrate that our proposed CTN can achieve state-of-the-art accuracy on two fine-grained action recognition datasets ( i.e ., Epic-Kitchens and Diving 48) with a small computational increase.
Adversarial Scrutiny of Evidentiary Statistical Software

The U.S. criminal legal system increasingly relies on software output to convict and incarcerate people. In a large number of cases each year, the government makes these consequential decisions based on evidence from statistical software—such as probabilistic genotyping, environmental audio detection, and toolmark analysis tools—that defense counsel cannot fully cross-examine or scrutinize. This undermines the commitments of the adversarial criminal legal system, which relies on the defense’s ability to probe and test the prosecution’s case to safeguard individual rights.Responding to this need to adversarially scrutinize output from such software, we propose robust adversarial testing as an audit framework to examine the validity of evidentiary statistical software. We define and operationalize this notion of robust adversarial testing for defense use by drawing on a large body of recent work in robust machine learning and algorithmic fairness. We demonstrate how this framework both standardizes the process for scrutinizing such tools and empowers defense lawyers to examine their validity for instances most relevant to the case at hand. We further discuss existing structural and institutional challenges within the U.S. criminal legal system that may create barriers for implementing this and other such audit frameworks and close with a discussion on policy changes that could help address these concerns.
Persuasive Mobile NOW Interactions

This paper addresses the challenge of increasing adherence to self-report questionnaires by introducing a mobile communication technique called NOW Interactions. NOW Interactions persuades people to provide bits of information in a moment, at the right time, with minimal interruption of their current activity, and makes it easier for users to respond than to dismiss an information request, without opening an application. NOW Interactions is based on principles from interaction design, microinteractions, and persuasive design. Timely and smooth interactions are ensured by taking into account the process that users go through when providing information. We illustrate NOW Interactions in a functional prototype that aims to reinforce the quality and reliability in e-healthcare solutions by innovating the methods used to gather information through self-report questionnaires. Results from a pilot study confirm the need for innovation of self-report questionnaires, indicate potential for NOW Interactions, and suggest the need for further research on NOW Interactions to test interplay with sensors, authoring tools, and integration in health applications.
Extracting and Re-mapping Narrative Text Structure Elements Between Languages Using Self-supervised and Active Few-Shot Learning

Transcreators extract crucial information from text written in one language for a specific media type and translate this text into a different language and a different media type. Multiple factors drive changes in narrative structures in different languages for different media platforms. AI-based approaches can be used to extract critical information elements from text and augment human analysis and insight to facilitate transcreation. In this study, we apply self-supervised learning and active few-shot learning based on generative pretrained transformer models (e.g. GPT-N) to perform information extraction. We also used Wikifier ( https://wikifier.org/ ) to annotate the related text with links to relevant Wikipedia concepts to augment human users with additional explanations. The performance statistics were collected using four news stories, and the results show that self-supervised approach is error-prone because the GPT-3 pretrained language model can generate synthetic information based on patterns learned from its huge training corpus instead of reflecting only relevant facts in the prompted text. On the other hand, active few-shot learning worked very well with 87.5% accuracy on the experimental examples. Wikifier also provides a large number of correct and useful links to named entities such as human names, locations, organizations, and concepts. Transcreators can leverage these AI tools to augment their ability to effectively perform their tasks.
Programming Legal Contracts

We discuss the design principles of Stipula, a domain specific language that can assists lawyers in programming legal contracts through specific software patterns. The language is based on a small set of primitives, that precisely correspond to the distinctive elements of legal contracts, and that are amenable to be prototyped on both centralized or distributed systems. We also outline two formal techniques to reason about Stipula contracts: a type inference system that allows to derive types for fields, assets and contract’s functions, and an analyzer of liquidity that pinpoints those contracts that do not freeze any asset forever.
Hybrid Cloud Security by Revocable KUNodes-Storage with Identity-Based Encryption

Cloud storage is a service involving cloud service providers providing storage space to customers.Cloud storage services have numerous advantages, including convenience, high computation, and capacity, thereby attracting users to outsource data in the cloud.However, users outsource data directly via cloud stage services that are unsafe when outsourcing data is sensitive for users.Therefore, cipher text-policy attribute-based encryption is a promising cryptographic solution in a cloud environment, and can be drawn up for access control by data owners (DO) to define access policy.Unfortunately, an outsourced architecture applied with attribute-based encryption introduces numerous challenges, including revocation.This issue is a threat to the data security of DO.Furthermore, highly secure and flexible cipher text-based attribute access control with role hierarchy user grouping in cloud storage is implemented by extending the KUNodes (revocation) storage identity-based encryption.Result is evaluated using Cloudsim, and our algorithm outperforms in terms of computational cost by consuming 32 MB for 150-MB files.
Path Planning and Guidance Laws of a Formula Student Driverless Car

Autonomous driving has been a topic of great interest in several areas, from which motor racing is no exception. Aiming to autonomously control the future Formula Student Lisboa vehicle, this work is developed with the objective of implementing different strategies for control and path planning, with the purpose of minimising race lap times. These strategies are tested in simulation, using a realistic model of the prototype. The approach followed involves the decoupling of the lateral and longitudinal subsystems, where the reference path is obtained using artificial potential fields and then combined with a two passes algorithm developed to generate a speed profile. This allows to obtain a sub-optimal solution that adequately portrays the expected behaviour of a human driver while respecting traction conditions.
Sets of Wide-Gap Frequency-Hopping Sequence With Optimal Maximum Hamming Correlation

In application, frequency-hopping (FH) communication system often suffers various interferences, such as single frequency narrow-band interference, partial band blocking interference and tracking interference, and so on. For all that, by using optimal Wide-Gap (WG) FH sequences, FH communication system can significantly improve the anti-interference performances. In this paper, the relations betweenWG FH sequence theoretic bounds published in the journal IEEE Access (Peihua Li et al., 2019) are first made clear, and then five types of generalized methods are presented to design new classes of WG FH sequence sets. It is shown that all designed WG FH sequence sets are optimal according to the bound derived by Peihua Li et al. And by selecting appropriate original sequence sets, many of the optimal WG FH sequence sets can be obtained by our methods. Most importantly, these WG FH sequence sets have new parameters that are not covered in the literature.
Learning from Small Medical Data - Robust Semi-Supervised Cancer Prognosis Classifier with Bayesian Variational Autoencoder

Background: Cancer is one of the world’s leading mortality causes, and its prognosis is complex and hard to predict due to complicated biological interactions among heterogeneous data types. When applying deep learning models in this field, numerous challenges arise, including missing values, censorship, high dimensionality, and especially, a small sample size. They all prevent researchers from using more powerful neural network-based models for better prediction accuracy.Methods: We propose a robust Semi-supervised Cancer prognosis classifier with bAyesian variational autoeNcoder (SCAN) as a structured machine learning framework for cancer prognosis prediction that can take full advantage of the preciously available patient data. SCAN incorporates advanced semi-supervised learning techniques for predicting five-year disease-specific survival and overall survival in breast and non-small cell lung cancer (NSCLC) patients, respectively. Prognostic gene biomarkers were selected through our systems biology feature selector. In addition, the clinical features were utilized to build the model.Findings: With the introduction of semi-supervised and ensemble learning approaches, SCAN achieved significantly better AUROC scores than all existing benchmarks (81·73% for breast cancer; 80·46% for NSCLC), including our previously proposed bimodal neural network classifiers (77·71% for breast cancer; 78·67% for NSCLC). Furthermore, comprehensive analyses verified that SCAN achieved robust performance for different cancer types. Independent validation results showed that SCAN still achieved better AUROC scores (74·74% for breast and 72·80% for NSCLC) than the bimodal neural network classifiers (64·13% for breast and 67·07% for NSCLC) and benchmark machine learning models.Interpretation: SCAN is general and can easily adapt to more patient data types without retraining the whole network. It can also be applied as distributed feature learning in federated learning. This paves the foundation for personalized medicine and provides a useful tool for early cancer risk screening.Funding Information: This research is sponsored by the Ministry of Technology and Science (MOST), Taiwan. The grant number is MOST 110-2221-E-002-112-MY3.Declaration of Interests: The authors declare no competing interests.
Achieving AI-Enabled Robust End-to-End Quality of Experience Over Backhaul Radio Access Networks

Emerging applications such as Augmented Reality, the Internet of Vehicles and Remote Surgery require both computing and networking functions working in harmony. The End-to-end (E2E) quality of experience (QoE) for these applications depends on the synchronous allocation of networking and computing resources. However, the relationship between the resources and the E2E QoE outcomes is typically stochastic and non-linear. In order to make efficient resource allocation decisions, it is essential to model these relationships. This article presents a novel machine-learning based approach to learn these relationships and concurrently orchestrate both resources for this purpose. The machine learning models further help make robust allocation decisions regarding stochastic variations and simplify robust optimization to a conventional constrained optimization. When resources are insufficient to accommodate all application requirements, our framework supports executing some of the applications with minimal degradation (graceful degradation) of E2E QoE. We also show how we can implement the learning and optimization methods in a distributed fashion by the Software-Defined Network (SDN) and Kubernetes technologies. Our results show that deep learning-based modeling achieves E2E QoE with approximately 99.8% accuracy, and our robust joint-optimization technique allocates resources efficiently when compared to existing differential services alternatives.
Symmetric Key Cryptography

In general terms, a cryptosystem is a system of the form $$\displaystyle \langle {\mathcal M},{\mathcal C},e,d,{\mathcal K}_e,{\mathcal K}_d\rangle $$ where $${\mathcal M}$$ is the message space, $${\mathcal C}$$ is the space of all possible cryptograms, e is the encryption transformation, d is the decryption transformation, $${\mathcal K}_e$$ is the encryption keyspace, and $${\mathcal K}_d$$ is the decryption keyspace.
Transparent Treatment of for-Loops in Proofs

Indexed loop scopes have been shown to be a helpful tool in creating sound loop invariant rules in dynamic logic for programming languages with abrupt completion, such as Java. These rules do not require program transformation of the loop body, as other approaches to dealing with abrupt completion do. However, indexed loop scopes were designed specifically to provide a loop invariant rule for while loops and work rather opaquely. Here we propose replacing indexed loop scopes with a more transparent solution, which also lets us extend this idea from while loops to for loops. We further present sound loop unrolling rules for while, do and for loops, which require neither program transformation of the loop body, nor the use of nested modalities. This approach allows for loops to be treated as first-class citizens in proofs – rather than the usual approach of transforming for loops into while loops – which makes semi-automated proofs more transparent and easier to follow for the user, whose interactions may be required in order to close the proofs.
Getting started with self-driving planning

This chapter focuses on the planning layer of autonomous vehicle systems, which consists of three layers: task planning, behavior planning, and motion planning. Task planning is also called path planning. In task planning, the A∗ algorithm, as a discrete space search algorithm, is primarily used to solve the problem of optimal path search in discrete space. Behavior planning is a core part of behavior planning for autonomous vehicle systems. The finite state machine (FSM) and hierarchical FSM are often used to design a behavioral decision model. To make the motion planning easy to understand, we illustrate a series of methods for spline interpolation.
Convolutional Neural Networks: Architecture Optimization and Regularization

Since its invention, convolutional neural network (CNN) has revolutionized the artificial intelligence field. Furthermore, its development has become a very active research area. This is due to its unavoidable achievements in most real world applications. However, the determination of an optimal CNN architecture is still unclear until now. Furthermore, the choice of CNN architectures previously proposed is based only on experiments. To deal with this issue, we suggest, in this work, a mathematical modelling for CNN Architecture Optimization and training in terms of a mixed integer nonlinear optimization problem with linear constraints. We adopt the genetic algorithm to solve the resulting model. To further evaluate our suggested mathematical modeling, we conduct various experiments on two benchmark datasets. Numerical results confirm that our proposal achieves improvement in terms of optimization and classification performance.