Reviewerly/exHarmony huggingface dataset:
Comparison Between The Method of Principal Component Analysis And Principal Component Analysis Kernel For Imaging Dimensionality Reduction

This paper tackles with two methods to dimensionality reduction, namely principal component analysis (PCA ) in the case of linear combinations and kernel principal component analysis method in the case of nonlinear combinations to digital image processing and analysis for useful information .And then compare the two methods and know which methods are appropriate to imaging dimensionality reduction The methods were applied to a group of satellite images of an area in the province of Basra, which represents the mouth of the Tigris and Euphrates in the Shatt al-Arab, as well as the water channels permeating Basra Governorate and the water bodies scattered around these channels.In this research, it is shown that the fourth image band is best when using the PCA method the value of it is eigen value was the biggest ,while the KPCA method showed that the third image band was the best, giving the highest latent value. Comparing the two methods using the mean error error (MSE) method, the results showed that the main KPCA method was the best.
Evolving a cloud-robust water index with genetic programming

Over the years, remote sensing (RS) experts created many indices to help them study satellite imagery by highlighting characteristics like vegetation, water, or burnt areas, among others. In this work, we study water indices. Although there is a large number of water indices that work perfectly in unclouded imagery, clouds and shadows cast by clouds are often mistaken for water. This work is focused on the automatic feature construction using genetic programming (GP), in an attempt to make features that are more robust to these issues. To do this, we use a dataset containing pixels from areas where we could find these issues to evolve models that learn how to classify those pixels correctly. The results indicate improvements when comparing evolved features with indices, but further improvements are required to tackle other issues found.
Particle swarm optimisation for sparsity-based feature selection in multi-label classification

Multi-label classification (MLC) is an emerging real-world problem in which each instance is associated with multiple class labels simultaneously. Multi-label classification is challenging due to the complex interactions of features and multiple labels. Sparsity-based feature selection is an effective and efficient approach to selecting relevant features for multi-label classification. However, most (if not all) sparsity-based approaches are gradient-based and thus they tend to get stuck at local optima. This paper proposes a new sparsity approach based on particle swarm optimisation (PSO) which enhances its global search ability, thereby avoiding local optima. The paper also proposes new sparsity-based fitness functions for PSO, which can consider the feature interactions. The experimental results show that PSO can enable sparsity-based methods to select highly relevant features, thus improving the multi-label classification performance. The proposed sparsity-based methods also achieve the highest number of statistically significant results in comparison to several state-of-the-art and standard sparsity-based benchmarks.
Evolutionary submodular optimisation

tutorial Share on Evolutionary submodular optimisation: tutorial Authors: Aneta Neumann The University of Adelaide, Australia The University of Adelaide, AustraliaView Profile , Frank Neumann The University of Adelaide, Australia The University of Adelaide, AustraliaView Profile , Chao Qian Nanjing University, China Nanjing University, ChinaView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1427–1449https://doi.org/10.1145/3520304.3533624Published:19 July 2022Publication History 0citation35DownloadsMetricsTotal Citations0Total Downloads35Last 12 Months35Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Securing One Time Password (OTP) for Multi-Factor Out-of-Band Authentication through a 128-bit Blowfish Algorithm

Authentication and cryptography have been used to address security issues on various online services. However, researchers discovered that even the most commonly used multi-factor out-of-band authentication mechanism was vulnerable to attacks and traditional crypto-algorithms were characterized to have some drawbacks making it crucial to choose desirable algorithms for a particular purpose. This study introduces an innovative modification of the Blowfish algorithm designed to capitalize on its strengths but supports 128-bits block size text input using dynamic selection encryption method and reduction of cipher function execution through randomly determined rounds. Experimentation results on 128-bit input text revealed significant performance improvements with utmost 5.91 % in terms of avalanche effect, 38.97 % for integrity, and 41.02 % in terms of execution time. Results also showed that the modification introduced extra security layer, thus, displaying higher complexity and stronger diffusion at faster execution time making it more difficult and complex for an unauthorized individual to decipher the information and desirable to be used for applications with multiple users respectively. This is a good contribution to the continuous developments in the field of information security particularly in cryptography and towards providing a secure OTP for multifactor out-of-band authentication.
Readiness of Humanitarian Subjects Teachers for Career Guidance Work in Secondary Education Institutions

The article considers the theoretical foundations of professional orientation as a component of the professional activity of a teacher of humanitarian subjects. Vocational guidance in secondary education institutions is characterized as a science-based system for assisting high school students to identify and develop personal professional aptitudes and cognitive interests in choosing a profession. It is noted that vocational guidance in educational institutions is impossible without education, upbringing, the study of psychophysiological characteristics of high school students, psychodiagnostics, organization of elective courses and communications specially organized on a parity basis. The essential aspects of the teacher's career guidance activity (socio-economic, psychological-pedagogical and medical-physiological) are described, which are aimed at promoting the operative and meaningful choice of the future profession by high school students. The importance of involving various types of student activities (cognitive, socially useful, communicative, playful, etc.) in the career guidance work and the development of humanitarian subjects of educational technologies by the teacher are pointed out. The readiness of a humanitarian subjects teacher for career guidance work is determined by his effective theoretical and practical preparation for career guidance within his subject, effective formation of students' ability to independently and consciously choose future professional activities, responsibility for high school students' choice and career guidance system in a secondary school institution in general. Proper readiness is shown when the teacher establishes effective criteria and indicators of the effectiveness of the vocational guidance.
Optimal Accuracy-Privacy Trade-Off of Inference as Service

In this paper, we propose a general framework to provide a desirable trade-off between inference accuracy and privacy protection in the inference as service scenario (IAS). Instead of sending data directly to the server, the user will preprocess the data through a privacy-preserving mapping, which will increase privacy protection but reduce inference accuracy. To properly address the trade-off between privacy protection and inference accuracy, we formulate an optimization problem to find the privacy-preserving mapping. Even though the problem is non-convex in general, we characterize nice structures of the problem and develop an iterative algorithm to find the desired privacy-preserving mapping, with convergence analysis provided under certain assumptions. From numerical examples, we observe that the proposed method has better performance than gradient ascent method in the convergence speed, solution quality and algorithm stability.
Generating the Trends and Forecast of Crime Rates in Ozamiz City, Philippines

Crime is considered as a serious issue. It makes people worried on their own safety especially when the crime rate is increasing. The study generated the trend analysis and forecasted data of the recorded crimes in Ozamiz City using autoregressive integrated moving average through the GRTEL software. Results showed that majority of the crimes recorded decreased from 2018 to 2021. These include crimes against person, crimes against property, crimes against personal liberty and security and violations against special laws particularly the comprehensive dangerous drugs Act of 2002. However, the violators who resisted persons in authority drastically increased. On the other hand, forecasted data revealed that most of the crimes will most likely increase in occurrence in the year 2022 and will be stabilized in the remaining years. The data gathered from this study can be used by the different law enforcement agencies in planning and creating intervention strategies towards mitigating the occurrence of crimes in Ozamiz City.
Recomposition of Process Choreographies Using a Graph-Based Model Repository

Modeling process choreographies is a key activity to illustrate collaboration between different organizations. In this paper, we propose a method to automatically generate BPMN Choreography Diagrams based on existing models. A process choreography can be decomposed into a number of reusable parts. Such parts are then stored in a web-based repository, which uses the BPMN Ontology to describe structural properties of each component. New diagrams are generated by composing a graph based on the general connectivity rules and requirements provided by the user. Our results show that composing new models in such a way may serve as decision support to domain experts and reduce the risk of designing a flawed diagram.
A security services for internet of thing smart health care solutions based blockchain technology

Pervasive and ubiquitous computing has enabled people better integrate physical things into the digital world. The internet of things (IoT) has been considerably more widely used in business and everyday life in the last decade. Innovative healthcare information and communication technologies are a vast field of research and applications that need IoT benefits, including speed, security, and low cost. The proposed modified advanced encryption standard (AES)-cipher block chaining (CBC)-based blockchain technology offers a shared key to devices that need to communicate directly with or with entities outside the smart healthcare network to give users greater control over transactions. The experiments are carried out using a Raspberry Pi 3, whereas two different sensors are ‎employed in this case. Blockchain technology encrypts data between doctor and patient with varied user numbers. The results from experiments revealed that the proposed modified AES-CBC based blockchain technology could provide the IoT application with security services (confidentiality, integrity, and access control) with efficient execution time.
Skewness in the Cryptocurrency Market

The cryptocurrency market is generally accepted in the world, and its price has soared and plummeted sharply. Meanwhile, skewness is an index reflecting the rapid rise and fall of asset prices in a short period. Studying the relationship between the skewness of cryptocurrency and its returns can help risk evaders expect bad news and provide a reference for risk enthusiasts to make investment decisions. Therefore, through univariate combination analysis, this paper groups cryptocurrency according to the skewness of the previous month before buying and holding it in the next week. Moreover, the excess return series are calculated to make statistical tests on it. Then we construct a three-factor model of cryptocurrency and adjust the return series. To enhance the robustness of the conclusion, we also use other measures of skewness such as idiosyncratic skewness to conduct a univariate combination analysis. The results show that a positive correlation between cryptocurrency skewness and its returns exists, which can be used as a reference index of the returns.
Functioning E-Textile Sensors for Car Infotainment Applications

Car interiors are envisioned to be living spaces that support a variety of non-driving-related activities. Previous work focuses on enhancing driving-related functions, performance and safety. By developing textile-based sensors, we focus on enabling non-driving activities integrated in the car interior and supporting a richer user experience. In this paper, we introduce an array of new applications using e-textile sensors to the design space of car interiors. Our functional prototypes implement hand interactions (such as press and double tap gestures) on the leather or fabric of the steering wheel and back of the head rest. We then propose applications for these sensors to control media, car windows, and air-conditioning. Overall, the paper contributes a novel tactile input modality to support drivers and empower backseat passengers.
Pyramid Attention “Zero-Shot” Network for Single-Image Superresolution

Single-image superresolution (SISR) is one of the requisite image processing methods used to reconstructs a high-resolution (HR) image from a low-resolution (LR) observation. Existing SISR methods mostly rely on supervised learning and are confined to specific training data, where implicit conformity matching of the LR images with their high-resolution (HR) counterparts is conducted. However, real LR images rarely obey these constraints, and the degradation process is much more intricate and unknown. In this paper, we proposed a pyramid attention zero-shot (PAZS) network for SISR that sufficiently explores the information hidden in an image itself by learning the patch distribution at a different scale of the image. The proposed pyramid generative model learns the patch distribution in the internal patch learning stage. Then, based on the learned intrinsic property, we explore the corresponding superresolution (SR) image by integrating the intrinsic information into a self-attention mechanism with a progressive generation style at test time. This mechanism consists of internal-external attention and a cross-scale guided fusion module as the connecting passageway. Thus, the PAZS architecture maintains both the global structure and the fine textures of the SR image. This allows SR to be performed without other training datasets and can be adapted to different settings for each image. Experiments on diverse datasets demonstrate that the proposed method outperforms other methods based on single-image training.
Normal Map Estimation in the Wild

We propose a method to estimate normal maps of objects in the wild, from just a single RGB image. Our approach is based on deep learning, and we use synthetic data to train our network. Lastly, we show its applicability by improving the results of image-based appearance editing tasks.
Decolonizing the Machine: Race, Gender and Disability in Robots and Algorithmic Art

This paper calls attention to critical race theory, critical disability studies, decolonial theory and their relevance to the study of robotic art and performances that utilise algorithms and other forms of computation.Our purpose is to uncover the veiled links between racial, gendered, and ableist practices that inform theory and practice in media art and performance, and to combat the governing codes that constructand continue to normalizepractices of dehumanizing exclusions.While robots and cyborgs have the potential to figure posthuman forms of subjectivation, in practice they often reinforce human-machine, self-other, or abled-disabled binaries and gloss over the racist and dehumanizing exclusions that uphold neoliberal forms of power and Western conceptions of the human.Our aim is that this track, and the papers and discussions that follow, will highlight mechanisms for meaningful intervention and instigate critical reflection within media art theory to make visible how artworks and technologies continue to encode colonial hierarchies.
Data Classification and Access Control in Indonesia One Map Policy Geoportal

Indonesia has a data access policy on one map policy geoportal. Users at each level of government have different access rights to data. The President has full rights to view and downloads the data, while the governor or regent can only see certain attributes and download only its region on the same data. Data in one map policy geoportal must be able to facilitate the differences in access rights. The one map policy geoportal uses a service query system to facilitate the classification of access to process view data. In the process of downloading data geoportal using database queries. The choice of download data through the database is based on the results of the test download data from the database and service. These results indicate that downloading data through the database is faster than downloading data through a service.
Optimal Path Planning With Minimum Inspection Teams and Balanced Working Hours For Power Line Inspection

Power line inspection plays a significant role in the normal operation of power systems. Although there is much research on power line inspection, the question of how to balance the working hours of each worker and minimize the total working hours, which is related to social fairness and maximization of social benefits, is still challenging. Experience-based assignment methods tend to lead to extremely uneven working hours among the working/inspection teams. Therefore, it is of great significance to establish a theoretical framework that minimizes the number of working teams and the total working hours as well as balances the working hours of inspection teams. Based on two real power lines in Jinhua city, we first provide the theoretical range of the minimum number of inspection teams and also present a fast method to obtain the optimal solution. Second, we propose a transfer-swap algorithm to balance working hours. Combined with an intelligent optimization algorithm, we put forward a theoretical framework to balance the working hours and minimize the total working hours. The results based on the two real power lines verify the effectiveness of the proposed framework. Compared with the algorithm without swap, the total working hours obtained by the transfer-swap algorithm are shorter. In addition, there is an interesting finding: for our transfer-swap algorithm, the trivial greedy algorithm has almost the same optimization results as the simulated annealing algorithm, but the greedy algorithm has an extremely short running time.
Hand Posture Recognition Using Convolutional Neural Network

In this work we present a convolutional neural network-based algorithm for recognition of hand postures on images acquired by a single color camera. The hand is extracted in advance on the basis of skin color distribution. A neural network-based regressor is applied to locate the wrist. Finally, a convolutional neural network trained on 6000 manually labeled images representing ten classes is executed to recognize the hand posture in a sub-window determined on the basis of the wrist. We show that our model achieves high classification accuracy, including scenarios with different camera used in testing. We show that the convolutional network achieves better results on images pre-filtered by a Gabor filter.
Strike Action and the Academic Performance of Students of Alvan Ikoku Federal College of Eduction Owerri

This study examined the influence of strike action on the academic performance of students in tertiary institutions with particular reference to Alvan Ikoku Federal College of Education, Owerri, Imo State. The population of the study was 635 academic staff in five schools of the college. A sample of 128 representing 20% of the population was taken. The study adopted a descriptive survey design while the instrument for data collection was structured questionnaire. The likertfour-point, scale rating was adopted whereas the data collected were analyzed using mean. The result of the data analyzed showed that strike action highly influences the academic performance of students in tertiary institution. Poor remuneration, negligence by government, conducive academic environment among others were identified as major causes of strike action. The researchers recommend that autonomy of tertiary institutions, adequate funding, and the re-orientation of staff to find alternative ways of expressing their grievances as measures to curb incessant strike actions in our tertiary institutions.
Multihop Question Answering by Using Sequential Path Expansion With Backtracking

Multi-hop question answering from knowledge graphs has gained a growing attention in the past few years due to its vast applications in AI. Existing approaches in this regard mostly rely on exhaustively traversing all paths in the graph until reaching a candidate answer entity, leading to high time complexity and reasoning errors when training the model. Recently, a few works have tried to reduce the search space by proposing sequential-decision techniques so that only the path that is likely to lead to the answer is traced. However, the sequential decision on the relations is likely to accumulate errors due to the potential incorrect matching with the question. Alternatively, this work proposes a method that leverages sequence matching and a backtracking algorithm to identify the correct path to the answer while minimizing the accumulated error. The process starts from the question entity and grows the path iteratively by reasoning over the outgoing relations. The aim is to find the relation with the highest similarity to the question and to transit through it to the next entity. Meanwhile, a termination/backtracking check is performed at each iteration to validate the path and act accordingly either by proceeding to the next entity, stopping the process, or backtracking to correct a wrongly expanded path. The proposed method does not require setting a maximum number of hops in advance and uses a compare-aggregate model with attention mechanism for effective sequence matching. Experimental results show that our method significantly outperforms existing methods on three benchmarking datasets.
Assessment of Students in Online Industrial Practice Activities Using Machine Learning Based on Mobile Application

All of the learning in the pandemic era uses online learning including practical in the industry that should do all of the students to apply their knowledge. The practical industry online is very difficult to assement students that the assessment is given from both company and the university. Companies have many parameters to assessment and each company has different parameters. This study uses 14 parameters that are generally used in assessment for practical students and the university side using 10 parameters. The problem is that every parameter has a different weight than it makes it confusing to give marks with manual assessment. This research uses machine learning to fix this problem based on mobile applications for the user interfaces. The result of testing this application had an average accuracy for assessment students based on parameters of companies and universities that is 83,3%.