Deep Learning

This article aims to develop learning methods to establish the character of students as learners. Learning method developed is deep learning to establish the character of students as learners. This research method used document study based on research results and literature review. Deep learning is a learning that leverages the power of new partnerships to engage students in practicing the learning process through discovering and mastering existing knowledge and then creating and using new knowledge in the world so that the outcomes of deep learning seek to increase students' understanding of their strengths and weaknesses, data collection regarding student profile information and trust value building among members of student learning groups. Abstrak Tujuan dari penulisan ini adalah untuk mengembangkan metode pembelajaran mendalam untuk membentuk karakter siswa sebagai pembelajar. Metode penelitian yang digunakan dalam penulisan ini adalah studi dokumen berbasis hasil penelitian dan kajian literatur. Pembelajaran mendalam merupakan pembelajaran yang memanfaatkan kekuatan kemitraan baru untuk melibatkan para siswa dalam mempraktekkan proses pembelajaran melalui menemukan dan menguasai pengetahuan yang ada dan kemudian menciptakan serta menggunakan pengetahuan baru di dunia sehingga hasil dari pembelajaran mendalam berupaya pada peningkatan pemahaman siswa tentang kelebihan dan kelemahannya, pengumpulan data mengenai informasi profil siswa dan pembangunan nilai kepercayaan diantara anggota kelompok belajar siswa.

Widespread use of the Web and other Internet technologies in postsecondary education has exploded in the last 15 years. Using a set of items developed by the National Survey of Student Engagement (NSSE), the researchers utilized the hierarchical linear model (HLM) and multiple regressions to investigate the impact of Web-based learning technology on student engagement and self-reported learning outcomes in face-to-face and online learning environments. The results show a general positive relationship between the use the learning technology and student engagement and learning outcomes. We also discuss the possible impact on minority and part-time students as they are more likely to enroll in online courses.

Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of affairs must follow. In this review, we seek to present a refresher of the many different stacked, connectionist networks that make up the deep learning architectures followed by automatic architecture optimization protocols using multiagent approaches. Further, since guaranteeing system uptime is
fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale
with information.

This report provides an overview of supervised deep learning, followed by an in-depth explanation of the workings of a deep convolutional neural network, which despite its performance, gathers criticism for its tendency to be used as a black box. The rest of the report goes over the research that was carried out. The first is a simple mathematical analysis which proposes an explanation for the recent success of the Rectified Linear Unit as the chief activation function of CNNs. The next sections are applied: understanding why learning fails with class imbalance and noisy labels, and exploring ways of tackling it -- including a novel cost function with a probabilistic interpretation which delivered up to 25 additional percentage points in classification performance.

Everyone thought it was great to use differentiable, symmetric, non-linear activation functions in feed-forward neural networks, until Alex Krizhevsky [8] found that Rectifier Linear Units, despite being not entirely differentiable, nor symmetric, and most of all, piece-wise linear, were computationally cheaper and worth the trade-off with their more sophisticated counterparts. Here are just a few thoughts on the properties of these activation functions, a potential explanation for why using ReLUs speeds up training, and possible ways of applying these insights for better learning strategies.

In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research.We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community.
Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.

The term Artificial Intelligence is often cited in popular press as well as in art and philosophy circles as an alchemic talisman whose functioning is rarely explained. The hegemonic paradigm to date (also crucial to the automation of labour) is not based on GOFAI (Good Old-Fashioned Artificial Intelligence that never succeeded at automating symbolic deduction) but on the neural networks designed by Frank Rosenblatt back in 1958 to automate statistical induction. The text highlights the role of logic gates in the distributed architecture of neural networks, in which a generalized control loop affects each node of computation to perform pattern recognition. In this distributed and adaptive architecture of logic gates, rather than applying logic to information top-down, information turns into logic, that is a representation of the world becomes a new function in the same world description. This basic formulation is suggested as a more accurate definition of learning to challenge the idealistic definition of (artificial) intelligence. If pattern recognition via statistical induction is the most accurate descriptor of what is popularly termed Artificial Intelligence, the distorting effects of statistical induction on collective perception, intelligence and governance (over-fitting, apophenia, algorithmic bias, ‘deep dreaming’, etc.) are yet to be fully understood.

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.

There are certain thoughts - intimations - that will seem corny, or laughable, and therefore dismissed - and only seriously absorbed into the changing mind when felt, in the shelter of the quietly moving music of the soul.
It is the function of art, and of love, to make this music heard . . . 

  It is not the music of depression - it is the way through.

This research, conducted by the author when a Senior Researcher at Newcastle University, UK, was funded by a UK governance organisation to help them assess, integrate and improve their understanding of the governed population.

ABSTRACT: Governance organisations have been urged to develop more holistic evidence-based approaches with partners to better deal with issues affecting the governed population. This presupposes an understanding of the population, yet there has been little research work addressing how governance organisations come to understand their governed population, nor how this understanding can be improved upon within such organisations.

This case study examined in-depth how understanding of a governed population was developed within one UK local governance organisation and it explored how such understanding might be developed and improved through more holistic and evidence-based organisational learning within the organisation and its governance partnerships.

The research methodology involved placement within the governance organisation, participant observations on several projects and innovative initiatives, observations within, across, and outside the organisation, and also interviews with varied key stakeholders.

The study examined:

- stakeholder engagement and networks
- current and varied understandings of the population
- mechanisms actually used for developing understandings
- the influence of context on organisational learning

and analysed these to arrive at a recommendations to improve organisational understanding of the governed population.

In the case studied, significant strengths were found: including innovative past learning trials for development, extensive ongoing networking activities, and many varied and accumulating quantitative datasets available across partnerships.

However, entrenched organisational approaches and practices constrained the development of holistic evidence-based understanding, for instance: the absence of systematic maintained collective learning processes; weak recording and forgetting of understandings; methodological exclusion of stakeholders from the development of understanding; the relative neglect of qualitative data, explanations, investigations, interpretations, and reflexivity; and also implicit organisational acceptance of such limitations and low aspirations.

Nonetheless, untapped opportunities existed to enable change including: latent practitioner knowledge and innovative management champions, the desire for improvement and raised aspirations within learning networks, and the development potential of alterative learning approaches.

It was concluded that more holistic evidence-based understanding was possible, but required new learning approaches beyond those then in use in both local government or academia.   

Recommendations are made to utilise and develop improved organisational learning approaches trialed and reported in the case study.

It is argued that the case study findings, conclusions, and recommendations may be applicable to other similar local governance organisations and partnerships that seek to better understand local populations, services and interventions.

The work was also submitted for, and awarded, an MPhil (at Newcastle University, supervised by Prof. Patsy Healy)

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.

—The performance of most conventional classification systems relies on appropriate data representation and much of the efforts are dedicated to feature engineering, a difficult and time-consuming process that uses prior expert domain knowledge of the data to create useful features. On the other hand, deep learning can extract and organize the discriminative information from the data, not requiring the design of feature extractors by a domain expert. Convolutional Neural Networks (CNNs) are a particular type of deep, feedforward network that have gained attention from research community and industry, achieving empirical successes in tasks such as speech recognition, signal processing, object recognition, natural language processing and transfer learning. In this paper, we conduct some preliminary experiments using the deep learning approach to classify breast cancer histopathological images from BreaKHis, a publicly dataset available at http://web.inf.ufpr.br/vri/breast-cancer-database. We propose a method based on the extraction of image patches for training the CNN and the combination of these patches for final classification. This method aims to allow using the high-resolution histopathological images from BreaKHis as input to existing CNN, avoiding adaptations of the model that can lead to a more complex and computationally costly architecture. The CNN performance is better when compared to previously reported results obtained by other machine learning models trained with hand-crafted textural descriptors. Finally, we also investigate the combination of different CNNs using simple fusion rules, achieving some improvement in recognition rates.

In this introduction to the Focus Section “Learning as a Geographical Process,” I provide a context for the four articles that follow, by means of (1) making explicit the threefold rationale for this initiative; (2) relating this initiative with previous geographical scholarship on the problematic of learning; (3) highlighting the significance of the self-referential character of our work; (4) providing a brief outline of the articles that follow; and (5) pointing out the important fact that both “learning” and “geographical process” constitute semantically rich categories and that relating the two involves a many-to-many type of logical mapping.

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines.
Despite the great efforts of the past decades, however, a natural and
robust human-machine speech interaction still appears to be out of reach,
especially when users interact with a distant microphone in noisy and re-
verberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one
of the major open challenges in the field.
This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called “network of deep neural networks”.
The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.

Recent findings [HOT06] have made possible the learning of deep layered hier- archical representations of data mimicking the brains working. It is hoped that this paradigm will unlock some of the power of the brain and lead to advances towards true AI.
In this thesis I implement and evaluate state-of-the-art deep learning models and using these as building blocks I investigate the hypothesis that predicting the time-to-time sensory input is a good learning objective. I introduce the Predictive Encoder (PE) and show that a simple non-regularized learning rule, minimizing prediction error on natural video patches leads to receptive fields similar to those found in Macaque monkey visual area V1. I scale this model to video of natural scenes by introducing the Convolutional Predictive Encoder (CPE) and show similar results. Both models can be used in deep architectures as a deep learning module.

The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.

Fire disasters are man-made disasters, which cause ecological, social, and economic damage. To minimize these losses, early detection of fire and an autonomous response are important and helpful to disaster management systems. Therefore, in this article, we propose an early fire detection framework using fine-tuned convolutional neural networks for CCTV surveillance cameras, which can detect fire in varying indoor and outdoor environments. To ensure the autonomous response, we propose an adaptive prioritization mechanism for cameras in the surveillance system. Finally, we propose a dynamic channel selection algorithm for cameras based on cognitive radio networks, ensuring reliable data dissemination. Experimental results verify the higher accuracy of our fire detection scheme compared to state-of-the-art methods and validate the applicability of our framework for effective fire disaster management.

The objective of this study is to propose an alternative, hybrid solution method for diagnosing diabetic retinopathy from retinal fundus images. In detail, the hybrid method is based on using both image processing and deep learning for improved results. In medical image processing, reliable diabetic retinopathy detection from digital fundus images is known as an open problem and needs alternative solutions to be developed. In this context, manual interpretation of retinal fundus images requires the magnitude of work, expertise, and over-processing time. So, doctors need support from imaging and computer vision systems and the next step is widely associated with use of intelligent diagnosis systems. The solution method proposed in this study includes employment of image processing with histogram equalization, and the contrast limited adaptive histogram equalization techniques. Next, the diagnosis is performed by the classification of a convolu-tional neural network. The method was validated using 400 retinal fundus images within the MESSIDOR database, and average values for different performance evaluation parameters were obtained as accuracy 97%, sensitivity (recall) 94%, specificity 98%, precision 94%, FScore 94%, and GMean 95%. In addition to those results, a general comparison of with some previously carried out studies has also shown that the introduced method is efficient and successful enough at diagnosing diabetic retinopathy from retinal fundus images. By employing the related image processing techniques and deep learning for diagnosing diabetic retinopathy, the proposed method and the research results are valuable contributions to the associated literature.

Pembelajaran bermakna bukanlah satu pendekatan pembelajaran yang baru. Namun beberapa elemen baru telah dimasukkan ke dalam pendekatan ini bagi menjenamakannya semula sebagai pedagogi baru. Kapasiti pedagogi pembelajaran bermakna atau dalam istilah inggerisnya, New Pedagogies for Deep Learning (NPDL) yang diimplementasi pada masa kini di IPG mempunyai pengaruh elemen reka model bentuk Fullan, Quinn dan McEachen. Elemen ini mengandungi empat bahagian yang saling menyokong dan melengkapi antara satu sama lain bagi menghasilkan sebuah konsep NPDL yang mantap dalam Pengajaran dan Pembelajaran (PdP) di sesebuah institusi pendidikan. Empat bahagian tersebut ialah amalan pedagogi, pemanfaatan digital, rakan pembelajaran dan persekitaran pembelajaran.

Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.

In this study the effects of two different interactive learning tasks, in which simple games were included were described with respect to student motivation and deep strategy use. The research involved 235 students from four elementary schools in The Netherlands. One group of students (N ¼ 128) constructed their own memory 'drag and drop' game, whereas the other group (N ¼ 107) played an existing 'drag and drop' memory game. Analyses of covariance demonstrated a significant difference between the two conditions both on intrinsic motivation and deep strategy use. The large effect sizes for both motivation and deep strategy use were in favour of the construction condition. The results suggest that constructing a game might be a better way to enhance student motivation and deep learning than playing an existing game. Despite the promising results, the low level of complexity of the games used is a study limitation.

Data  mining  and  machine  learning  have  become  a  vita
l  part  of  crime  detection  and  prevention.  In  this
research, we use WEKA, an open source data mining s
oftware, to conduct a comparative study between the
violent crime patterns from the Communities and Cri
me Unnormalized Dataset provided by the University
of  California-Irvine  repository  and  actual  crime  st
atistical  data  for  the  state  of  Mississippi  that  ha
s  been
provided  by  neighborhoodscout.com.  We  implemented  t
he  Linear  Regression,  Additive  Regression,  and
Decision  Stump  algorithms  using  the  same  finite  set
  of  features,  on  the  Communities  and  Crime  Dataset.
Overall, the linear regression algorithm performed
the best among the three selected algorithms. The s
cope
of this project is to prove how effective and accur
ate the machine learning algorithms used in data mi
ning
analysis can be at predicting violent crime pattern
s.

Alzheimer’s Disease is a severe neurological brain disorder. It destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities. Alzheimer’s Disease is not curable, but earlier detection can help improve symptoms in a great deal. Machine learning techniques can vastly improve the process for accurate diagnosis of Alzheimer’s Disease. In recent days deep learning techniques have achieved major success in medical image analysis. But relatively little investigation has been done to applying deep learning techniques for Alzheimer’s Disease detection and classification. This paper presents a novel deep learning model for multi-Class Alzheimer’s Disease detection and classification using Brain MRI Data. We design a very deep convolutional network and demonstrate the performance on the Open Access Series of Imaging Studies (OASIS) database.

Causality  is  the  most  important  topic  in  the  history  of  Western  Science,  and  since  the beginning of the statistical paradigm, it meaning has been reconceptualized many times. Causality entered  into  the  realm  of  multi-causal  and  statistical  scenarios some  centuries  ago.
Despite  of widespread  critics,  today Deep  Learning  and  Machine  Learning  advances  are  not weakening causality  but  are  creating  a  new  way  of  finding  indirect  factors  correlations.  This  process makes
possible us to talk about approximate causality, as well as about a situated causality.

Deep learning is quickly becoming one of the most important tools for image classification. This technology is now beginning to be applied to the tasks of plant disease classification and recognition. The positive results that are being obtained using this approach hide some issues that are seldom taken into account in the respective experiments. This article presents an investigation into the main factors that affect the design and effectiveness of deep neural nets applied to plant pathology. An in-depth analysis of the subject, in which advantages and shortcomings are highlighted, should lead to more realistic conclusions on the subject. The arguments used throughout the text are built upon both studies found in the literature and experiments carried out using an image database carefully built to reflect and reproduce many of the conditions expected to be found in practice. This database, which contains almost 50,000 images, is being made freely available for academic purposes.

The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering , as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.

In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques. We first discuss models such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) that can effectively exploit variablelength contextual information, and their various combination with other models. We then describe models that are optimized end-to-end and emphasize on feature representations learned jointly with the rest of the system, the connectionist temporal classification (CTC) criterion, and the attention-based sequenceto-sequence translation model. We further illustrate robustness issues in speech recognition systems, and discuss acoustic model adaptation, speech enhancement and separation, and robust training strategies. We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.

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.