Transfer of Learning

... Both authors gratefully acknowledge the support of the Public Sector Leadership Development Programme Steering Committee (Roma Bridger, Cathy Mercer, and Karen Taylor) and Memorial University's Gardiner Centre (Susan Arscott, Jackie Collins, and Bill Morrissey). ...

Although transfer of learning was among the very first issues addressed by early psychologists, the extant literature remains characterized by inconsistent measurement of transfer and significant variability in findings. This article presents a meta-analysis of 89 empirical studies that explore the impact of predictive factors (e.g., trainee characteristics, work environment, training interventions) on the transfer of training to different tasks and contexts. We also examine moderator effects of the relationships between these predictors and transfer. Results confirmed positive relationships between transfer and predictors such as cognitive ability, conscientiousness, motivation, and a supportive work environment. Several moderators had significant effects on transfer relationships, including the nature of the training objectives. Specifically, most predictor variables examined (e.g., motivation, work environment) had stronger relationships to transfer when the focus of training was on open (e.g., leadership development) as opposed to closed (e.g., computer software) skills. Other moderators related to the measurement of transfer also influenced transfer relationships, including situations in which transfer outcomes were obtained by the same source in the same measurement contextwhich consistently inflated transfer relationships. Findings are discussed in terms of their relevance for future research and training practice.

Artificial neural networks learn how to solve new problems through a computationally intense and time consuming process. One way to reduce the amount of time required is to inject preexisting knowledge into the network. To make use of past knowledge, we can take advantage of techniques that transfer the knowledge learned from one task, and reuse it on another (sometimes unrelated) task. In this paper we propose a novel selective breeding technique that extends the transfer learning with behavioural genetics approach proposed by Kohli, Magoulas and Thomas (2013), and evaluate its performance on financial data. Numerical evidence demonstrates the credibility of the new approach. We provide insights on the operation of transfer learning and highlight the benefits of using behavioural principles and selective breeding when tackling a set of diverse financial applications problems.

Although training is often used in child welfare as an intervention to promote effective performance on the job (transfer of learning—TOL) as well as staff retention, there is not a preponderance of empirical evidence to support this practice. The “transfer problem” is well documented in training and development literature and the relationship between training and turnover in child welfare is unclear. This article describes a longitudinal research study that attempted to promote a better understanding of the training, transfer, and turnover relationship. The research identified transfer of learning factors and an indicator of overall transfer support and explored their relationship to child welfare social worker retention. Overall transfer support, demographic variables (experience, age, education, gender) and several transfer support factors (supervisory support, coworker support, application planning, caseload size) were identified as predictors of retention. The results provide support for the use of training and development activities as part of comprehensive staff retention programs. The results also raise additional questions about the ambiguous relationship between training and turnover and suggest the need for ongoing research in child welfare regarding training, transfer of learning and staff retention.

Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI), which are not generally used. Sagittal MRIs from ADNI and OASIS data sets were employed. Experiments were conducted using Transfer Learning (TL) techniques in order to achieve more accurate results. There are two main conclusions to be drawn from this work: first, the damages related to AD and its stages can be distinguished in sagittal MRI and, second, the results obtained using DL models with sagittal MRIs are similar to the state-of-the-art, which uses the horizontal-plane MRI. Although sagittal-plane MRIs are not commonly used, this work proved that they were, at le...

Abstract—Artificial neural networks learn how to solve new problems through a computationally intense and time consuming process. One way to reduce the amount of time required is to inject pre-existing knowledge into the network. To make use of past knowledge, we can take advantage of techniques that transfer the knowledge learned from one task, and reuse it on another (sometimes unrelated) task. In this paper we propose a novel selective breeding technique that extends the transfer learning with behavioural genetics approach proposed by Kohli, Magoulas and Thomas (2013), and evaluate its performance on financial data. Numerical evidence demonstrates the credibility of the new approach. We provide insights on the operation of transfer learning and highlight the benefits of using behavioural principles and selective breeding when tackling a set of diverse financial applications problems. Keywords—transfer learning, artificial neural networks, ge-netic algorithms, population studies, ...

This paper focuses mainly on theoretical frameworks for understanding and investigating informal learning in the workplace, which have been developed through a series of large-and small-scale projects. The main conclusions are included but readers are referred to other publications for more detailed accounts of individual projects. Two types of framework are discussed. The ®rst group seeks to deconstruct the`key concepts' of informal learning, learning from experience, tacit knowledge, transfer of learning and intuitive practice to disclose the range of different phenomena that are embraced by these popular terms. The second group comprises frameworks for addressing the three central questions that pervaded the research programme: what is being learned, how is it being learned and what are the factors that in¯uence the level and directions of the learning effort?

The goal of this work is to examine how second-language learners acquire grammatical knowledge. To measure this knowledge, our research examines how native speakers of English choose between constructions that have similar meanings, but occur in different discourse contexts. An example is the dative alternation (give someone the book vs. give the book to someone). The distribution of these two constructions is explained by a four-factor model that describes the patterns or rules that underlie native-speaker usage. We hypothesized that training examples could be selected to instantiate these four factors in a way that would lead to improved learning of the dative alternation. In addition, we predicted that explicit instruction of the four grammar principles would further enhance learning. Two studies were conducted. Results showed rapid learning and good retention across a week-long delay. Examples that were labeled as “easy” or prototypical by the grammar model were learned faster t...

This paper extends recent efforts to critique and reconceive transfer by using an empirical study to rethink the
surface/structure distinction of the traditional transfer paradigm. The findings suggest that what researchers typically
consider a surface feature can present conceptual complexities for students that are more structural in nature than
previously understood. In particular,we investigated the quantitative reasoning (meaning reasoning with measurable
properties of an object) that is involved in making sense of a typical transfer situation. Two related analyses—one
focused on quantitative reasoning and one on transfer — were performed on a case study. The results document
how a student reconstructed his understanding of the relationships among quantities in a complex transfer situation
in such a way that he was able to see the situation as fundamentally proportional in nature and subsequently make
connections with previous proportional-reasoning experiences from a teaching experiment. In our discussion of the
findings, we identify four relationships between quantitative reasoning and transfer.

JANUARY/FEBRUARY 2003 17
Limitations with current approaches to the investigation of the transfer
of learning in design experiments constrain the type of information
that is available to researchers as they make design decisions. This article
addresses these limitations by presenting a reconceptualization
of transfer, called actor-oriented transfer, which emerged from design
experiment work. The merits of this alternative model are considered
in terms of the information it provides to design experimenters.

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Cornwall has implemented significant changes to the way that it delivers its safeguarding adults training. This paper outlines the benefits of combining safeguarding adults, the Mental Capacity Act 2005 (HM Government, 2005a) and equality and diversity training within a human rights framework. It examines the notion of learning transfer and considers how the design and delivery of training can improve

The paper reviews theories of learning transfer from the perspective of whether they contain guidelines for generating educational approaches to the production of facilitative transfer. Two classes of theories are described. The first class of theories are based on the notion that the conditions for transfer are established when an original learning

The aim of all education is to apply what we learn in different contexts and to recognise and extend this learning to new situations. Virtual learning environments can be used to build skills. Recent research in cognitive psychology and education has shown that acquisitions are linked to the initial context. This provides a challenge for virtual reality in education or training. A brief overview of transfer issues highlights five main ideas: (1) the type of transfer enables the virtual environment (VE) to be classified according to what is learned; (2) the transfer process can create conditions within the VE to facilitate transfer of learning; (3) specific features of VR must match and comply with transfer of learning; (4) transfer can be used to assess a VE's effectiveness; and (5) future research on transfer of learning must examine the singular context of learning. This paper discusses how new perspectives in cognitive psychology influence and promote transfer of learning through the use of VEs.

This article traces the uncomfortable relationship that Writing Studies has had with the concept of learning transfer. It reviews attitudes to learning transfer in writing theory influenced by rhetorical genre studies, activity theory and situated learning, It then reviews learning transfer theory itself, a field seldom referred to explicitly in Writing Studies. It concludes with a synthesis in which transfer theory is brought to bear on Writing Studies, suggesting directions for the development of research and pedagogical practices related to business and technical communication.

Millions of people with speech and hearing impairments communicate with sign languages every day. For hearingimpaired people, gesture recognition is a natural way of communicating, much like voice recognition is for most people. In this study, we look at the issue of translating/converting sign language to text and propose a better solution based on machine learning techniques. We want to establish a system that hearing-impaired people may utilise in their everyday lives to promote communication and collaboration between hearing-impaired people and people who aren't trained in American Sign Language (ASL). To develop a deep learning model for the ASL dataset, we'll use a technique called Transfer Learning in combination with Data Augmentation.

The outbreak of novel coronavirus disease (COVID- 19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and computed tomography (CT) images along with DL methods for classifying COVID-19 affected versus pneumonia. The applications of DL techniques to medical images are further described in terms of image localization, segmentation, registration, and classification leading to COVID-19 detection. The reviews of recent papers indicate that the highest classification accuracy of 99.80% is obtained when InstaCovNet-19 DL method is applied to an X-ray dataset of 361 COVID-19 patients, 362 pneumonia patients and 365 normal people. Furthermore, it can be seen that the best classification accuracy of 99.054% can be achieved when EDL_COVID DL method is appl...

Quest’elaborato verte sullo studio delle reti neurali artificiali, e si prefigge principalmente due scopi: il primo consiste nel comprendere il funzionamento che sta alla base delle reti neurali ed il loro apprendimento; il secondo sta nell’affrontare studio ed implementazione dei modelli più all’avanguardia delle Convolutional Neural Networks
e saggiare la loro nota efficacia nei compiti di visione artificiale, in particolare nella classificazione.
Nel Capitolo 1 viene fornita un’introduzione alle reti neurali, che si conclude con la proposta di un problema da risolvere con un percettrone multi-strato; nel Capitolo 2 si percorre passo passo l’implementazione da zero di quest’ultimo, fino a portare a termine l’effettivo addestramento; il Capitolo 3 introduce le Convolutional Neural Networks, ne spiega l’architettura ed i loro ultimi successi; nel Capitolo 4 vengono implementate queste reti e testate su un due dataset diversi; il Capitolo 5 presenta un’architettura allo stato dell’arte che viene confrontata con quella del capitolo precedente; nel Capitolo 6 viene analizzato un caso d’uso industriale della classificazione, affrontato sfruttando il transfer-learning; il Capitolo 7, infine, espone le riflessioni sul
lavoro svolto e conclude l’elaborato.

In this paper, we proposed a transfer learning-based English language learning chatbot, whose output generated by GPT-2 can be explained by corresponding ontology graph rooted by fine-tuning dataset. We design three levels for systematically English learning, including phonetics level for speech recognition and pronunciation correction, semantic level for specific domain conversation, and the simulation of "free-style conversation" in English-the highest level of language chatbot communication as 'free-style conversation agent'. For academic contribution, we implement the ontology graph to explain the performance of free-style conversation, following the concept of XAI (Explainable Artificial Intelligence) to visualize the connections of neural network in bionics, and explain the output sentence from language model. From implementation perspective, our Language Learning agent integrated the mini-program in WeChat as front-end, and fine-tuned GPT-2 model of transfer learning as back-end to interpret the responses by ontology graph. All of our source codes have uploaded to GitHub: https://github.com/p930203110/EnglishLanguageRobot.

Traditionally, data mining algorithms and machine learning algorithms are engineered to approach the problems in isolation. These algorithms are employed to train the model in separation on a specific feature space and same distribution. Depending on the business case, a model is trained by applying a machine learning algorithm for a specific task. A widespread assumption in the field of machine learning is that training data and test data must have identical feature spaces with the underlying distribution. On the contrary, in real world this assumption may not hold and thus models need to be rebuilt from the scratch if features and distribution changes. It is an arduous process to collect related training data and rebuild the models. In such cases, Transferring of Knowledge or transfer learning from disparate domains would be desirable. Transfer learning is a method of reusing a pre-trained model knowledge for another task. Transfer learning can be used for classification, regression and clustering problems. This paper uses one of the pre-trained models-VGG-16 with Deep Convolutional Neural Network to classify images.

1. Einführung ................................................................  1
2. „Sozialplanung“ und die Anfänge der 
    Lerntheorie...................................................................2
3. Die Theorie der logischen Typen ..............................2
4. Kontextualisierung .....................................................2
5. Lernen als Veränderung – Lernen NULL ...............3
6. Lernen I ....................................................................3
7. Lernen II – Gewohnheits- und Charakterbildung.......3
8. Lernen III ....................................................................4
9. Lernen IV – Ontogenese plus Phylogenese ........5
    Anwendungsversuch I: Lernen II im Kulturvergleich 5
    Anwendungsversuch II: Die pädagogische Praxis 5
10. Kritik I: Übertragung der logischen Typen ........5
    Kritik II: Fehlende Empirie ......................................6
    Kritik III: Epimenides’ Paradoxon ...............................6
Quellen und Literatur: .....................................................6

We present EleMental: The Recurrence, a novel game that provides computer science students the opportunity to write code and perform interactive visualizations to learn about recursion through depth-first search of a binary tree. We designed the game to facilitate maximum transfer of learning to writing real programs, while also providing for interactive visualizations. We conducted a study with computer science majors to measure the impact of the game on learning and on attitudes toward educational games. Our results demonstrate the enthusiasm students have for learning games and provide insight into how such games should be constructed.

Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions on the retina that effect vision. If it is not detected early, it can lead to blindness. Unfortunately, DR is not a reversible process, and treatment only sustains vision. DR early detection and treatment can significantly reduce the risk of vision loss. The manual diagnosis process of DR retina fundus images by ophthalmologists is time-, effort-, and costconsuming and prone to misdiagnosis unlike computer-aided diagnosis systems. Recently, deep learning has become one of the most common techniques that has achieved better performance in many areas, especially in medical image analysis and classification. Convolutional neural networks are more widely used as a deep learning method in medical image analysis and they are highly effective. For this article, the recent state-of-theart methods of DR color fundus images detection and classification using deep learning techniques have been reviewed and analyzed. Furthermore, the DR available datasets for the color fundus retina have been reviewed. Difference challenging issues that require more investigation are also discussed.

Image classification is getting more attention in the area of computer vision. During the past few years, a lot of research has been done on image classification using classical machine learning and deep learning techniques. Presently, deep learningbased techniques have given stupendous results. The performance of a classification system depends on the quality of features extracted from an image. The better is the quality of extracted features, the more the accuracy will be. Although, numerous deep learning-based methods have shown enormous performance in image classification, still due to various challenges deep learning methods are not able to extract all the important information from the image. This results in a reduction in overall classification accuracy. The goal of the present research is to improve the image classification performance by combining the deep features extracted using popular deep convolutional neural network, VGG19, and various handcrafted feature extraction methods, i.e., SIFT, SURF, ORB, and Shi-Tomasi corner detector algorithm. Further, the extracted features from these methods are classified using various machine learning classification methods, i.e., Gaussian Naïve Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBClassifier) classifier. The experiment is carried out on a benchmark dataset Caltech-101. The experimental results indicate that Random Forest using the combined features give 93.73% accuracy and outperforms other classifiers and methods proposed by other authors. The paper concludes that a single feature extractor whether shallow or deep is not enough to achieve satisfactory results. So, a combined approach using deep learning features and traditional handcrafted features is better for image classification.

Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for features extracting and classification. The performance of the iris recognition system is tested on four public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The results show that the proposed system is achieved a very high accuracy rate.

This dissertation could not have been completed without the commitment, contributions, dedication, interest, love, and patience furnished by my wife and best friend, Louise R. Levergneux. Between 2000 and 2007 my wife took on the status of doctoral student widow. Anyone who has tried to complete a PhD will know what that meant-the brooding silence, unanswered questions, late nights alone, tired looks, severe glances, impatient responses, and overall foul personality. There were some hard times during those seven years, but Louise followed me to Kent, Ohio (USA), from Ottawa, Ontario (CANADA), and stayed with me. She spent much of her time training our beautiful Sheltie, Imperial Topaz, while keeping our home in order, looking after the bills, cooking meals, creating and marketing her artists' books, and continuing her professional work as a visual artist. I look forward to spending more time with her and Topaz and repay them with a more pleasant personality and increased sense of humour once I have my time back from this life-changing endeavour.

This article briefly discusses the correlation between critical thinking and the transfer of knowledge and skills, and the two different aspects of their combination, in making up the coherent learning process. The two aspects are:
a)      Critical thinking as a skill to be transferred in different learning experiences;
b)      The role of “Critical Thinking” as a vehicle in maximizing transfer and ensuring a positive transfer of knowledge and skills
In the second part of the article, two strategies, which demonstrate how critical thinking can enhance the transfer of knowledge and skills in the learning process of Chinese language, are addressed.

This chapter reports methods, findings, and implications for research and policy from 10 metaanalytic reviews of the effects on non-arts cognition from instruction in various art forms. Three analyses demonstrate generalizable, causal relationships: classroom drama and verbal achievement, music listening and spatial reasoning, and music learning and spatial reasoning.

Mango is the popular tropical fruit that is consumed all around the world. India is the largest producer of mango and
it is exported to many countries. Proper classification of mango is must in agro-based industries. The conventional methods were
time-consuming and tedious. The aim of this paper is to develop an automated system which can recognize and classify mango
images. The identification of mango species is necessary in Indian market. Hence in this proposed paper, a deep learning
approach known as Convolutional Neural Networks (CNN) using transfer learning is used to classify mango species with high
accuracy and implemented by retraining the pretrained CNN. The Inception V3 model is trained on various image dataset of 12
categories of mango. This proposed work is simple and efficient. This model achieves an overall test accuracy of 97.6% for the
task of classification of mango images.

According to data obtained by the World Health Organization, the global pandemic of COVID-19 has severely impacted the world and has now infected more than eight million people worldwide. Wearing face masks and following safe social distancing are two of the enhanced safety protocols need to be followed in public places in order to prevent the spread of the virus. To create safe environment that contributes to public safety, we propose an efficient computer vision based approach focused on the real-time automated monitoring of people to detect both safe social distancing and face masks in public places by implementing the model on raspberry pi4 to monitor activity and detect violations through camera. After detection of breach, the raspberry pi4 sends alert signal to control center at state police headquarters and also give alarm to public. In this proposed system modern deep learning algorithm have been mixed with geometric techniques for building a robust modal which covers three aspects of detection, tracking, and validation. Thus, the proposed system favors the society by saving time and helps in lowering the spread of corona virus. It can be implemented effectively in current situation when lockdown is eased to inspect persons in public gatherings, shopping malls, etc. Automated inspection reduces manpower to inspect the public and also can be used in any place.

Technological advances enable the use of innovative learning tools for education. This work gives a brief insight into the potential and challenges of using collaborative Augmented Reality (AR) in education within the greater context of immersive virtual learning environments. As an example the experiences made during the development of a collaborative AR application specifically designed for mathematics and geometry education called Construct3D are summarized. Construct3D is based on the mobile collaborative AR system "Studierstube". We describe our efforts in developing a system for the improvement of spatial abilities and maximization of transfer of learning. Means of application and integration in mathematics and geometry education at high school as well as university level are being discussed. Anecdotal evidence supports our claim that Construct3D is easy to learn, encourages experimentation with geometric constructions and improves spatial skills.

In an attempt to illustrate the application of cognitive science principles to hard AI problems in machine learning we propose the LIDA technology, a cognitive science based architecture capable of more human-like learning. A LIDA based software agent or cognitive robot will be capable of three fundamental, continuously active, humanlike learning mechanisms: 1) perceptual learning, the learning of new objects, categories, relations, etc., 2) episodic learning of events, the what, where, and when, 3) procedural learning, the ...

Chatbot is a software agent that is used to conduct intelligent conversations between machines and humans. Chatbots are mostly depend on Natural Language Processing (NLP). In this paper, the design and implementation of a chatbot are provided to help Kurdish speakers in using online conversations via texts to find answers instead of direct contact with human agents. The NLP-based software agent is implemented using the Chatfuel platform. Chatfuel uses artificial intelligence to communicate with humans by simulating human conversations through voice commands or texts. The proposed chatbot is tested on an electronic tourist guide that helps visitors to the religious places in the mountainous village of Barzanja that is located in Iraqi Kurdistan. The case study is conducted by using three-hundred questions and answers. One hundred volunteers participated in this study. The participant asks a question and the bot provides an answer if it recognizes the question, otherwise it provides a default answer along with a suggestion of how to use the system properly. The data of these experiment is collected, analyzed, and problems regarding Kurdish language are detected. Designing software agents for processing Kurdish texts faces many challenges. Kurdish texts have not yet been processed using natural language processing (NLP). In addition, Kurdish font disorder and the lack of standardized keyboards and writing styles makes processing Kurdish text difficult. Furthermore, Kurdish language consists of variety of different dialects with different typing styles. In this research, we specifically focus on the design of a software agent for the Central Kurdish (Sorani) dialect. We managed to solve some of the problems related to the Kurdish language and suggest solutions to others.

This article explores what metacognition is, why it is important and how it develops in children. It argues that teachers need to help children develop metacognitive awareness, and identifies the factors which enhance metacognitive development. Metacognitive thinking is a key element in the transfer of learning. The child's development of metacognitive skills is defined as meta-learning. Meta-teaching strategies can help mediate the metacognitive skills of children, help to stimilate children's metacognitive thinking. The article draws upon reserch currently being undertaken in London schools on raising achievement in thinking and learning through developing the metacognition of children as learners in schools.

"Approaches to the longstanding challenges of ‘integrating’ subject-based and work-based knowledge have typically focused on questions of how learning can be ‘transferred’ from
one setting to another, usually from theory into practice.
What has continually dogged attempts at transfer is how to overcome the assumed ‘abstract’ nature of theory in relation to the assumed ‘real’ nature of practice. This is often seen as a
single movement as encapsulated in the term ‘from theory to practice’. We have developed a fresh approach that concentrates on different forms of knowledge and the ways in which these are contextualised and ‘recontextualised’ as people move between
different sites of learning in colleges and workplaces."

In the construction industry, some progress have been achieved by researchers to design and implement environments for task training using VR technology and its derivatives such as Augmented and Mixed Reality. Although, these developments have been well recognized at the application level, however crucial to the virtual training system is the effective and reliable measurement of training performance of the particular skill and handling the experiment for long-run. It is known that motor skills cannot be measured directly, but only inferred by observing behaviour or performance measures. The typical way of measuring performance is through measuring task completion time and accuracy, but can be supported by indirect measurement of some other factors. In this paper, a virtual crane training system has been developed which can be controlled using control commands extracted from facial gestures and is capable to lift up loads/materials in the virtual construction sites. Then, we integrate affective computing concept into the conventional VR training platform for measuring the cognitive load and level of satisfaction during performance using human's forehead bioelectric-signals. By employing the affective measures and our novel control scheme, the designed interface could be adapted to user's affective status during the performance in real-time. This adaptable user interface approach helps the trainee to cope with the training for long-run performance, leads to gaining more expertise and provides more effective transfer of learning to other operation environments. The detailed methodology of the affective control is presented in the paper. The results and future applications of the proposed method for disabled users, especially from neck down are discussed.

In the 1980s to 1990s, studies of perceptual learning focused on the specificity of training to basic visual attributes such as retinal position and orientation. These studies were considered scientifically innovative since they suggested the existence of plasticity in the early stimulus-specific sensory cortex. Twenty years later, perceptual training has gradually shifted to potential applications, and research tends to be devoted to showing transfer. In this paper we analyze two key methodological issues related to the interpretation of transfer. The first has to do with the absence of a control group or the sole use of a test-retest group in traditional perceptual training studies. The second deals with claims of transfer based on the correlation between improvement on the trained and transfer tasks. We analyze examples from the general intelligence literature dealing with the impact on general intelligence of training on a working memory task. The re-analyses show that the reports of a significantly larger transfer of the trained group over the test-retest group fail to replicate when transfer is compared to an actively trained group. Furthermore, the correlations reported in this literature between gains on the trained and transfer tasks can be replicated even when no transfer is assumed.

Artificial intelligence in combination with modern technologies including medical screening devices has the potential to deliver better management services to deal with chronic diseases with higher accuracy, efficiency, and satisfaction. With the recent evolution in digitized data acquisition, computer vision and machine learning, AI solutions are spreading into areas which were previously examined by well-trained clinicians. Early diagnosis of diabetic retinopathy (DR) and foot ulcers (DFU) occurrence through image analysis is in high demand as many individuals are left without any supervision due to the limited resources such as trained clinicians or suitable equipment especially, in rural areas. Furthermore, the existing system will become even more insufficient as the number of people with diabetes increases. In this research paper, we propose a prototype that involves an autonomous system called an Intelligent Diabetic Assistant (IDA), which decides the diagnosis and the treatment prioritization depending upon the observations appeared in the screen. The IDA consists of knowledge-based modules for severity level-based classification, clinical decision support and near real- time foot ulcer detection and boundary screening. We use the System Usability Scale (SUS) in terms of performance; learn ability, and satisfaction to measure the usability of the IDA. The mean SUS score was 88.5, demonstrating good but not exceptional system usability. We perform our experiments with clinicians who have been involved in diabetic care.