Collaborative Features Research Articles

Mo-doped titanate nanofibers from hydrothermal syntheses for improving bone scaffold

A longstanding interest in bone tissue engineering is the development of new bio-scaffolds that can be manufactured on a large scale with high throughput at low cost. Here, we report a low-cost and systematically optimized hydrothermal synthesis for producing Mo-doped potassium titanate nanofibers with high structural purity. This new nanosynthesis is based on bone tissue growth on an undoped titanate nanowires-entangled scaffold, as previously reported by our team. The morphological and structural characterization data suggest that the crystal structure of Mo-doped titanate nanofibers closely resembles that of the undoped ones. This resemblance is potentially valuable for assessing the role of Mo dopants in engineering bone tissue.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

Students Perceptions of the Personality Competencies of Guidance and Counseling Teachers in High Schools

Personality competency is one of the important things a teacher, including guidance and counseling teachers, have. The visible phenomenon is that there are still guidance and counseling teachers who do not have good personalities who support the implementation of guidance and counseling services in schools, which causes various perceptions towards the personality of guidance and counseling teachers. This research aims to reveal and describe students' perceptions of the personality competence of guidance and counseling teachers. This research was carried out at SMA Negeri 10 Medan using descriptive quantitative research with a positivistic paradigm. The population in the research was 210 class Data were collected using a perception scale regarding the personality of guidance and counseling teachers which was analyzed using descriptive analysis techniques. The overall research results of the research indicators obtained a percentage of 73.6% and were in the good category. So it can be concluded that students' perceptions of the personality competence of guidance and counseling teachers at this school are in the good category, meaning that guidance and counseling teachers have personality competence and quality so that it does not give rise to students having bad perceptions of guidance and counseling teachers at this school.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack// This page is in Indonesian Translate to English AfrikaansAlbanianAmharicArabicArmenianAzerbaijaniBengaliBulgarianCatalanCroatianCzechDanishDutchEnglishEstonianFinnishFrenchGermanGreekGujaratiHaitian CreoleHebrewHindiHungarianIcelandicIndonesianItalianJapaneseKannadaKazakhKhmerKoreanKurdish (Kurmanji)LaoLatvianLithuanianMalagasyMalayMalayalamMalteseMaoriMarathiMyanmar (Burmese)NepaliNorwegianPashtoPersianPolishPortuguesePunjabiRomanianRussianSamoanSimplified ChineseSlovakSlovenianSpanishSwedishTamilTeluguThaiTraditional ChineseTurkishUkrainianUrduVietnameseWelsh Always translate Indonesian to EnglishPRO Never translate Indonesian Never translate ejournal.uin-suska.ac.id

Wavelet-Based Self-Attention GAN With Collaborative Feature Fusion for Image Inpainting

Image inpainting is a significant task in the applications of computer vision, that aims to fill in damaged regions with visually realistic contents. With the development of deep learning, generative adversarial network (GAN)-based image inpainting approaches have achieved remarkable progress. However, these methods only utilize one-sided structure information to assist inpainting, which can not achieve satisfactory results, especially when synthesizing large-area missing complex images. In order to tackle this problem, a wavelet-based self-attention GAN (WSA-GAN) with collaborative feature fusion is proposed, which is embedded with a wavelet-based self-attention (WSA) and a collaborative feature fusion (CFF). The WSA is designed to conduct long-range dependence among multi-scale frequency information to highlight significant structure details for better generating texture boundaries. The CFF is presented to couple channel-guided space and space-affected channel streams to facilitate the interaction of spatial and channel features, which can effectively avoid potential domain conflicts. Besides, a novel wavelet consistency loss and a hierarchical pyramid feature matching (PFM) discriminator are introduced to stabilize the model training. Extensive experiments on three public datasets, including Paris StreetView, CelebA-HQ and Places, demonstrate that the proposed method outperforms the state-of-the-art methods both quantitatively and qualitatively.

Enhancing social sciences learning effectiveness with a learning management system

This study aims to analyze the use of Learning Management System (LMS) in improving the effectiveness of Social Science (IPS) learning. This research uses the research method of literature study, which involves searching, selecting, and analyzing literature relevant to the research topic. The results of the analysis show that the use of Learning Management System (LMS) in social studies learning has great potential to improve learning effectiveness. LMS provides accessibility and flexibility that allows students to access learning materials anytime and anywhere, overcoming space and time constraints. The interactive and collaborative features in the LMS facilitate interaction and collaboration between students and teachers, so students can share ideas, understanding, and experiences with fellow students and teachers. Through the LMS, teachers can monitor student progress in real-time and provide prompt feedback, which helps in the evaluation of learning. This research provides valuable insights for education practitioners in harnessing the potential of LMS to improve social studies learning.

Konsep Multiakad dalam Fatwa DSN-MUI: Sebuah Inovasi dalam Praktik Keuangan Syariah di Indonesia

This article discusses the concept of hybrid contracts in Fatwa DSN-MUI which is an innovation in Islamic financial practice in Indonesia. The hybrid contracts concept is an approach that combines several Islamic contracts or financial instruments in one transaction to achieve the goals desired by the parties involved. This paper outlines various examples of the application of hybrid contracts in Islamic financial transactions. This article provides an overview of how the hybrid contracts concept became an innovation in Islamic financial practice in Indonesia. By using hybrid contracts, financial actors can achieve their goals in a sharia manner and minimize the risks associated with such transactions. However, a good understanding of sharia principles and guidance from authoritative institutions such as DSN-MUI is still needed to maintain the integrity and validity of sharia transactions.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

FOSTERING CRITICAL THINKING SKILLS IN EFL UNIVERSITY STUDENTS DURING LISTENING COMPREHENSION USING DIGITAL TEACHING-MATERIALS: A CASE STUDY

Understanding listening materials is perceived as the most challenging aspect of language learning by many EFL University students. They face difficulties in recognizing English words, phrases, pronunciation, vocabulary, and sentence complexity, compounded by the rapid speech rate of native speakers. Addressing these challenges, this study explores the use of digital materials that require students to analyse, evaluate and make informed judgments based on listening content, thereby nurturing higher order thinking skills. A qualitative case study was conducted, involving the collection of comprehensive data from course syllabi, lecturers and students. A total of sixty-one university students enrolled intermediate listening course, along with two listening lecturers, were actively engaged to enrich the data. The results shows that the use of digital materials, particularly the e-module, has proven to be effective. Students’ responses indicate that the e-module effectively facilitates critical thinking, improves reflective ability, and encourages motivation. Moreover, the e-module features, including well-defined learning outcomes, authentic materials, scaffolded learning activities, engaging multimedia elements, collaborative features, and opportunities for reflection and self-assessment, collectively contribute to foster critical thinking skills and motivation among students.

Interpersonal process features of collaboration between probation and behavioral health practitioners.

This study sought to identify and conceptualize the central interpersonal process features that comprise good collaboration between behavioral health practitioners (BHPs) and probation officers (POs). Eighteen POs and 21 BHPs from geographically adjacent jurisdictions in a Midwestern state each participated in one of six focus groups. Researchers systematically coded focus group transcripts for interpersonal collaborative themes using both inductive and deductive strategies. Seven core themes-shared values and beliefs, communication and information sharing, knowledge of the other, trust, respect, team-based decision making, and equitable contribution-were identified and coded for importance, as indexed by frequency, latent tone, and group consensus. An initial working operational definition of these constructs, based on practitioners' dialogue, is also provided. This study is one of the first to provide BHPs and POs who work with justice-involved people with prescriptive guidance about interpersonal actions and approaches that will best serve their professional collaborative efforts. Future research should examine whether these constructs hold across other samples and contexts and work to fully operationalize and measure these constructs and their importance for affecting various outcomes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Character Development through Student Guidance and Counseling at School

Guidance and counseling is an integral part of the national education system, therefore the policies, objectives and implementation of guidance and counseling are also part of the policies and objectives and implementation of character education. Schools play an important role in shaping the character of a nation and the next generation. at school, students receive various educational information about character, which not only goes into the classroom, but also through teaching and counseling programs, which can strengthen character values such as openness, honesty, etc., and promote discipline. This also requires special strategies, including coercion. Hardworking, creative, independent, caring, responsible. This research methodology uses a qualitative approach with the type of library research. The research has shown that counseling and mentoring programs are developed through student needs analysis, so that the attending teacher or counselor makes the counseling and mentoring program feasible for student character development and provides appropriate services.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

The encroachment strategy under eco-innovation collaboration with delayed realized efficiency information

PurposeEco-innovation products, which means achieving more efficient and responsible use of resources and reducing the detrimental impact on the environment, can win a competitive advantage for the enterprises. But it is not easy to implement due to the high cost of eco-innovative technologies development, the uncertainty of market needs and return risk of investment. Many enterprises seek collaborations from their upstream suppliers to jointly carry out eco-innovation, such as Apple, IBM and Nike. A unique feature of collaboration is that efforts by one party enhance the marginal value of the other party's efforts. However, the collaboration will make the partner know the eco-innovation technology and prompt the partner to encroach the market to sell competitive products by herself. Motivated by this observation, this paper considers the optimal collaboration strategy on eco-innovation between upstream and downstream supply chain member and the optimal encroachment strategy of upstream supplier in a supply chain.Design/methodology/approachThis paper models a supply chain wherein a supplier provides products or materials for her manufacturer and cooperates with her manufacturer in eco-innovation. Also, the supplier could encroach on the market to sell similar products by herself. Then this paper uses game theory and mathematical modeling to do relative analysis.FindingsThe analysis reveals several interesting insights. First, eco-innovation collaboration makes supplier encroachment no longer only rely on the encroachment cost. The delayed realized eco-innovation efficiency information also plays a vital role. Second, different from previous research, the authors find the manufacturer's preference for supplier encroachment depends on the uncertainty of eco-innovation efficiency and potential market demand. Third, both partial and full encroachment strategies of the supplier can effectively improve the eco-innovation level.Originality/valueThe paper is the first to take the interplay between collaboration and encroachment into account in a supply chain. The results caution enterprises and policymakers to take vertical collaboration and delayed realized information into account in the competitive supply chain before making any operational decisions. Furthermore, the authors propose that governmental intervention aimed at stimulating supplier encroachment in appropriate circumstances can contribute to the improved environmental performance of products.

How to configure intellectual capital of research teams for triggering scientific breakthroughs: Exploratory study in the field of gene editing

Considerable research has focused on finding the optimum solution to reveal the role of intellectual capital in scientific breakthroughs, ignoring the fact that all roads lead to Rome. To this end, this study aims to explore the configuration of intellectual capital—comprising human and social capital—within research teams to trigger scientific breakthroughs. By identifying research teams of scientific breakthroughs and their control groups involved in gene editing, we initially determined the potential antecedents of scientific breakthroughs pertaining to intellectual capital through an integrative comparison of research teams with breakthroughs and non-breakthroughs. Subsequently, we employed a holistic approach grounded in configurational theory to investigate how the combination of human capital (within-domain experience and knowledge structure) and social capital (collaboration features within and beyond a team) facilitates scientific breakthroughs. A fuzzy-set qualitative comparative analysis (fsQCA) approach was used to empirically pinpoint seven configurations of human and social capital that explain scientific breakthroughs; these demonstrate that research teams making scientific breakthroughs are not all-around players in intellectual capital and that their success depends more on resourceful capital allocation. Our findings inspire team managers to rationally deploy critical set solutions to optimize team structure and resource allocation to pursue high-quality scientific innovation.

Air Quality Classification Using Extreme Gradient Boosting (XGBOOST) Algorithm

Air pollution is a serious issue caused by vehicle exhaust, industrial factories, and piles of garbage. The impact is detrimental to human health and the environment. To quickly and accurately monitor classification, techniques are used. One efficient and accurate classification algorithm is XGBoost, a development of the Gradient Decision Tree (GDBT) with several advantages, such as high scalability and prevention of overfitting. The parameters used in the classification include (PM10), (PM2,5),(SO2),(CO),(O3) and (NO2). This study aims to classify air quality into three labels or categories: good, moderate, and unhealthy. In the dataset used to experience an imbalance class, to overcome the imbalance class, techniques will be carried out, namely SMOTE, Random UnderSampling, and Random OverSampling, by producing an accuracy of up to 98,61% with the SMOTE technique for class imbalance. Testing the level of accuracy is done by using the Confusion Matrix.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian //  TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

Distinguishing collaboration from related elements of crime

The article is devoted to the distinction between the corpus delicti of crimes under Article 111-1 of the Criminal Code of Ukraine “Collaboration” and related crimes. Scientific articles by researchers who studied who have studied the objective and subjective features of collaboration have been analysed. It is stated that collaboration differs from high treason by the nature of the acts constituting the objective element of the crime, the time of the crime and the subject of the crime. The collaboration is distinguished from aiding and abetting the aggressor State by the subject of collaboration.The set of actions that make up the objective side of the specified crimes also has differences. The difference between the creation of illegal paramilitary formations and collaborative activities lies in the object of the crime. In addition, the crime provided for in Part 7 of Article 111-1 of the Criminal Code of Ukraine is committed in the conditions of the Ukrainian territory occupation as a result of the invasion of the aggressor state troops. The collaborative activity must be distinguished from the crime provided for in Article 436-2 of the Criminal Code of Ukraine “Justification, recognition as legitimate, denial of the armed aggression of the russian federation against Ukraine, glorification of its participants”. The differences are in the object of the crime. In addition, collaboration can only be carried out in the occupied territory.It is proposed to make changes to the Criminal Code of Ukraine. In particular, it is proposed to define the concept of subversive activity, which is used in Article 111 of the Criminal Code of Ukraine. It is necessary to specify the list of actions that can be considered as assisting the enemy in subversive activities. Establish clear distinctions between collaborative activities and assisting the enemy in subversive activities. It is necessary to solve other problematic issues of qualification of violations of the legislation of Ukraine under Article 111-1 of the Criminal Code of Ukraine or other articles of the Code containing similar offenses.

Comparative Analysis of Data Visualization Tools: Power BI and Tableau

This research paper aims to provide a comprehensive comparative analysis of Power BI and Tableau, two popular tools used for business analytics and data visualization. The study highlights key differences in pricing structure, user interface, integration options, visualization capabilities, data modeling and ETL capabilities, collaboration features, and mobile app functionality. By examining these factors, the research aims to guide organizations in making informed decisions when choosing between Power BI and Tableau based on their specific needs and requirements. Key Words: Data Visualization, Data Analytics, Power BI, Tableau.

Intent Disentanglement and Feature Self-Supervision for Novel Recommendation

One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation studies over-emphasize the importance of tail items without differentiating the degree of users' intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing methods has ever taken the extreme case of tail items, i.e., cold-start items without any interaction, into consideration. In this work, we first disclose the mechanism that drives a user's interaction towards popular or niche items by disentangling her intent into conformity influence (popularity) and personal interests (preference). We then present a unified end-to-end framework to simultaneously optimize accuracy and novelty targets based on the disentangled intent of popularity and that of preference. We further develop a new paradigm for novel recommendation of cold-start items which exploits the self-supervised learning technique to model the correlation between collaborative features and content features. We conduct extensive experimental results on three real-world datasets. The results demonstrate that our proposed model yields significant improvements over the state-of-the-art baselines in terms of accuracy, novelty, coverage, and trade-off.

Establishing Grenada’s First Collaborative High School

The author describes creating a new collaborative high school in Grenada. She explains the key features of effective collaboration between teachers, students, parents, and the community. This commentary highlights the challenges of reframing education to emphasize cooperation and effective school culture-building, reclaim the purpose of education, and avoid reproduction of colonial models of schooling. The commentary follows up on an interview in this journal, 45 (2–3).

Overcoming the Global Economic Crisis in The Perspective of Islamic Finance

This paper makes a connection between the global financial crisis and an Islamic financial model. The goal is to downplay the severity and frequency of the financial crisis by sharing risk among financial participants. This is accomplished through a system of shared credit creation. When purchasing real goods and services from people who have them and need to sell, people are given access to credit that they can use to buy what they need. Both sellers and buyers want to receive delivery of their purchased items, so the system creates economic growth through increased credit availability. Studying Islamic finance literature allows people to explore the possibilities and restrictions of inherited situations. This leads to new financial design solutions for the current financial crisis as well as future financial collapses. It also uses critical thinking and analysis to create new possibilities and constraints in situations. New financial systems should use a business model based on shared profits and losses. This was based on academic studies and doesn’t include any real-world testing. By incorporating Islamic financial principles into business practices, a new financial model could help finance businesses. This alternate system for the banking system could help control our financial system and encourage economic growth through more controlled credit expansion TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack// This page is in English Translate to Indonesian AfrikaansAlbanianAmharicArabicArmenianAzerbaijaniBengaliBulgarianCatalanCroatianCzechDanishDutchEnglishEstonianFinnishFrenchGermanGreekGujaratiHaitian CreoleHebrewHindiHungarianIcelandicIndonesianItalianJapaneseKannadaKazakhKhmerKoreanKurdish (Kurmanji)LaoLatvianLithuanianMalagasyMalayMalayalamMalteseMaoriMarathiMyanmar (Burmese)NepaliNorwegianPashtoPersianPolishPortuguesePunjabiRomanianRussianSamoanSimplified ChineseSlovakSlovenianSpanishSwedishTamilTeluguThaiTraditional ChineseTurkishUkrainianUrduVietnameseWelsh Always translate English to IndonesianPRO Never translate English Never translate ejournal.iainpalopo.ac.id

Analysing educational scientific collaboration through multilayer networks: patterns, impact and network generation model

Abstract Scientific collaboration is an essential aspect of the educational field, offering significant reference value in resource sharing and policy making. With the increasing diversity and inter-disciplinary nature of educational research, understanding scientific collaboration within and between various subfields is crucial for its development. This article employs topic modelling to extract educational research topics from publication metadata obtained from 265 scientific journals spanning the period from 2000 to 2021. We construct a multilayer co-authorship network whose layers represent the scientific collaboration in different subfields. The topological properties of the layers are compared, highlighting the differences and common features of scientific collaboration between hot and cold topics, with the main difference being the existence of a significant largest connected component. Further, the cross-layer cooperation behaviour is investigated by studying the structural measures of the multilayer network and reveals authors’ inclination to collaborate with familiar individuals in familiar subfields. Moreover, the relationships between the authors’ features on the network topology and their H-index are investigated. The results emphasize the significance of establishing a clear research direction to enhance the academic reputation of authors, as well as the importance of cross-layer collaboration for expanding their research groups. Finally, based on the above results, we propose a multilayer network generation model of scientific collaboration and verify its validity.

Lithium-Ion Battery State of Health Prediction With a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach

Lithium-ion (Li-ion) batteries play an important role in providing necessary energy when acting as a main or backup source of electricity. Indeed, the unavailability of battery aging discharge data in most real-world applications makes the State of Health (SoH) assessment very challenging. Alternatively, accelerated aging is therefore adopted to emulate the degradation process and to achieve an SoH estimate. However, accelerated aging generates limited deterioration patterns suffering from a higher level of complexity due to the non-linearity and non-stationarity imposed by harsh conditions. In this context, this paper aims to provide a predictive model capable of solving incomplete data problems by providing two main solutions for each of the problems of complexity and missing patterns, respectively. First, to overcome the problem of lack of patterns, a robust collaborative feature extractor (RCFE) is designed by collaborating between a set of improved restricted Boltzmann machines (I-RBMs) to be able to share learning knowledge among different locally trained I-RBMs to create a more generalized global extraction model. Second, a set of RCFEs is then evolved through a neural network with an augmented hidden layer (NAHL) to enhance the predictive ability by further exploring representation learning to overcome pattern complexity issues. The designed RCFE-NAHL is trained to predict SoH using constant current (CC) discharge characteristics by implying multiple characteristics recorded through the constant voltage (CV) charging process as indicators of health. The proposed SoH prediction approach performances are evaluated on a set of battery life cycles from the well-known NASA database. In this context, the achieved results clearly highlight the higher accuracy and robustness of the proposed learning model.

Intrusion detection in cloud computing based on time series anomalies utilizing machine learning

The growth of cloud computing is hindered by concerns about privacy and security. Despite the widespread use of network intrusion detection systems (NIDS), the issue of false positives remains prevalent. Furthermore, few studies have approached the intrusion detection problem as a time series issue, requiring time series modeling. In this study, we propose a novel technique for the early detection of intrusions in cloud computing using time series data. Our approach involves a method for Feature Selection (FS) and a prediction model based on the Facebook Prophet model to assess its efficiency. The FS method we propose is a collaborative feature selection model that integrates time series analysis techniques with anomaly detection, stationary, and causality tests. This approach specifically addresses the challenge of misleading connections between time series anomalies and attacks. Our results demonstrate a significant reduction in predictors employed in our prediction model, from 70 to 10 predictors, while improving performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Median Absolute Percentage Error (MdAPE), and Dynamic Time Warping (DTW). Furthermore, our approach has resulted in reduced training, prediction, and cross-validation times of approximately 85%, 15%, and 97%, respectively. Although memory consumption remains similar, the utilization time has been significantly reduced, resulting in substantial resource usage reduction. Overall, our study presents a comprehensive methodology for effective early detection of intrusions in cloud computing based on time series anomalies, employing a collaborative feature selection model and the Facebook Prophet prediction model. Our findings highlight the efficiency and performance improvements achieved through our approach, contributing to the advancement of intrusion detection techniques in the context of cloud computing security.

Joint multi-type feature learning for multi-modality FKP recognition

Multimodal biometric recognition has attracted intensive attention due to its significant performance improvement for personal authentication by exploiting multiple resources of data. However, most existing multimodal biometric recognition methods tend to fuse completely different biometric traits, making them hard to explore the complementary features of the multimodal data. In this paper, we propose a new multimodal biometric descriptor by jointly learning the multi-type collaborative features of multi-modality finger-knuckle-print (FKP) images. First, we form multi-type feature vectors to capture the texture and direction patterns of the multimodal FKP images. Then, we jointly learn the feature projection to map multi-type vectors into a compact FKP descriptor. Moreover, our method automatically selects the optimal weights for multi-type features during feature learning to make the learned feature codes discriminative. Lastly, we integrate the non-overlapping block-wise histograms of the learned binary codes as the final multimodal FKP feature descriptor. The experimental results conducted on the benchmark PolyU FKP database demonstrate the effectiveness of the proposed method for multimodal FKP recognition.