Reviewerly/exHarmony huggingface dataset:
Applying declarative analysis to industrial automotive software product line models

Program analysis of automotive software has several unique challenges, including that the code base is ultra large, comprising over a hundred million lines of code running on a single vehicle; the code is structured as a software product line (SPL) for managing a family of related software products from a common set of artifacts; and the analysis results (despite being numerous and despite being variable) need to be presented to the engineer in a way that is manageable. In previous work, we reported on lifting declarative analyses to apply to a software product line, rather than to an individual product variant. This paper reports on milestone results from applying lifted declarative analyses (behaviour alteration, recursion analysis, simplifiable global variable analysis, and two of their variants) to automotive software product lines from General Motors and assessing the scalability of the analyses and the effectiveness of reporting to engineers conditional analysis results (i.e., results conditioned on SPL program variants). We also reflect on some of the lessons learned throughout this project.
Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

The outbreak of the pandemic, caused by Coronavirus Disease 2019 , has affected the daily activities of people across the globe.During COVID-19 outbreak and the successive lockdowns, Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously.Several studies used Sentiment Analysis (SA) to analyze the emotions expressed through tweets upon COVID-19.Therefore, in current study, a new Artificial Bee Colony (ABC) with Machine Learning-driven SA (ABCML-SA) model is developed for conducting Sentiment Analysis of COVID-19 Twitter data.The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made upon COVID-19.It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors.For identification and classification of the sentiments, the Support Vector Machine (SVM) model is exploited.At last, the ABC algorithm is applied to fine tune the parameters involved in SVM.To demonstrate the improved performance of the proposed ABCML-SA model, a sequence of simulations was conducted.The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.
Motif Mining: Finding and Summarizing Remixed Image Content

On the Internet, images are no longer static; they have become dynamic content. Thanks to the availability of smartphones with cameras and easy-to-use editing software, images can be remixed (i.e., redacted, edited, and re-combined with other content) on-the-fly, allowing a world-wide audience to repeat the process many times. From digital art to memes, the evolution of images through time is now an important topic of study for digital humanists, social scientists, and media forensics specialists. However, because typical data sets in computer vision are composed of static content, there has been limited development of automated algorithms for analyzing remixed content. In this paper, we propose the idea of Motif Mining: the process of finding and summarizing remixed image content in large collections of unlabeled and unsorted data. For the first time, this idea is formalized and a reference implementation grounded in that formalism is introduced. We conduct experiments on three meme-style data sets, including a newly collected set associated with the Russo-Ukrainian conflict. The proposed motif mining approach is able to identify related remixed content that, when compared to similar approaches, more closely aligns with the preferences and expectations of human observers.
IMPLEMENTASI CORRELATION MATRIX PADA KLASIFIKASI DATASET WINE

Wine is an alcoholic drink with various ingredients that can affect the quality of the wine.So many types of wine make it difficult for people to sort out the types and quality of the wine.This study uses a classification method to determine the quality of wine to reduce the role of wine experts or make it easier for experts to label the types and quality of the wine.The data mining classification algorithm used in this study is k-NN.To increase the accuracy value of the k-NN classification algorithm, normalization and feature selection methods are used.The normalization used are Z-score and Min-max normalization, and the feature selection used to improve accuracy is the correlation matrix.This study's results proved that using a feature selection correlation matrix could increase the accuracy value in Z-score normalization from 73.75% to 75.62%, and in Min-max normalization can increase accuracy from 68.12% to 71.25%.So it can be concluded that the correlation matrix feature selection can be used to increase the accuracy value and make it higher, and the study will be more accurate.
An Innovative Blockchain System for Smart Grids

Smart grids are one of the most advanced technologies that can intelligently integrate generators, consumers and energy storage. TESTBED2 is a large interdisciplinary European research project that focuses on developing new techniques to improve the scalability of smart grid services using new technologies such as blockchain. In the current paper we explained a general architecture of Testbed2 system that includes blockchain as a certification authority. Next to the description of the system in the current paper we present the blockchain implementation together with policies for smart grid authorization.
Effective Deep Learning Technique for Weapon Detection in CCTV Footage

The modern world is highly concerned with safety and security. The level of safety and security in a country’s environment determines its capacity to draw international investment and tourism. Closed Circuit Television (CCTV) cameras are used for monitoring and to keep an eye on activities like robberies, but they still require human monitoring & engagement. A system that can instantly identify these criminal acts is required. Despite of cutting-edge deep learning algorithms, quick processing power, & sophisticated CCTV cameras, the real-time weapon detection remains a significant barrier. In this work, YOLOV5 algorithm is used to detect the weapons in Closed Circuit Television (CCTV). The proposed system exhibits 96% F1 score and 96.6% mean average precision. This is a significant improvement when comparing with existing system.
Quantum algorithm for ESOP minimization

The Exclusive-OR Sum-of-Product (ESOP) minimization problem has long been of interest to the research community because of its importance in classical logic design (including low-power design and design for test), reversible logic synthesis, and knowledge discovery, among other applications. However, no exact minimal minimization method has been presented for more than seven variables on arbitrary functions. This paper presents a novel quantum-classical hybrid algorithm for the exact minimal ESOP minimization of incompletely specified Boolean functions. This algorithm constructs oracles from sets of constraints and leverages the quantum speedup offered by Grover's algorithm to find solutions to these oracles, thereby improving over classical algorithms. Improved encoding of ESOP expressions results in substantially fewer decision variables compared to many existing algorithms for many classes of Boolean functions. This paper also extends the idea of exact minimal ESOP minimization to additionally minimize the cost of realizing an ESOP expression as a quantum circuit. To the extent of the authors' knowledge, such a method has never been published. This algorithm was tested on completely and incompletely specified Boolean functions via quantum simulation.
Explainability for Ensemble Supervised Models

Ensemble models are considered to be effective when individual models are failing to balance the bias and variance for a training dataset. The predictions are aggregated in ensemble models to generate the final models. In the case of supervised regression models, many models are generated, and the averages of all the predictions are taken into consideration to generate the final prediction. Similarly, for supervised classification problems, multiple models are being trained, and each model generates a classification. The final model takes into account the majority voting rule criteria to decide the final prediction. Because of the nature of ensemble models, these are harder to explain to end users. That is why we need frameworks that can explain the ensemble models.
Trajectory Planning Strategies and Simulation for Autonomous-surface-vehicle–Remotely-operated-vehicle (ASV–ROV) Cooperative System

With the increase in the number of offshore power generation facilities and aquaculture ponds, demand for remotely operated vehicles (ROVs) to inspect underwater structures and conduct ecological surveys is increasing.Because ROVs are connected to a ground station on the water by a tether cable to enable the transmission of operating commands and images underwater, where radio communication is difficult, they can only operate within the range of the tether cable.To expand the operational range of ROVs, an autonomous surface vehicle (ASV)-ROV cooperative system, in which a tether cable is connected to an ASV and radio waves are used for remote operation from the autonomous vessel, is attracting attention.In a cooperative system, the tether cable must be managed on the ASV side, and the position and cable length of the ASV must be controlled to prevent the cable from pulling against or tangling with the ROV, which moves around underwater under the command of a human controller.We propose a method of controlling the position and cable length of the ASV using the information on the position of the ROV obtained from an underwater positioning device for an ASV-ROV cooperative system that we are also developing.In addition, we confirmed through simulations that the ASV and cable length follow target values even when the ROV moves along a complicated path.
Cross-language font style transfer

Abstract In this paper, we propose a cross-language font style transfer system that can synthesize a new font by observing only a few samples from another language. Automatic font synthesis is a challenging task and has attracted much research interest. Most previous works addressed this problem by transferring the style of the given subset to the content of unseen ones. Nevertheless, they only focused on the font style transfer in the same language. In many cases, we need to learn font style from one language and then apply it to other languages. Existing methods make this difficult to accomplish because of the abstraction of style and language differences. To address this problem, we specifically designed the network into a multi-level attention form to capture both local and global features of the font style. To validate the generative ability of our model, we constructed an experimental font dataset of 847 fonts, each containing English and Chinese characters with the same style. Results show that our model generates 80.3% of users’ preferred images compared with state-of-the-art models.
A Multi -level Alignment Training Scheme for Video-and-Language Grounding

To solve video-and-language grounding tasks, the key is for the network to understand the connection between the two modalities. For a pair of video and language description, their semantic relation is reflected by their encodings' similarity. A good multi-modality encoder should be able to well capture both inputs' semantics and encode them in the shared feature space where embedding distance gets properly translated into their semantic similarity. In this work, we focused on this semantic connection between video and language, and developed a multi-level alignment training scheme to directly shape the encoding process. Global and segment levels of video-language alignment pairs were designed, based on the information similarity ranging from high-level context to fine-grained semantics. The contrastive loss was used to contrast the encodings' similarities between the positive and negative alignment pairs, and to ensure the network is trained in such a way that similar information is encoded closely in the shared feature space while information of different semantics is kept apart. Our multi-level alignment training can be applied to various video-and-language grounding tasks. Together with the task-specific training loss, our framework achieved comparable performance to previous state-of-the-arts on multiple video QA and retrieval datasets.
Individual Intelligent Recognition Method Based on Fingerprint Features of Radar Emitter

Aiming at the problem of radar emitter individual recognition with unintentional modulation feature, a method of emitter fingerprint identification based on deep learning is proposed. In this method, the deep fingerprint features of radar emitter are extracted by deep Convolutional neural network, and the fingerprint feature library is constructed. Through similarity calculation, the individual recognition results of radar emitter signal are obtained. The results show that the new method can effectively identify both known and unknown emitter individuals.
Object Detection Using Machine Learning and Deep Learning

Abstract: An object detection system finds objects of the real-world present either in a digital image or an object detection system locates real-world items that are present in a digital image or a video. These objects can be any type of object, such as people, automobiles, or other objects. A model database, a feature detector, a hypothesis, and a hypothesis verifier are the four components that the system must have in order to successfully detect an item in an image or video. This paper provides an overview of the many methods for object detection, localization, classification, feature extraction, appearance information extraction, and many other tasks in photos and videos. The remarks are derived from the literature that has been analyzed, and important concerns are also noted
Dual Image Watermarking using Hessenberg decomposition and RDWT-DCT-SVD in YCbCr color space

A dual-image watermarking approach is presented in this research. The presented work utilizes the properties of Hessenberg decomposition, Redundant discrete wavelet transform (RDWT), Discrete cosine transform (DCT) and Singular value decomposition (SVD). For watermarking, the YCbCr color space is employed. Two watermark logos are for embedding. A YCbCr format conversion is performed on the RGB input image. The host image's Y and Cb components are divided into various sub-bands using RDWT. The Hessenberg decomposition is applied on high-low and low-high components. After that, SVD is applied to get dominant matrices. Two different logos are used for watermarking. Apply RDWT on both watermark images. After that, apply DCT and SVD to get dominant matrices of logos. Add dominant matrices of input host and watermark images to get the watermarked image. Average PSNR, MSE, Structural similarity index measurement (SSIM) and Normalized correlation coefficient (NCC) are used as the performance parameters. The resilience of the presented work is tested against various attacks such as Gaussian low pass filter, Speckle noise attack, Salt and Pepper, Gaussian noise, Rotation, Median and Average filter, Sharpening, Histogram equalization and JPEG compression. The presented scheme is robust and imperceptible when compared with other schemes.
AutoAD: an Automated Framework for Unsupervised Anomaly Detection

Over the last decade, we witnessed the proliferation of several machine learning algorithms capable of solving different tasks for the most diverse applications. Often, for an algorithm to be effective, significant human effort is required, in particular for hyper-parameter tuning and data cleaning. Recently, there have been increasing efforts to alleviate such a burden and make machine learning algorithms easier to use for researchers with varying levels of expertise. Nevertheless, the question of whether an efficient and fully generalizable automated Machine Learning (autoML) framework is possible remains unanswered. In this paper, we present autoAD, the first autoML framework for unsupervised anomaly detection. By leveraging a pool of different anomaly detection algorithms, each one coming with its own hyper-parameter search space, our framework automatically selects the best performing approach, while determining an optimal configuration for its hyper-parameters on a given dataset. Our extensive experimental evaluation, conducted on a rich collection of datasets, shows the substantial gains that can be achieved with autoAD compared to state-of-the-art methods for unsupervised anomaly detection.
Overview of the current situation relating to chemical engineering degree courses

In 2020, the Conference of Chemical Engineering Directors and Deans (CODDIQ) proposed the creation of an observatory to monitor chemical engineering degrees in Spain. This representative radiography of Chemical Engineering studies offers an initial point to observe the future changes when Royal Decree 822/2021 and proposed challenges in the last Ministerial Conference on the European Higher Education Area (EHEA) will be implanted. The survey data from CODDIQ partners allow us to know important data such as (i) the 24 international quality accreditations at Spanish universities, (ii) the high demand and the required marks, an average of 7.25, for the Chemical Engineering Bachelor’s degree, (iii) 9560 undergraduate students in this degree in Spain and their gender profile which is around 43% of women, similar than women lectures, (iv) the difficulty of this Bachelor’s degree through some indicators like duration of studies (5.25 years), graduation rate (41%) and drop-out rate (26%), (v) the employability after obtaining the Bachelor's degree is very high (>70%). In addition, Chemical engineering undergraduate and graduate students indicate their expectations are covered. In this paper, some consequences of the pandemic on students' performance (lower than before COVID-19) are analyzed, despite lectures tried to innovate in their classes and the university provided adequate tools for online teaching.
Strength in coalitions: Community detection through argument similarity

We present a novel argumentation-based method for finding and analyzing communities in social media on the Web, where a community is regarded as a set of supported opinions that might be in conflict. Based on their stance, we identify argumentative coalitions to define them; then, we apply a similarity-based evaluation method over the set of arguments in the coalition to determine the level of cohesion inherent to each community, classifying them appropriately. Introducing conflict points and attacks between coalitions based on argumentative (dis)similarities to model the interaction between communities leads to considering a meta-argumentation framework where the set of coalitions plays the role of the set of arguments and where the attack relation between the coalitions is assigned a particular strength which is inherited from the arguments belonging to the coalition. Various semantics are introduced to consider attacks’ strength to particularize the effect of the new perspective. Finally, we analyze a case study where all the elements of the formal construction of the formalism are exercised.
THE POTENTIAL OF DIGITAL GAME-BASED LEARNING FOR ENGLISH VOCABULARY ACQUISITION AMONG MALAYSIAN ADOLESCENTS

English is one of the mandatory subjects in primary and secondary school. However, few young Malaysians are proficient in the language, especially those living in rural areas. This study investigates the potential of using a word-search puzzle game as an alternative learning tool for English vocabulary acquisition. A word-search puzzle game was designed and developed. Additional dictionary features, such as words’ definitions, synonyms, antonyms, and pronunciation, are included in the word-search puzzle game. An online survey was conducted to evaluate the game experience (i.e. game flow and game challenge), perceived usefulness, and intention to use the game. The participants of this research comprised of a total of 30 adolescents recruited using convenient sampling among the contacts of the researchers. Participants were asked to install and play the word-search puzzle game before responding to the questionnaire. The findings indicate that the word-search puzzle game indeed has the potential to improve English vocabulary acquisition among adolescents.
Graph-Based Non-Sampling for Knowledge Graph Enhanced Recommendation

Knowledge graph (KG) enhanced recommendation, which aims to solve the cold start and explainability in recommender systems, has attracted considerable research interest recently. Existing recommender systems usually focus on implicit feedback such as purchase history without negative feedback. Most of them apply the negative sampling strategy to deal with the implicit feedback data, which may ignore the latent positive user-item interaction. Some other works adopt the non-sampling strategy that treats all non-observed interactions as negative samples and assigns a weight for each negative sample to represent the probability that this sample is a positive sample. However, they use a simple and intuitive weight assignment strategy and cannot catch the latent relationship from all interaction data. To address these problems, we consider graph structure information of both user-item interaction and knowledge graph, and propose a Graph-based Non-Sampling strategy to achieve efficient performance in Knowledge graph enhanced Recommendation (GNSKR). GNSKR utilizes node centrality to significantly improve recommendation performance with low computation cost. Meanwhile, we combine knowledge graph embedding and recommendation task with a local aggregation block, which efficiently catches the high-order connection information in KG enhanced recommendation. Experiments on three datasets show that GNSKR embraces the state-of-the-art with competitive efficiency.
Quick Estimate of Information Decomposition for Text Style Transfer

A growing number of papers on style transfer for texts rely on information decomposition. The performance of the resulting systems is usually assessed empirically in terms of the output quality or requires laborious experiments. This paper suggests a straightforward information theoretical framework to assess the quality of information decomposition for latent representations in the context of style transfer. Experimenting with several state-of-the-art models, we demonstrate that such estimates could be used as a fast and straightforward health check for the models instead of more laborious empirical experiments.
Application of augmented reality, gamification and m-learning

&lt;p&gt;Augmented reality, gamification and m-learning occupy today an important place in education, given the advantages that the use of mobile technologies has brought, independently or in combination with other technological scenarios and different devices that affect the teaching and learning of disciplines or that serve as a bridge to improve various processes mediated by ICT in teaching and/or learning. In this sense, the article presented here shows an analytical study that was developed under an exploratory, descriptive and interpretative methodology of the categories augmented reality, gamification and m-learning, based on a characterization in the databases, a literature review was conducted in the main databases such as Science direct, Scopus and web Science, obtaining 100 reference articles. Thus, it was found that these three categories determined as teaching strategies significantly influence motivation, interest in knowledge, retention and understanding of information for the development of teaching and learning processes. The method used allowed to develop the description of the panorama about the use of augmented reality, m-learning and gamification in elementary, middle and higher education.&lt;/p&gt;