Reviewerly/exHarmony huggingface dataset:
ARET-IQA: An Aspect-Ratio-Embedded Transformer for Image Quality Assessment

Image quality assessment (IQA) aims to automatically evaluate image perceptual quality by simulating the human visual system, which is an important research topic in the field of image processing and computer vision. Although existing deep-learning-based IQA models have achieved significant success, these IQA models usually require input images with a fixed size, which varies the perceptual quality of images. To this end, this paper proposes an aspect-ratio-embedded Transformer-based image quality assessment method, which can implant the adaptive aspect ratios of input images into the multihead self-attention module of the Swin Transformer. In this way, the proposed IQA model can not only relieve the variety of perceptual quality caused by size changes in input images but also leverage more global content correlations to infer image perceptual quality. Furthermore, to comprehensively capture the impact of low-level and high-level features on image quality, the proposed IQA model combines the output features of multistage Transformer blocks for jointly inferring image quality. Experimental results on multiple IQA databases show that the proposed IQA method is superior to state-of-the-art methods for assessing image technical and aesthetic quality.
Fast Joint Multi-Robot Trajectory Optimization by GPU Accelerated Batch Solution of Distributed Sub-Problems

We present a joint multi-robot trajectory optimizer that can compute trajectories for tens of robots in aerial swarms within a small fraction of a second. The computational efficiency of our approach is built on breaking the per-iteration computation of the joint optimization into smaller, decoupled sub-problems and solving them in parallel through a custom batch optimizer. We show that each of the sub-problems can be reformulated to have a special Quadratic Programming structure, wherein the matrices are shared across all the problems and only the associated vector varies. As result, the batch solution update rule reduces to computing just large matrix vector products which can be trivially accelerated using GPUs. We validate our optimizer's performance in difficult benchmark scenarios and compare it against existing state-of-the-art approaches. We demonstrate remarkable improvements in computation time its scaling with respect to the number of robots. Moreover, we also perform better in trajectory quality as measured by smoothness and arc-length metrics.
Feature Extraction Based Deep Indexing by Deep Fuzzy Clustering for Image Retrieval Using Jaro Winkler Distance

Abstract Image retrieval aims to locate similar image based on given query from large-sized image dataset. In the recent decades, different methods are developed to retrieve similar images, but increasing the retrieval performance is a challenging task. Hence, an effective method named Lion Henry Gas Solubility Optimization-based Deep Fuzzy Clustering (LHGSO-based DFC) is developed to increase the retrieval efficiency of image retrieval. Based on the features extraction, images are indexed efficiently to the corresponding cluster using clustering model. The proposed method retrieves the relevant images with respect to query image based on the similarity. The similarity measure is computed using Jaro Winkler distance, in which the images corresponding to the cluster that have higher similarity measure is retrieved more effectively. The proposed method achieved higher performance in terms of the metrics, like F-measure, precision and recall with the values of 0.887, 0.891 and 0.882.
Spam Email Classification by Hybrid Feature Selection with Advanced Machine learning Algorithm – Future Perspective

Recently, email has become a common way for people to communicate and share information both officially and personally. Email may be used by spammers to transmit harmful materials to Internet users. The data must be protected from unauthorized access, which necessitates the development of a reliable method for identifying spam emails. As a result, a variety of solutions have been devised. An innovative hybrid machine learning strategy for effectively detecting spam emails has been discussed in this study. This means that identifying spam and non-spam email is a difficult process. Spam email categorization has undergone a significant evolution in recent years, as shown by the research given below. For locating spam, this study uses a mixed approach. Different email categorization algorithms are used to rank them for future perspective.
Computing performance requirements for web service compositions

In order to produce service compositions, modern web applications now combine both in-house and third-party web services. Therefore, their performance depends on the performance of the services that they integrate. At early stages, it may be hard to quantify the performance demanded from the services to meet the requirements of the application, as some services may not be available or may not provide performance guarantees. The authors present several algorithms that compute the required performance for each service from a model of a service composition at an early stage of development. This is also helpful when testing service compositions and selecting candidate web services, enabling performance-driven recommendation systems for web services that could be integrated into service discovery. Domain experts can annotate the model to include partial knowledge on the expected performance of the services. We develop a throughput computation algorithm and two time limit computation algorithms operating on such a model: a baseline algorithm, based on linear programming, and an optimised graph-based algorithm. We conduct theoretical and empirical evaluations of their performance and capabilities on a large sample of models of several classes. Results show that the algorithms can provide an estimation of the performance required by each service, and that the throughput computation algorithm and the graph-based time limit computation algorithm show good performance even in models with many paths.
RESEKRA: Remote Enrollment Using SEaled Keys for Remote Attestation

This paper presents and implements a novel remote attestation method to ensure the integrity of a device applicable to decentralized infrastructures, such as those found in common edge computing scenarios. Edge computing can be considered as a framework where multiple unsupervised devices communicate with each other with lack of hierarchy, requesting and offering services without a central server to orchestrate them. Because of these characteristics, there are many security threats, and detecting attacks is essential. Many remote attestation systems have been developed to alleviate this problem, but none of them can satisfy the requirements of edge computing: accepting dynamic enrollment and removal of devices to the system, respecting the interrupted activity of devices, and last but not least, providing a decentralized architecture for not trusting in just one Verifier. This security flaw has a negative impact on the development and implementation of edge computing-based technologies because of the impossibility of secure implementation. In this work, we propose a remote attestation system that, through using a Trusted Platform Module (TPM), enables the dynamic enrollment and an efficient and decentralized attestation. We demonstrate and evaluate our work in two use cases, attaining acceptance of intermittent activity by IoT devices, deletion of the dependency of centralized verifiers, and the probation of continuous integrity between unknown devices just by one signature verification.
Detecting relevant app reviews for software evolution and maintenance through multimodal one-class learning

Mobile app reviews are a rich source of information for software evolution and maintenance. Several studies have shown the effectiveness of exploring relevant reviews in the software development lifecycle , such as release planning and requirements engineering tasks. Popular apps receive even millions of reviews, thereby making manual extraction of relevant information an impractical task. The literature presents several machine learning approaches to detect relevant reviews. However, these approaches use multi-class learning, implying more user effort for data labeling since users must label a significant set of relevant and irrelevant reviews. This article investigates methods for detecting relevant app reviews considering scenarios with small sets of labeled data. We evaluated unimodal and multimodal representations, different labeling levels, as well as different app review domains and languages. We present a one-class multimodal learning method for detecting relevant reviews. Our approaches have two main contributions. First, we use one-class learning that requires only the labeling of relevant app reviews, thereby minimizing the labeling effort. Second, to handle the smaller amount of labeled reviews without harming classification performance, we also present methods to improve feature extraction and reviews representation. We propose the Multimodal Autoencoder and the Multimodal Variational Autoencoder. The methods learn representations which explore both textual data and visual information based on the density of the reviews. Density information can be interpreted as a summary of the main topics or clusters extracted from the reviews. Our methods achieved competitive results even using only 25% of labeled reviews compared to models that used the entire training set. Also, our multimodal approaches obtain the highest F 1 -Score and AUC-ROC in twenty-three out of twenty-four scenarios. Our one-class multimodal methods proved to be a competitive alternative for detecting relevant reviews and promising for practical scenarios involving data-driven software evolution and maintenance.
Using Augmented Reality (AR) to Educate the Student: Bridging the Communication for Internet Addiction

Internet gaming addiction is now recognized as a mental health condition that can have a significant impact on an individual's well-being. Over the last decade, studies have accumulated showing that overuse of the Internet can lead to the development of behavioral addiction. Meanwhile, technological advances have introduced virtual and augmented environment, which allows developer to reduce the time and cost of product development iterations while improving the quality of final products, of course bringing user with richness and immersiveness with large scale environment. It opens up new media and approaches to the gaming experience and online browsing activities. The implications means that immersive technology promises higher design fidelity, resulting in higher quality final products at a lower cost than traditional prototyping technology can offer. Thus, this study want to explore in the qualitative approach on how the design in term of augmented reality product influence the student's behavior in the educational purposes.
First-photon target detection: Beating Nair's pure-loss performance limit

In 2011, Nair published a no-go theorem for quantum radar target detection [Phys. Rev. A {\bf 84}, 032312 (2011)]. He showed, under fairly general assumptions, that a coherent-state radar's error probability was within a factor of two of the best possible quantum performance for the pure-loss (no background radiation) channel whose roundtrip radar-to-target-to-radar transmissivity $\kappa$ satisfies $\kappa \ll 1$. We introduce first-photon radars (FPRs) to circumvent and beat Nair's performance limit. FPRs transmit a periodic sequence of pulses, each containing $N_S$ photons on average, and perform ideal direct detection (photon counting at unit quantum efficiency and no dark counts) on the returned radiation from each transmission until at least one photon has been detected or a pre-set maximum of $M$ pulses has been transmitted. They decide a target is present if and only if they detect one or more photons. We consider both quantum (each transmitted pulse is a number state) and classical (each transmitted pulse is a coherent state) FPRs, and we show that their error-probability exponents are nearly identical when $\kappa \ll 1$. With the additional assumption that $\kappa N_S \ll 1$, we find that their advantage in error-probability exponent over Nair's performance limit grows to 3 dB as $M \rightarrow \infty$. However, because FPRs' pulse-repetition period must exceed the radar-to-target-to-radar propagation delay, their use in standoff sensing of moving targets will likely employ $\kappa N_S \sim 1$ and $M \sim 10$ and achieve ~2 dB advantage. Our work constitutes a new no-go theorem for quantum radar target detection.
A deep variational convolutional Autoencoder for unsupervised features extraction of ceramic profiles. A case study from central Italy

The need for a quantitative approach to the morphologic study of ceramics is becoming increasingly evident. Ceramics are the most common material in many archaeological sites and a huge amount of data has accumulated over time. This data can be handled by Machine Learning algorithms which are rapidly gaining popularity in archaeology. Although most approaches can be referred to classification tasks, in this contribution a particular type of Neural Network is proposed for feature extraction from archaeological ceramics. Through this proposed method it is possible to gain a numerical weighted representation of the pottery profile that can be used for multivariate analyses. The case study will focus on regionalisation processes in pottery production between the end of the 2nd millennium BC and the first half of the 1st millennium BC in central Tyrrhenian Italy, a period that saw major transformations in the cultural and socio-political structure of Ancient Italy. The results seem to confirm the regionalisation hypothesis and offer interesting insights into the quantitative study of archaeological ceramics.
Automating the design and development of gradient descent trained expert system networks

Prior work introduced a gradient descent trained expert system that conceptually combines the learning capabilities of neural networks with the understandability and defensible logic of an expert system. This system was shown to be able to learn patterns from data and to perform decision-making at levels rivaling those reported by neural network systems. The principal limitation of the approach, though, was the necessity for the manual development of a rule-fact network (which is then trained using backpropagation). This paper proposes a technique for overcoming this significant limitation, as compared to neural networks. Specifically, this paper proposes the use of larger and denser-than-application need rule-fact networks which are trained, pruned, manually reviewed and then re-trained for use. Multiple types of networks are evaluated under multiple operating conditions and these results are presented and assessed. Based on these individual experimental condition assessments, the proposed technique is evaluated. The data presented shows that error rates as low as 3.9% (mean, 1.2% median) can be obtained, demonstrating the efficacy of this technique for many applications.
A coin selection strategy based on the greedy and genetic algorithm

Abstract Coin selection method refers to the process undergone when selecting a set of unspent transaction outputs (UTXOs) from a cryptocurrency wallet or account to use as inputs in each transaction. The most applied coin selection method that UTXO-based cryptocurrencies currently employ is an algorithm that decides on a certain set of UTXOs that matches the target amount and limits the transaction fee. However this approach trades off favourable maintenance overhead of the entire network for low transaction fees, as many low-value UTXOs known as “dust” is produced. Over time, this will impact the scalability and management of the cryptocurrency network as the global set of UTXOs become larger. Therefore, there is an urgency to find a higher-performing coin selection method suitable for UTXO-based cryptocurrencies. This paper proposes a method based on the greedy and genetic algorithm for effectively choosing sets of UTXOs in Bitcoin. The main objective of this coin selection strategy is to get as close as possible to the target while also maintaining and possibly reducing the number of UTXO inputs.
A Hybrid PSO-ACO Algorithm to Facilitate Software Project Scheduling

While tailoring software applications, in the present day scenario, there is a huge demand for cost effective products however it is so challenging for the developers on how to make a trade-off between cost and quality as well as between quality and time. Late delivery, sometimes, has become inevitable due to the reasons like: improper time schedules, technical issues, changing customer requirements, miscommunication amongst team members, failure to analyse the risks involved, and underrating of resources like man cost, power, effort, etc. Here in this work, an attempt has been made to study the advantages of swarm based optimization techniques over traditional optimization approaches and thereby employ them efficiently to schedule projects. A hybrid PSO-ACO algorithm is proposed for scheduling purposes. The nuances of natural swarm cultures are appropriately incorporated in to the software scheduling scenario and the results are recorded and reported. It is observed that the readings are passible and are bench marked through relevant measures of quality.
An Intelligent and Fast Dance Action Recognition Model Using Two-Dimensional Convolution Network Method

In the field of computer vision, action recognition is a very difficult topic to study. This paper suggests a dance movement recognition method based on DL network in accordance with the characteristics of dance movements. The backbone network in this study is a thin network called Mobile Net. The two-dimensional convolution network, which can only extract spatial features, can extract and fuse time domain features and use them for dance movement recognition by combining the time domain modelling strategy of time domain feature transfer between convolution layers. It uses fewer network parameters and less computation than the original multitarget detection model. Using the clustering method to preset the prior frames of human detection with various sizes and numbers also enhances the model's performance. Finally, the experimental findings demonstrate that the algorithm suggested in this paper outperforms the Incision v3 algorithm in F1 by 9.87 percent and outperforms the traditional CNN algorithm in identification accuracy by 6.51 percent and 10.76 percent, respectively. It is evident that the algorithm used in this paper reduces running time and, to a certain extent, improves the accuracy of dance movement recognition. For related research, it offers some references.
Improved constrained social network rating-based neural network technique for recommending products in E-commerce environment

In the modern world, the essentiality in the utilization of the e-commerce contents like movies, music and electronic goods becomes indispensable with diversified items searched over the internet. The relevant results of the items search are made feasible through the enforcement of filtering techniques since it determines relevant data for recommendation of an item. A diversified number of filtering schemes are available of filtering the data instead of accessing each data available on the internet for deriving associated results. The data access and efficiency, the process of identifying relevant results based on users’ preferences is challenging task. In this paper, the proposed Constrained Social Network Rating-based Neural Network Technique (CSNR-NNT) is presented with the key significances and implementation processes. This proposed CSNR-NNT significantly concentrates on the exploration of trustee information that aids in social content persuading selection process for facilitating superior recommendation. The proposed CSNR-NNT scheme utilized the benefits of neural learning for ensuring recommendation through the incorporation of distrust and trustee relation. This proposed CSNR-NNT scheme also aids in categorizing the positive and negative recommendation of the trustee based on the process of the prediction.
Towards ontological interoperability of cognitive IoT agents based on natural language processing¶

The interoperability of devices from distinct brands on the Internet of Things (IoT) domain is still an open issue. The main reason is that pioneer companies always deliberately neglected to deploy devices able to interoperate with competitors products. The key factors that may invert such a trend derive, on one hand, from the abstraction of communication protocols that facilitates the migration from vertical to horizontal paradigms and, on the other hand, from the introduction of common and shared ontologies encoding devices specifications. The Semantic Web, with all its layers, can be considered the main framework for delivering ontologies, and by virtue of its features, it is surely the ideal means for providing shared knowledge. In this paper we present a framework that instantiates cognitive agents operating in IoT context, endowed with meta-reasoning in the Semantic Web. The framework, called SW-Caspar, is also provided with a module that performs semi-automatic ontology learning from sentences expressed in natural language; such a learning process generates a conceptual space reflecting the domain of discourse with an instance of a novel foundational ontology called Linguistic Oriented Davidsonian Ontology (LODO), whose main feature is to increase the deepness of reasoning without compromising linguistic-related features. LODO is inspired by the First-Order Logic Davidsonian notation and is serialized in OWL 2. Well-known examples derived from the theory of logical reasoning and a case-study applied to automation on health scenarios are also provided.
Deep Transfer Learning Based Real Time Fitness Movement Identification

Sports are full of people’s lives, and regular exercise has become an indicator of people’s health. Due to the high price, most people who exercise at home will not hire fitness trainers, but learn about fitness through media communities. This is likely to lead to the wrong posture of fitness, which can lead to injury. A cheap, simple, and accurate fitness recognition system could increase fitness awareness. This paper proposes a deep transfer learning method that uses Yolov4 to classify fitness movements, which can instantly recognize fitness movements with only one network camera. We built a database, which contains 20 users and online fitness photos, a total of 16302 images, including 12 kinds of fitness movements. 10 user and online photos are used to train Yolov4, and another 10 user photos are used for testing. In the experiment based on Yolov4 to detect fitness, mAP is 99.71%, Precision is 97.9%, Recall is 98.56%, and F1-score is 98.23%. The results show that fitness movements can be detected accurately and quickly using this method.
Semantic Cluster Operator for Symbolic Regression and Its Applications

In this paper, a novel operator, semantic cluster operator, was developed to overcome the low convergence performance of genetic programming in symbolic regression. The main strategy for steep convergence was to narrow search space and scrutinize the narrowed search space using a semantic cluster library. To demonstrate the success of this idea, the computation time and offspring fitness of the operator developed in this paper were compared with those of exhaustive search. The computation time of the operator was approximately 6% of that of the exhaustive search, and its offspring fitness was in the top 0.5% among all offspring derived from the exhaustive search. In two application problems, derived models from an algorithm using the operator showed high prediction accuracy comparable to an artificial neural network, random forest, and support vector machine despite its simplicity.