Miracle-dz/UniFAR-FTUs huggingface dataset:
mSkinColorCalib - Computer vision-based system for color calibration of mobile-acquired skin lesion images

Mobile teledermatology (MT) applications have risen as a promising tool to assist healthcare professionals in the diagnosis of skin cancers. These applications rely on the extraction of color features, which are sensitive to both camera and lighting conditions. This thesis introduces the development of a color calibration system targeted at skin lesion images captured with a mobile device. By using a color compensation chart to estimate the relationship between the colors in the image and the respective reference colors, the implemented system reduces the influence of not only the surrounding environment, as well as the one resulting from the camera used for such e↵ect itself. The methodology is based on a phased approach: the pre-acquisition stage and a post-acquisition stage. The former determines the optimal camera settings for image capture by acquiring images of the color compensation chart with di↵erent camera settings and calculating the average color di↵erence of the patches; the latter accomplishes the calibration task itself which consists of two steps: the profile creation process and the profile application process. In the profile creation process an image of the color compensation chart is acquired, and a relationship between the captured colors and reference colors is estimated. This estimation is embedded in a color profile which is applied to images captured under the same light conditions as those of the chart during the profile application process. The performance of this system was evaluated in two sets of experiments. The first set was performed under known light conditions of five di↵erent color temperatures and with three di↵erent mobile devices, whereas the second set of experiments took place under arbitrary light conditions with the same mobile devices. The performance was evaluated based on the system’s ability to approximate captured colors to the reference colors and was also evaluated in its adaptability to calibrate skin lesion images. The results obtained suggest that the implemented method can decrease the color xi
Towards a secure incremental proxy re‐encryption for e‐healthcare data sharing in mobile cloud computing

Cloud computing provides universal access to a pool of shared resources to numerous stakeholders/shareholders of the e‐healthcare industry. The speedy adoption of cloud computing has inevitably raised security concerns for the outsourced data. Since mobile devices are resource constrained, the security solutions must discharge the computing comprehensive operations on the cloud for implementation. Conventionally, any modification to uploaded record would compel the mobile client to encrypt and compute the hash value from scratch. Through this paper, we intend to propose a pairing‐free incremental proxy re‐encryption scheme, without certificates, which would run proportionate to the number of modifications in time, instead of the document length for improvement in the file modification tasks. The proposed scheme shows a significant improvement in the file modification system regarding the energy consumption and the turnaround timer taken. The proposed scheme has been verified through a formal method using Z3 solver.
Incumbent System Context and Job Outcomes of Effective Enterprise System Use

Enterprise system (ES) implementations frequently fail to deliver job benefits for employees, many of whom are dissatisfied with these systems that were implemented to support them in their jobs. The literature is clear that the realization of job benefits depends on how these systems are used, motivating us to focus on the determinants and outcomes of effective ES use. Focusing on employees' use of systems to support their work processes, we examine how employees' pre-implementation context-specifically, the use of an incumbent system and the associated work processes-affects their performance expectancy of a new ES and, consequently, their effective use of the ES and the resulting job outcomes. Our results suggest that (1) employees' perceptions of two facets of information transparency based on incumbent system use, namely information visibility and information credibility, have different impacts on employees' performance expectancy of a new ES depending on their perceptions of process standardization in the incumbent system context, and that (2) effective ES use mediates the impact of pre-implementation performance expectancy on post-implementation user satisfaction and, consequently, job effectiveness. Our findings provide insights into the mechanisms linking the context of using an incumbent system to post-implementation effective ES use and job outcomes, thereby integrating perspectives from technology acceptance and use, IS success, and work design.
A Multi-dimensional Model for Determining Susceptibility to Unintentional Insider Threats: the Case of Social Engineering Through Phishing

Phishing threats are real and are ever increasing in their reach and devastating effects. This study delves into the role of cognitive processing in detecting and curtailing phishing attacks. The proposed model is grounded on the Elaboration Likelihood Model and is tested empirically using data from 192 cases. Data was collected through direct observations of phishing susceptibility and self-reported questionnaires after staging a phishing attack targeting a university population in Nairobi, Kenya. The model was found to have excellent fit and was able to account for 50.8% of a person's cognitive processing of a phishing attack, 69.5% of their ability to detect phishing threats and could predict 28% of their actual phishing susceptibility. Analysis was done to test 25 hypothesis, and to examine the mediating effects of cognitive processing and threat detection. In addition, multigroup moderation analysis was done to examine if the model was invariant based on the level of knowledge. Results indicate that threat detection has the strongest effect in reducing phishing susceptibility. Threat detection was found to be what explains why people who expend cognitive effort processing phishing communication are less likely to fall for phishing threats.
Densely Connected Neural Network With Unbalanced Discriminant and Category Sensitive Constraints for Polyp Recognition

Automatic polyp recognition in endoscopic images is challenging because of the low contrast between polyps and the surrounding area, the fuzzy and irregular polyp borders, and varying imaging light conditions. In this article, we propose a novel densely connected convolutional network with “unbalanced discriminant (UD)” loss and “category sensitive (CS)” loss (DenseNet-UDCS) for the task. We first utilize densely connected convolutional network (DenseNet) as the basic framework to conduct end-to-end polyp recognition task. Then, the proposed dual constraints, UD loss and CS loss, are simultaneously incorporated into the DenseNet model to calculate discriminative and suitable image features. The UD loss in our network effectively captures classification errors from both majority and minority categories to deal with the strong data imbalance of polyp images and normal ones. The CS loss imposes the ratio of intraclass and interclass variations in the deep feature learning process to enable features with large interclass variation and small intraclass compactness. With the joint supervision of UD loss and CS loss, a robust DenseNet-UDCS model is trained to recognize polyps from endoscopic images. The experimental results achieved polyp recognition accuracy of 93.19%, showing that the proposed DenseNet-UDCS can accurately characterize the endoscopic images and recognize polyps from the images. In addition, our DenseNet-UDCS model is superior in detection accuracy in comparison with state-of-the-art polyp recognition methods. Note to Practitioners—Wireless capsule endoscopy (WCE) is a crucial diagnostic tool for polyp detection and therapeutic monitoring, thanks to its noninvasive, user-friendly, and nonpainful properties. A challenge in harnessing the enormous potential of the WCE to benefit the gastrointestinal (GI) patients is that it requires clinicians to analyze a huge number of images (about 50 000 images for each patient). We propose a novel automatic polyp recognition scheme, namely, DenseNet-UDCS model, by addressing practical image unbalanced problem and small interclass variances and large intraclass differences in the data set. The comprehensive experimental results demonstrate superior reliability and robustness of the proposed model compared to the other polyp recognition approaches. Our DenseNet-UDCS model can be further applied in the clinical practice to provide valuable diagnosis information for GI disease recognition and precision medicine.
Trends of mobile learning: A review of the top 100 highly cited papers

Highly cited papers can be representative of the latest changes in specific research issues, as they reflect the important issues or research trends that researchers have paid attention to recently. Through reading highly cited papers, researchers can identify some important issues for consideration in their own research, which can then catch other researchers? eyes easily and efficiently. In order to explore the feature of the latest changes in mobile learning, in this study, a review of the top 100 highly cited mobile learning papers was conducted. According to the review, four advantages were identified from the highly cited mobile learning studies: proposing new learning strategies, applying mobile learning to seldom-considered subjects, applying mobile learning to seldom-investigated domains and investigating seldom-discussed issues. It was found that the studies focused more on comparing different mobile learning modes for finding more effective mobile learning approaches rather than comparing the impacts of mobile learning with traditional instruction. Moreover, the studies focused more on learners? higher order thinking performance and learning behaviors. Some suggestions, differing from those presented by previous mobile learning reviews, are provided as reference for researchers to conduct related research. Practitioner Notes What is already known about this topic Mobile learning has been regarded as a tool for improving learners? learning and bridging students? learning in class and in the field. Research trends can be highlighted and provide a more accurate view of the publication patterns. Highly cited papers can be used to identify the advance of the research foci. What this paper adds Most highly cited studies mainly focused on comparing the different mobile learning modes to find more effective approaches for conducting mobile learning activities. The mobile learning places have changed from classrooms to real-world contexts; in addition, the number of across-context studies is increasing. The highly cited papers started to focus more on learners? higher-order thinking performance and learning behaviors. Implications for practice and/or policy Researchers can consider proposing new learning strategies, applying mobile learning to seldom-considered subjects, applying mobile learning to seldom-investigated domains, and investigating seldom-discussed issues. Adopting mixed methods is becoming the mainstream of mobile learning studies. Several seldom-discussed issues, such as higher order thinking, collaboration/communication skills, and learning behaviors are potential mobile learning research topics.
Pervasive Data Science on the Edge

Proliferation of sensors into everyday environments is resulting in a connected world that generates large volumes of complex data. This data is opening new scientific and commercial investigations in fields such as pollution monitoring and patient health monitoring. Parallel to this development, deep learning has matured into a powerful analytics technique to support these investigations. However, computing and resource requirements of deep learning remain a challenge, often forcing analysis to be carried at remote third-party data centers. In this paper, we describe an alternative computing as a service model where available smart devices opportunistically form micro-data centers that can support deep learning-based investigations of data streams generated by sensors. Our model enables smart homes, smart buildings, smart offices, and other types of smart spaces to become providers of powerful computation as a service, enabling edge analytics, and other applications that require pervasive (in-space) decisioning.
THE FACTORS OF INITIAL RETURN RELATED TO IPO COMPANIES ON THE INDONESIA STOCK EXCHANGE

Companies that conduct IPOs will increase company’s value with an optimal capital structure. Initial return is a profit that investors can obtain from the initial share price is lower than the opening price of the secondary shares on the first day. Underpricing conditions occurs because the initial stock price is lower than the secondary stock price on the first day. This study aimed to analyze factors that impact initial returns on companies that conduct IPOs on the Indonesia Stock Exchange, analyze the effects of financial factors (ROE, DER, and BI Rate) and non-financial factors (professional auditors and underwriters) on initial returns to companies conducting IPOs in IDX, and how the behavior of investors towards those analysis. The linear regression data processing using SPSS 16 produced result that only the BI Rate variable which affected the initial return on the seven days, 30 days, and one year after the IPO observation period. The statistical results show the best r-square value is 17.6 percent, which means that the independent variables can be used to explain the effect to the initial return on 17.6 percent.
A Survey on Optical Technologies for IoT, Smart Industry, and Smart Infrastructures

In the Internet of Things (IoT), a huge number of sensors, actuators and other equipment for data acquisition and processing will be interconnected by means of an omnipresent communication network able to efficiently support heterogeneous transmission technologies and applications. On the one hand, advanced optical communication systems, which already play a significant role in modern networks, are currently evolving to meet very high requirements of modern applications. On the other hand, there are already many ways to utilize optical components and effects for building precise, efficient, and reliable sensors. Thus, optical technologies have the potential to greatly help in realizing future smart infrastructures and systems. This paper gives an overview of currently available and emerging optical technologies for sensing and communication applications and reviews their possible application in the context of the IoT for realizing smart systems and infrastructures.
Design of haptic feedback in pedal based UGV teleoperation to enhance situation awareness

Teleoperation of Unmanned Ground Vehicles (UGVs) is dependent on several factors as the human operator is physically detached from the UGV. The lack of Situation Awareness (SA) can lead to undesired behaviour of the UGV as the exclusive use of visual information to simultaneously perform the main task and to be aware of possible impediments to UGV operation becomes a very challenging task for a single human operator. This assignment is being done in cooperation with i-Botics, a joint research and development center founded by TNO and the University of Twente. i-Botics focuses on innovation for interaction robotics and telerobotics where the center of interest lies in designing systems in such a way that the operator is well supported with the best perceptual and manipulation tools in order to increase his or her situation awareness to perceive the remote environment as intuitive as possible. The goal of this assignment is to design and implement haptic feedback for a remote pedal based robotic platform where the feedback is based on the presence of obstacles around the UGV. The main research question in this project is:"How can haptic feedback be designed in order to improve the situation awareness of a human operator in remote UGV control? "
Boosted projections and low cost transfer learning applied to smart surveillance

Computer vision is an important area related to understanding the world through images. It can be used in biometrics, by verifying whether a given face is of a certain identity, used to look for crime perpetrators in an airport blacklist, used in human-machine interactions and other goals. Deep learning methods have become ubiquitous in computer vision achieving many breakthroughs, making possible for machines, for instance, to verify if two photos belong to the same person with human-level skill. This work tackles two computer vision problems applied to surveillance. First, we explore deep learning methods for computer vision in the task of face verification and second, we explore dimensionality reduction techniques for the task of detection.
Technology Acceptance in the Use of Social Networks by Teachers and Employees of Education Offices in Ahwaz.

Educational systems and stakeholders embark upon changes and adaptation with changes on the basis of social and historical developments. One of the influential factors in current world is emerging technologies like social networks that can affect educational systems and stakeholders. This research deal with studying the acceptance of social networks by those involved in the educational system of Ahwaz. For this purpose, a sample of 523 people was selected by random sampling from among teachers and administrative employees and analyzed by a 53-question questionnaire with Cronbach's alpha coefficient of 0.88 based on the TAM2 model and SEM method. The results showed that teachers did not have high acceptance for using social networks, but administrative employees welcomed this phenomenon.
An E-learning Acceptance Evaluation Through UTAUT Model in a Postgraduate Program

This study aims to evaluate e-learning acceptance through the UTAUT model by showing the contributing variables to the acceptance of e-learning in a Postgraduate Program at Universitas Negeri Makassar, Indonesia. This study was an ex post facto study with 170 samples distributed proportionally. The data were collected through a questionnaire that was developed from UTAUT model variables and indicators. The data collected were analyzed by path. The results of the e-learning acceptance evaluation based on the hypothesis test showed that facilitating conditions, behavioral intention, effort expectancy, performance expectancy, and social influence significantly and positively affected behavioral intention. Facilitating conditions and behavioral intention significantly and positively affected the e-learning acceptance. Variables that greatly contributed to the higher or lower e-learning acceptance were facilitating conditions and behavioral intention. Facilitating conditions were strongly affected by the students’ knowledge and internet speed. Meanwhile, the behavioral intention was strongly influenced by the level of students’ belief in the future of e-learning and students’ eagerness that e-learning be integrated in every subject. Nevertheless, social influence variables need more attention for implementing a better and sustainable e-learning system.
Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments

Recent work on evaluating grammatical knowledge in pretrained sentence encoders gives a fine-grained view of a small number of phenomena. We introduce a new analysis dataset that also has broad coverage of linguistic phenomena. We annotate the development set of the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018) for the presence of 13 classes of syntactic phenomena including various forms of argument alternations, movement, and modification. We use this analysis set to investigate the grammatical knowledge of three pretrained encoders: BERT (Devlin et al., 2018), GPT (Radford et al., 2018), and the BiLSTM baseline from Warstadt et al. We find that these models have a strong command of complex or non-canonical argument structures like ditransitives (Sue gave Dan a book) and passives (The book was read). Sentences with long distance dependencies like questions (What do you think I ate? ) challenge all models, but for these, BERT and GPT have a distinct advantage over the baseline. We conclude that recent sentence encoders, despite showing near-human performance on acceptability classification overall, still fail to make fine-grained grammaticality distinctions for many complex syntactic structures.
Deep learning with noisy supervision

Central to many state-of-the-art classification systems via deep learning is sufficient accurate annotations for training. This is almost the bottleneck of all machine learning algorithms deployed with deep neural networks. The dilemma behind such a phenomenon is essentially the trade-off between the low expensive model design and the low expensive sample collection. For practical purposes to alleviate this issue, learning with noisy supervision is a critical solution in the Big Data era, since the noisily annotated data on the social websites and Amazon Mechanical Turk platforms can be easily acquired. Therefore, in this dissertation, we explore to solve the fundamental problems when training deep neural networks with noisy supervision. Our first work is to introduce the low expensive noise structure information to overcome the decoupling bias issue existed learning with noise transition. We study the noise effect via a variable whose structure is implicitly aligned by the provided structure knowledge. Specifically, a Bayesian lower bound is deduced as the objective and it naturally degenerates to previous transition models in the case that there is no structure information available. Furthermore, a generative adversarial implementation is given to stably inject the structure information when training deep neural networks. The experimental results show the consistently improvement in the different simulated noises and the real-world scenario. Our second work targets to substitute the previous ill-posed stochastic approximation to the noise transition with a rigorous stochastic reallocation regarding the confusion matrix. This work discovers the reason that causes the unstable issue in modeling the noise effect by a neural Softmax layer and introduces a Latent Class-Conditional Noise model to overcome it. In addition, a computational effective dynamic label regression method is deduced for optimization, which stochastic trains the deep neural network and safeguards the noise transition estimation. The proposed method achieves the state-of-the-art results on two toy datasets and two large real-world datasets. The last work aims to alleviate the difficulty that the ideal assumption on the accurate noise transition is usually not fulfilled and the noise could still pollute the classifier in the back-propagation. We specially introduce a quality embedding factor to apportion the reasoning in the backpropagation, yielding a quality-augmented class-conditional noise model. On the network implementation, we elaborately design a contrastiveadditive layer to infer the latent variable and deduce a stochastic optimization via reparameterization tricks. The results on a noisy web dataset and a noisy crowdsourcing dataset confirm the superiority of our model in the accuracy and interpretability.