Reviewerly/exHarmony huggingface dataset:
Application of Remote Sensing Data in Lithological Discrimination of Kerdous Inlier in the Anti Atlas Belt of Morocco

Remote sensing data reveals a great importance for lithological mapping due to their spatial, spectral and radiometric characteristics. Lithological mapping using spatial data is a preliminary and important step to mineral mapping. In this work, several spectral and radiometric transformations methods were applied on Landsat 8 OLI data to enhance lithological units in the study area situated in the Anti Atlas belt. The methods of Optimum Index Factor (OIF), Decorrelation Stretching (DS), Principal Components Analysis (PCA) and Band Ratioing (BR) showed good results for lithological mapping in comparison with the existing geological and field investigation. An RGB color composite of OLI bands 651 was developed for mapping lithological units of the study area by fusing optimum index factor (OIF) and decorrelation stretching methods. furthermore, Band ratios derived from image spectra were applied in two RGB color composites (7+4/2, PC1, PC2)Â and (PC1, 7/6, 3/7) providing good discrimination of the lithological units. The Landsat-8 OLI data significantly provided satisfied results for lithological mapping.
Amplifying Tests for Cross-Platform Apps through Test Patterns

Cross-platform app development has achieved meaningful results in practice with frameworks like React Native, Xamarin, and Apache Cordova.Unlike native apps, such frameworks support the development of a mobile app that can run in different platforms.Nevertheless, the literature lacks techniques to test cross-platform apps since most of the existing works focus on native Android apps.A promising strategy for native apps is to amplify test suites so that the specific characteristics of mobile apps can be tested.This paper aims to investigate the test amplification of cross-platform apps.To do so, we apply four test patterns that verify well-known characteristics of mobile computing and amplify existing test suites.The proposed approach has been implemented in a tool capable of generating Appium test scripts and was evaluated with nine cross-platform apps.The amplified test suites exercise new scenarios, uncovering 23 unique bugs in eight out of nine apps.
Certificateless Blind Signcryption With Low Complexity

Elliptic curve cryptosystem (ECC) has the merits of providing shorter key length and higher processing speed compared with other public key cryptosystems with respect to the same security level. In addition, blind signcryption is widely used for privacy-preserving applications, such as electronic cash, electronic voting, electronic auction and electric contract. In this work, we construct certificateless blind signcryption with low complexity (LC-CLBS) by combining elliptic curve blind signcryption with certificateless public key cryptosystem. Its security is based on the intractability of the discrete logarithm and computational Diffie-Hellman problems in elliptic curve. Analysis demonstrates that LC-CLBS scheme enjoys high computational efficiency and low communication overheads. LC-CLBS scheme is especially suitable to be implemented in resource-restrained devices, e.g. it is appropriate for various applications in the ubiquitous mobile computing environments.
Transforming care for people with dementia using the Internet of Things

The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 5.120 (2020 JCR, received in June 2021)
Breast Cancer Computer-aided Diagnosis System from Digital Mammograms

Recently, breast cancer is one of the most popular cancers that women could suffer from. The gravity and seriousness of breast cancer can be evidenced by the fact that the mortality rates associated with it are the second highest after lung cancer. For the treatment of breast cancer, Mammography has emerged as the one whose modality when it comes to the defection of this cancer is most effective despite the challenges posed by dense breast parenchyma. In this regard, computer-aided diagnosis (CADe) leverages the mammography systems’ output to facilitate the radiologist’s decision. It can be defined as a system that makes a similar diagnosis to the one done by a radiologist who relies for his/her interpretation on the suggestions generated by a computer after it analyzed a set of patient radiological images when making. Against this backdrop, the current paper examines different ways of utilizing known image processing and techniques of machine learning detection of breast cancer using CAD – more specifically, using mammogram images. This, in turn, helps pathologist in their decision-making process. For effective implementation of this methodology, CADe system was developed and tested on the public and freely available mammographic databases named MIAS database. CADe system is developed to differentiate between normal and abnormal tissues, and it assists radiologists to avoid missing breast abnormalities. The performance of all classifiers is the best by using the sequential forward selection (SFS) method. Also, we can conclude that the quantization grey level of (gray-level co-occurrence matrices) GLCM is a very significant factor to get robust high order features where the results are better with L equal to the size of ROI. Using an enormous number of several features assist the CADe system to be strong enough to distinguish between the different tissues.
Research on Intelligent Access Control System Based on Interactive Face Liveness Detection and Machine Vision

Abstract With the improvement of people’s safety awareness and the renewal of smart home technology, access control security has become a special concern of community, schools and enterprises. How to achieve efficient, convenient, safe and intelligent access control management is the main research direction in the field of smart home right now.Based on multi-task cascade convolutional neural network (MTCNN) and improved face recognition algorithm k-Nearest Neighbor (KNN), this paper proposes an interactive face liveness detection method by eye and mouth state.According to this method, an intelligent access control system based on machine vision is designed.The system solves the problem of identity forgery attacks by calling the camera to track the face in real time and issuing randomized action instructions to the user,after confirming that the object being detected is a living body and the face information matching is successful, the door lock will be opened.The experiment shows that the face recognition rate of the system can reach 98.3%, which has good practical significance.
Artificial Intelligence Imagery Analysis Fostering Big Data Analytics

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.
Keynote: Standards and Communities: Connected People, Consistent Data, Usable Applications

Provides an abstract of the keynote presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.
Distributed High-Degree Cubature Information Filter With Embedded Hybrid Consensus

To settle the problem of distributed nonlinear state estimation in sensor networks with naive nodes, a novel distributed high-degree cubature information filter with embedded hybrid consensus (DHCIF) is proposed. The multi-dimensional Gaussian weighted integrals involved in the filtering process are approximated by the fifth-degree cubature rule. A novel scheme, with consideration for the predicted measurement errors, is applied to compute the information contribution. With parallel consensus performed on both prior and measurement information, the proposed DHCIF is derived. The stability analysis with regard to consistency and boundedness of estimation errors for the proposed DHCIF is also developed. Finally, the effectiveness and advantage of the proposed DHCIF is validated by a typical maneuvering target tracing scenario. The experimental results indicate that the proposed DHCIF outperforms the existing algorithms in the aspects of estimation accuracy, consistency and consensus at the cost of a little more extra computation burden.
Combining SVD-DCT Image Watermarking Scheme Based on Perona-Malik Diffusion

In this paper, we propose an efficient image watermarking scheme based on combining Singular Value Decomposition (SVD) and Discrete Cosine Transform (DCT) transforms. The main idea consists of exploiting results obtained by the well-known Perona-Malik diffusion applied in image de-noising in watermarking domain by selecting significant blocks to embed a binary logo watermark. After applying the DCT on the selected blocks, the SVD is performed on each of these transformed blocks to modify a few numbers of its U matrix elements according to some defined conditions. Computer simulation and experimental results prove the efficiency of our proposed method against image processing attacks and outperform several previous schemes in terms of imperceptibility and robustness.
Implementation of Weight Sum Model(WSM) in the Selection of Football Athletes

Every soccer team has a standard that is used by coaches in determining athletes that fit the criteria owned by athletes. In this study, researchers used a decision support system (SPK) with the Weight Sum Model (WSM) method, which assesses the results of the criteria needed in the selection of soccer athletes. Decisions made in applying the WSM method will produce recommendations according to the standards used by the trainer. The purpose of this research is to apply the WSM method in selecting soccer athletes based on the desired category. The existence of this system is expected to make selection more objective for trainers in making decisions
More Optimal Difference Systems of Sets and Frequency-Hopping Sequences From Zero-Difference Functions

Difference systems of sets (DSS) and frequency-hopping sequences (FHS) are two objects with many applications in wireless communication. Zero-difference balanced function (ZDBF) and near zero-difference balanced functions (NZDBF) are two types of functions which can be used to obtain optimal DSSs and FHSs. In order to obtain more optimal DSSs and FHSs, zero-difference function (ZDF) as a generalization of ZDBF and NZDBF was recently proposed. In this paper, four classes of ZDFs with good applications are given from some known ZDBFs. It is noticed that these ZDFs are neither ZDBFs nor NZDBFs. As a result, more optimal DSSs and FHSs with new flexible parameters are obtained.
An Educational Process for Requirements Extraction and Use Case Modeling Based on Problem-Based Learning and Knowledge Acquisition

The performance of Software Engineering professionals is related, among other factors, to adequate knowledge management practices. Specifically, business vision modeling, requirements, and use-case extraction are central elements in Software Engineering, since they reflect the needs, specificities and functionalities to be considered at the software development stage. All these activities are related to knowledge acquisition and modeling. The goal of this work is to implement an educational process based on knowledge acquisition that assists in more dynamic and active pedagogical practices. This process was tested with software engineering students. The results showed advantages in the adoption of a strategy using principles of knowledge acquisition and modeling. It is possible to conclude that the introduction of the process in the classroom improves students' performance in business vision modeling and in the extraction of use cases and requirements.
Learning Recursive Bayesian Nonparametric Modeling of Moving Targets via Mobile Decentralized Sensors

Bayesian nonparametric models, such as the Dirichlet Process Gaussian Process (DPGP), have been shown very effective at learning models of dynamic targets exclusively from data. Previous work on batch DPGP learning and inference, however, ceases to be efficient in multi-sensor applications that require decentralized measurements to be obtained sequentially over time. Batch processing, in this case, leads to redundant computations that may hinder online applicability. This paper develops a recursive approach for DPGP learning and inference in which a novel Dirichlet Process prior based on Wasserstein metric is used for measuring the similarity between multiple Gaussian Processes (GPs). Combined with the GP recursive fusion law, the proposed recursive DPGP fusion approach enables efficient online data fusion. The problem of active sensing for recursive DPGP learning and inference is also investigated by uncertainty reduction via expected mutual information. Simulation and experimental results show that the proposed approach successfully learns the models of moving targets and outperforms existing benchmark methods.
An Incremental Group-Specific Framework Based on Community Detection for Cold Start Recommendation

To address cold start problem by utilizing only rating information, this paper proposes an incremental group-specific framework for recommender systems. Firstly, a decoupled normalization method is introduced to extract preference patterns from ratings. Then, two incremental community detection methods are proposed to detect evolving communities in dynamic networks for capturing interest shifts, and new users/items corresponding communities according to the missing mechanism of ratings. Finally, an incremental group-specific model is proposed to incorporate evolving communities for recommender systems. A series of empirical analysis on three datasets is conducted to validate the rationality of grouping new users/items with missingness-related information. Experimental results show that the proposed framework can achieve better performance compared with other competitive methods, and is capable of handling the cold start problem and highly scalable with incremental data.
Shared Dynamic Data Audit Supporting Anonymous User Revocation in Cloud Storage

Collusion between revoked users and cloud service providers can pose a threat to the security of cloud storage data. If the original legitimate users cannot be revoked securely, it will lead to the leakage of shared data, thus affecting the security of cloud storage. In this paper, we combine vector commitment and anonymous revocation of group signature to propose an integrity audit scheme for cloud storage data that can support data modification. The anonymity of the group signature ensures that users' privacy information will not be snooped by the server. The proposed scheme supports the dynamic operation of stored data by legitimate group users besides data owners. When the user behaves improperly, the membership can be revoked by the group manager. After the user-modified data is stored in the cloud, whether the cloud server correctly stores the data can be audited by a trusted third party. Security analysis and experimental results demonstrate that our scheme is secure and efficient.
Workflow Scheduling in Cloud Environment Using Firefly Optimization Algorithm

One of the issues in cloud computing is workflow scheduling. A workflow models the process of executing an application comprising a set of steps and its objective is to simplify the complexity of application management. Workflow scheduling maps each task to a proper resource and sorts tasks on each resource to meet some efficiency measures such as processing and transmission costs, load balancing, quality of service, and etc. Task scheduling is an NP-Complete problem. In this study, meta-heuristic firefly algorithm (FA) is used to present a workflow scheduling algorithm. The purpose of the proposed scheduling algorithm is to explore optimal schedules such that the cost of processing and transmission of the whole workflow are minimized while there will be load balancing among the processing stations. The proposed algorithm is implemented in MATLAB and its efficiency is compared with cat swarm optimization (CSO) algorithm. The evaluations show that the proposed algorithm outperforms CSO in finding better solutions.
The Analysis of English Pronunciation Errors by English Education Students of FKIP UMSU

Pronunciation is one important thing that must be mastered by the students who studies English. Good Pronunciation makes students’ English speaking skills becomes better. This research aimed at analyzing the errors made by students of English education department of FKIP UMSU in pronouncing English words. The objectives of this study were 1) to describe the opinions of English education students about their English pronunciation skills so far; 2). to analyze the forms of English pronunciation errors made by the students; 3) to explore strategies or efforts made by students to improve their English pronunciation skills; 4) to Identify obstacles found by the students in English pronunciation. Descriptive qualitative design was used in this research. The data were obtained by doing the interview and asking the students to pronounce some English words in specific topic. The findings of this research revealed that students had various opinion about their ability in English pronunciation. Some students thought that they had good English pronunciation skills; some students thought that their ability in pronouncing English words was better than before and some other students still thought that their English pronunciation skills were not good. Based on the analysis of data, the students still made some errors in pronouncing English words. In order to improve their pronunciation, the students used some strategies such as watching YouTube in English and using google voice application.
Leveraging Pre-Trained Embeddings for Welsh Taggers

While the application of word embedding models to downstream Natural Language Processing (NLP) tasks has been shown to be successful, the benefits for low-resource languages is somewhat limited due to lack of adequate data for training the models.However, NLP research efforts for low-resource languages have focused on constantly seeking ways to harness pre-trained models to improve the performance of NLP systems built to process these languages without the need to reinvent the wheel.One such language is Welsh and therefore, in this paper, we present the results of our experiments on learning a simple multi-task neural network model for partof-speech and semantic tagging for Welsh using a pre-trained embedding model from Fast-Text.Our model's performance was compared with those of the existing rule-based standalone taggers for part-of-speech and semantic taggers.Despite its simplicity and capacity to perform both tasks simultaneously, our tagger compared very well with the existing taggers.
Hybrid Defense for Deep Neural Networks: An Integration of Detecting and Cleaning Adversarial Perturbations

Nowadays, deep neural networks (DNN) have achieved significant success in computer vision. However, recent investigations have shown that DNN models are highly vulnerable to an input adversarial example. How to defense against adversarial examples is an essential issue to improve the robustness of DNN models. In this paper, we present a hybrid defense framework that integrates detecting and cleaning adversarial perturbations to protect DNN. Specifically, the detecting part consists of statistical detector and Gaussian noise injection detector which are adaptive to perturbation characteristics to inspect adversarial examples, and the cleaning part is a deep residual generative network (ResGN) for removing or mitigating the adversarial perturbations. The parameters of ResGN are optimized by minimizing a joint loss including a pixel loss, a texture loss and a task loss. In the experiments, we evaluate our approach on ImageNet and the comprehensive results validate its robustness against current representative attacks.
Biological Viral Infection Watermarking Architecture of MPEG/H.264/AVC/HEVC

This paper addresses the viral infectious watermarking (VIW) model using biological virus infection for a new-paradigm of video copyright protection of MPEG/H.264/AVC/HEVC. Our model aims to spread or infect the watermark to different codecs each time video contents are copied, edited, or transcoded. Thus, we regard the watermark as the infectious virus, the video content as the host, and the video codec as the contagion medium and then model pathogen, mutant, and contagion as the infectious watermark. Then, we define the techniques of viral infectious watermark generation, kernel-based VIW, and content-based VIW. Furthermore, we present a reversible VIW for fast infection in VIW model. This makes the video quality and strength be adaptively controlled in the infectious process. Experiment results verified that our VIW model can detect or recover the reversible watermark without loss in different codecs and also can maintain the quality of video content that is recovered to the same bit rate.
Information Visualization for Food Mobile Application: Hangri 2.0

Information is easily available through social medias, newspapers, notice boards, books, radio, television with each medium having its unique presentation techniques.This holds true for mobile applications with applications being developed to provide direct information per-taining shopping, travel, hotel and restaurant bookings, as well as reviews.To encourage usage and ensure user satisfaction, mobile applica-tions should be developed creatively with user friendly interface.It is essential for app developers to understand the techniques of infor-mation presentation.This project's goal is to develop a mobile application that can efficiently provide information on food and beverage establishments that are available throughout Universiti Malaysia Sabah's (UMS) campus.With daily foot traffic of more than thousands ranging from students, staffs, and visitors, it is essential to have a medium to convey food and beverage information that can be easily ac-cessible anytime and anywhere.Information visualization technique was used to aid information presentation in the development of a mobile app using the Android platform.This application allows users to identify information on meals that are available throughout the campus, and the food and beverage vendors will be able to market their daily specials to a wider audience.