Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating mobile applications that support Mathematics learning in the Further Education and Training Phase

ion and generalization: The results attained from the data analysis were linked with the theoretical and conceptual frameworks. The theoretical framework acted as an undeveloped model for this study which the conceptual framework is based on. The conceptual framework aimed to describe how the concepts (dimensions) in the theoretical framework could be applied to educational environments. Dialogical reasoning: Interpretivism which acts as the philosophy of this study assumes that truth is socially constructed. The researcher engaged with the phenomenon to establish meaning using pre-understanding of the researcher and new knowledge attained from the participants. The data analysis provided the researcher with information which was used to draw correlations between preunderstandings and new knowledge. The researcher disregarded information from the data analysis which delivered differences between pre-understandings and new knowledge.
Applications of partition based clustering algorithms: A survey

Data mining is one of the interesting research areas in database technology. In data mining, a cluster is a set of data objects that are similar to one another with in a cluster and are different to the entities in the former clusters. Clustering is the efficient method in data mining in order to process huge data sets. The core methodology of clustering is used in many domains like academic result analysis of institutions. Also, the methods are very well suited in machine learning, clustering in medical dataset, pattern recognition, image mining, information retrieval and bioinformatics. The clustering algorithms are categorized based upon different research phenomenon. Varieties of algorithms have recently occurred and were effectively applied to real-life data mining problems. This survey mainly focuses on partition based clustering algorithms namely k-Means, k-Medoids and Fuzzy c-Means In particular, they applied mostly in medical data sets. The importance of the survey is to explore the various applications in different domains.
Product Classification - A Hierarchical Approach

The social and environmental impact associated with consuming a product is something that is becoming increasingly important to consumers and businesses alike. This impact can in theory be computed simply by classifying a product and by mapping the classified product to the corresponding life-cycle assessments research. However, this type of mapping requires an extensive product taxonomy with a large plurality of categories, which makes classification through machine learning a non-trivial task. This thesis describes the implementation of a hierarchical product classifier using the Python library scikit-learn, that makes it possible to automatically classify products based primarily on their brands and titles into a large taxonomy. Using a data set of 3.1 million products spread over 800 categories, we trained a set of hierarchical classifiers using different learning algorithms. Evaluations of these algorithms showed that the best hierarchical classifier reached a hierarchical F1-score (hF1) of 0.85. (Less)
Learning Objects for Instruction: Design and Evaluation

Learning Objects for Instruction shows how practical models of learning objects solutions are being applied in education, organizations, industry, and the military. It includes diverse strategies used across these groups to apply learning objects -- from the use of firmly-grounded theoretical contexts to practical tool-based solutions. The reader will find a thorough history, solid models and real-world practices for using learning objects for instruction in a variety of settings. Greater numbers of organizations are expected to embrace the use of objects for instruction as issues of standardization continue to be worked out.
Visual-Inertial Sensor Fusion for Tracking in Ambulatory Environments

Tracking a high-velocity object through a cluttered environment is daunting for even the human-observer. Vision-based trackers will frequently lose their lock on the object as features on the object become distorted or faded as a result of motion-blurring imparted by the high-velocity of the object. Moreover, the frequent occlusions as the object passes through clutter only serves to compound the issue. To boot, the usual difficulties associated with most vision-based trackers still apply such as: nonuniform illumination, object rotation, object scale changes, etc. Inertial-based trackers provide useful complementary data to aid the vision-based systems. The higher sampling rates of the inertial measurements gives invaluable information to be able to track high-speed objects. With the IMU attached to the object, the inertial measurements are immune to occlusions unlike their visual counterparts. Efficient combination of visual as well as inertial sensors into a unified framework is coined visual-inertial sensor fusion. Visual-inertial sensor fusion is a powerful tool for many industries: it allows the medical practitioners to better understand and diagnose illnesses; it allows the engineer to design more flexible and immersive virtual reality environments; and it allows the film-director to fully capture motion in a scene. The complementary nature of visual and inertial sensors is well-toted throughout these industries, the faster sampling rate of the inertial sensors fits lock-and-key with the higher accuracy of the visual sensor to unlock the potential for algorithms capable of tracking high-velocity objects through cluttered environments. Inevitably, sensor fusion is accompanied by higher algorithmic complexity and requires careful understanding of the components involved. For this reason, the approach taken in this thesis is a ground-up approach towards a complete visual-inertial system: from camera calibration all the way to handling of asynchronous sensor measurements for sensor-fusion.
On the exploration of high cardinality categorical data

Statistical data often contain high cardinality categorical variables, which are often hierarchically structured. An example of such a variable is the classification code of economic activity (NACE). In official statistics practice, there is a growing need for tools to explore and analyse such high cardinality categorical hierarchical data, since tabulation of such datasets is rather time-consuming and tedious. In this paper we propose the tableplot as a candidate tool to explore high cardinality data. The tableplot is an innovative visualisation method for exploring large statistical datasets that helps visually to explore relationships between variables, outliers and odd data patterns. We illustrate the suitability of the tableplot on the Dutch insurance policy record administration, which is a large data source with high cardinality hierarchical variables.
Fast Acquisition Based on FFT for High Dynamic GPS Signals

The fast acquisition for high dynamic GPS signals was studied, and an acquiring strategy was proposed which decreases acquiring time and works well in high dynamic environment. This method introduces a 2-D acquiring strategy, which adopts sequential acquisition in Doppler frequency domain and parallel acquisition using FFT instead of sliding correlation in time domain. Thus the acquiring performance of GPS receiver is improved. Moreover, both the principle and software implementation of the proposed method are analyzed. The accuracy of this acquisition is tested and verified using the raw GPS signal acquired.
Using the erroneous data clustering to improve the feature extraction weights of original image algorithms

Many data mining adopts the form of Artificial Neural Network (ANN) to solve many problems, many problems will be involved in the process of training Artificial Neural Network, such as the number of samples with volume label, the time and performance of training, the number of hidden layers and Transfer function, if the compared data results are not expected, it cannot be known clearly that which dimension causes the deviation, the main reason is that Artificial Neural Network trains compared results through the form of modifying weight, and it is not a kind of training to improve the original algorithm for the extraction algorithm of image, but tend to obtain correct value aimed at the result plus the weigh; in terms of these problems, this paper will mainly put forward a method to assist in the image data analysis of Artificial Neural Network; normally, a parameter will be set as the value to extract feature vector during processing the image, which will be considered by us as weight, the experiment will use the value extracted from feature point of Speeded Up Robust Features (SURF) Image as the basis for training, SURF itself can extract different feature points according to extracted values, we will make initial semi-supervised clustering according to these values, and use Modified K - on his Neighbors (MFKNN) as training and classification, the matching mode of unknown images is not one-to-one complete comparison, but only compare group Centroid, its main purpose is to save its efficiency and speed up, and its retrieved data results will be observed and analyzed eventually; the method is mainly to make clustering and classification with the use of the nature of image feature point to give values to groups with high error rate to produce new feature points and put them into Input Layer of Artificial Neural Network for training, and finally comparative analysis is made with Back-Propagation Neural Network (BPN) of Genetic Algorithm-Artificial Neural Network (GAANN through the weight training results.
Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an approach of domain transfer, where features learned from a large annotated dataset are transferred to a target domain where less annotated examples are available as is typical for the security and defense domain. Many of these networks trained on natural images appear to learn features similar to Gabor filters and color blobs in the first layer. These first-layer features appear to be generic for many datasets and tasks while the last layer is specific. In this paper, we study the effect of copying all layers and fine-tuning a variable number. We performed an experiment with a Caffe-based network on 1000 ImageNet classes that are randomly divided in two equal subgroups for the transfer from one to the other. We copy all layers and vary the number of layers that is fine-tuned and the size of the target dataset. We performed additional experiments with the Keras platform on CIFAR-10 dataset to validate general applicability. We show with both platforms and both datasets that the accuracy on the target dataset improves when more target data is used. When the target dataset is large, it is beneficial to freeze only a few layers. For a large target dataset, the network without transfer learning performs better than the transfer network, especially if many layers are frozen. When the target dataset is small, it is beneficial to transfer (and freeze) many layers. For a small target dataset, the transfer network boosts generalization and it performs much better than the network without transfer learning. Learning time can be reduced by freezing many layers in a network.
Using Web N-Grams to Help Second-Language Speakers

We demonstrate the NETSPEAK search engine which helps people with writing in a foreign language. To this end the search engine implements wildcard query processing on top of an inverted index of Web n-grams. By indexing word n-grams extracted from the whole Web, our search engine allows writers to retrieve phrases matching almost any given context, while the occurrence frequencies of the retrieved n-grams indicate their commonness in everyday writing. To allow for retrieval in less than a second for most queries, we have devised an inverted index and a query processor tailored to n-gram retrieval.
Research on Application of Iris Recognition Based on Improved Algorithm

The appropriate threshold is obtained through the histogram of the iris image,which is used in iris binary processing.The iris inner boundary is orientated roughly based on geometric gray-level projection method and the iris external boundary is orientated by the improved Daugman localization operator,both of which are fitted accurately by the method of the least squares round fitting.The experimental results show that the localization algorithm has the advantages of high positioning accuracy and high positioning speed,and can satisfy the requirement of real-time.Meanwhile,put forward an iris normalization method which is based on line extraction.In the feature extraction,iris feature information is extracted by the method of 2D Log-Gabo filter which can generate multi-channel filter though multi-scale and multiple directions,the method also can reduce the length of the iris code and the time of feature matching,improve the feature matching accuracy,can effectively improve the accuracy of iris recognition.
Research on second-generation ID card automatic segmentation method

In order to solve the difficulties of binarization caused by uneven illumination of the acquired images, the digital image uneven illumination correction algorithm based on the probability is applied to illumination correction for the ID card. This algorithm uses a statistical method when extracting the background. It first calculates the mean and standard deviation of the pixels' brightness, then calculates the grayscale of the background, according to the statistical value. This is conducive to exclude the interference of noise points, and enhance robustness. The horizontal projection is used to determine the line position of the character, and the vertical projection is used to determine the position of the single character.Experimental results show that the method can effectively binarize and accurately segment characters on the ID card.
Example-based Search: a New Frontier for Exploratory Search

Exploration is one of the primordial ways to accrue knowledge about the world and its nature. As we accumulate, mostly automatically, data at unprecedented volumes and speed, our datasets have become complex and hard to understand. In this context, exploratory search provides a handy tool for progressively gather the necessary knowledge by starting from a tentative query that can provide cues about the next queries to issue. An exploratory query should be simple enough to avoid complicate declarative languages (such as SQL) and convoluted mechanism, and at the same time retain the flexibility and expressiveness required to express complex information needs. Recently, we have witnessed a rediscovery of the so called example-based methods, in which the user, or the analyst circumvent query languages by using examples as input. This shift in semantics has led to a number of methods receiving as query a set of example members of the answer set. The search system then infers the entire answer set based on the given examples and any additional information provided by the underlying database. In this tutorial, we present an excursus over the main example-based methods for exploratory analysis. We show how different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data. We conclude by providing a unifying view of this query-paradigm and identify new exciting research directions.
Information Cascades Modeling via Deep Multi-Task Learning

Effectively modeling and predicting the information cascades is at the core of understanding the information diffusion, which is essential for many related downstream applications, such as fake news detection and viral marketing identification. Conventional methods for cascade prediction heavily depend on the hypothesis of diffusion models and hand-crafted features. Owing to the significant recent successes of deep learning in multiple domains, attempts have been made to predict cascades by developing neural networks based approaches. However, the existing models are not capable of capturing both the underlying structure of a cascade graph and the node sequence in the diffusion process which, in turn, results in unsatisfactory prediction performance. In this paper, we propose a deep multi-task learning framework with a novel design of shared-representation layer to aid in explicitly understanding and predicting the cascades. As it turns out, the learned latent representation from the shared-representation layer can encode the structure and the node sequence of the cascade very well. Our experiments conducted on real-world datasets demonstrate that our method can significantly improve the prediction accuracy and reduce the computational cost compared to state-of-the-art baselines.
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis

Document representation is the core of many NLP tasks on machine understanding. A general representation learned in an unsupervised manner reserves generality and can be used for various applications. In practice, sentiment analysis (SA) has been a challenging task that is regarded to be deeply semantic-related and is often used to assess general representations. Existing methods on unsupervised document representation learning can be separated into two families: sequential ones, which explicitly take the ordering of words into consideration, and non-sequential ones, which do not explicitly do so. However, both of them suffer from their own weaknesses. In this paper, we propose a model that overcomes difficulties encountered by both families of methods. Experiments show that our model outperforms state-of-the-art methods on popular SA datasets and a fine-grained aspect-based SA by a large margin.
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure

The classification of electroencephalography (EEG) signals is useful in a wide range of applications such as seizure detection/prediction, motor imagery classification, emotion classification and drug effects diagnosis, amongst others. With the large number of EEG channels acquired, it has become vital that efficient data-reduction methods are developed, with varying importance from one application to another. It is also important that online classification is achieved during EEG recording for many applications, to monitor changes as they happen. In this paper we introduce a method based on Mutual Information (MI), for channel selection. Obtained results show that whilst there is a penalty on classification accuracy scores, promising speed-up gains can be achieved using MI techniques. Using MI with signal epochs (3secs) containing signal transitions enhances these speed-up gains. This work is exploratory and we suggest further research to be carried out for validation and development. Benefits to improving classification speed include improving application in clinical or educational settings.
Rhizoma coptidis as a Potential Treatment Agent for Type 2 Diabetes Mellitus and the Underlying Mechanisms: A Review

Diabetes mellitus, especially type 2 diabetes mellitus (T2DM), has become a significant public health burden. Rhizoma coptidis (RC), known as Huang Lian, is widely used for treating diabetes in China. The bioactive compounds of RC, especially alkaloids, have the potential to suppress T2DM-induced lesions, including diabetic vascular dysfunction, diabetic heart disease, diabetic hyperlipidemia, diabetic nephropathy, diabetic encephalopathy, diabetic osteopathy, diabetic enteropathy, and diabetic retinopathy. This review summarizes the effects of RC and its bioactive compounds on T2DM and T2DM complications. Less research has been conducted on non-alkaloid fractions of RC, which may exert synergistic action with alkaloids. Moreover, we summarized the pharmacokinetic properties and structure-activity relationships of RC on T2DM with reference to extant literature and showed clearly that RC has potential therapeutic effect on T2DM.
KANDINSKY: Abstract Art-Inspired Visualization of Social Discussions

Many social media sites allow users to upload text, images, and videos (collectively referred to asanchor post) for public consumption. These posts may attract hundreds of comments from many social users leading to social conversations (ie discussions). Tools that can facilitate user-friendly and effective understanding and analysis of large volumes of comments associated with anchor posts can be of great benefit to individuals and organizations. In this demonstration, we present a novel end-to-end visualization system called Kandinsky to supportmulti-faceted visualization of social discussions associated with an anchor post. In Kandinsky, the social discussion landscape is visualized using a collection of colorfulcircles andconcentric circles, which are inspired from the famous abstract arts called"Squares with Concentric Circles" and"Several Circles" by Russian painter Wassily Kandinsky (1866-1944). Intuitively, a circle and a concentric circle represent a social comment and a collection of comments in a discussion thread, respectively. We discuss various innovative features of Kandinsky and demonstrate its effectiveness.
Developing Evaluation Metrics for Instant Search Using Mixed Methods Methods

Instant search has become a popular search paradigm in which users are shown a new result page in response to every keystroke triggered. Over recent years, the paradigm has been widely adopted in several domains including personal email search, e-commerce, and music search. However, the topic of evaluation and metrics of such systems has been less explored in the literature thus far. In this work, we describe a mixed methods approach to understanding user expectations and evaluating an instant search system in the context of music search. Our methodology involves conducting a set of user interviews to gain a qualitative understanding of users' behaviors and their expectations. The hypotheses from user research are then extended and verified by a large-scale quantitative analysis of interaction logs. Using music search as a lens, we show that researchers and practitioners can interpret the behavior logs more effectively when accompanied by insights from qualitative research. Further, we also show that user research eliminates the guesswork involved in identifying users signals that estimate user satisfaction. Finally, we demonstrate that metrics identified using our approach are more sensitive than the commonly used click-through rate metric for instant search.
Deep Distribution Network: Addressing the Data Sparsity Issue for Top-N Recommendation

Existing recommendation methods mostly learn fixed vectors for users and items in a low-dimensional continuous space, and then calculate the popular dot-product to derive user-item distances. However, these methods suffer from two drawbacks: (1) the data sparsity issue prevents from learning high-quality representations; and (2) the dot-product violates the crucial triangular inequality and therefore, results in a sub-optimal performance. In this work, in order to overcome the two aforementioned drawbacks, we propose Deep Distribution Network (DDN) to model users and items via Gaussian distributions. We argue that, compared to fixed vectors, distribution-based representations are more powerful to characterize users' uncertain interests and items' distinct properties. In addition, we propose a Wasserstein-based loss, in which the critical triangular inequality can be satisfied. In experiments, we evaluate DDN and comparative models on standard datasets. It is shown that DDN significantly outperforms state-of-the-art models, demonstrating the advantages of the proposed distribution-based representations and wassertein loss.