The task of reading a sentence and representing it using an ontology model is called semantic parsing (Popescu et al., 2003) a process which uses semantics in order to translate text to a knowledge representation structure. This process is nor- mally algorithmic, based upon heuristics, statistics or other rules (Tang and Mooney, 2001; Shi and Mihalcea, 2005; Wong and Mooney, 2006), or in more recent re- search, relies on Deep learning (Andor et al., 2016; Weiss et al., 2015; Zhang and McDonald, 2012) . Such a process might simulate the mechanism through which we acquire knowledge and information (Vlachos and Clark, 2014; Vlachos, 2012). A cognitive agent simulates the functions which enable humans to perform semantic parsing, rather than implementing semantic parsing as a machine-oriented mecha- nism. Therefore it is sound to presume that a cognitive agent capable of performing such a function with relative ease, would be one step closer to becoming able to autonomously learn indefinitely.
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One of the ultimate goals of artificial intelligence is the creation of computer agents that can undertake specific tasks, such as prediction and control, which would otherwise require human intelligence [65, 91, 89]. Earlier attempts in this field, such as symbolic approaches , involved the representation of human knowledge as theorems and facts and apply- ing logical inference rules. The most successful instances of this class of approaches are probably the expert systems studied during the 70s and 80s . Expert systems turned out difficult to maintain and incapable of autonomous learning from new data [29, 91]. Statistical learning  and soft computing , on the contrary, have been contributing more and more to the realization of a self learning agent. Instead of consulting human experts and hard-coding their knowledge into a computer system, one solely presents the agent data samples that define the task it is expected to undertake. For example, by processing enough images of handwritten digits and the corresponding labels, an agent learns the mapping pattern between the digit and the label, and can then predict labels for new handwritten digits. That is to say, instead of being programmed to do so, the agent learns to induce abstract –though sometimes not necessarily interpretable– rules from the data . These rules are expressed as hierarchies of mathematical operations . The designing and training of such an agent are major tasks in machine learning. It is often initialized with close to no prior knowledge and has to learn from its own errors.
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1. Local Representation: Local Representation identifies the local regions having salient motion information. The key benefit of the local features is that there is no need for knowledge about human body model or people localization. Local features  are extracted by applying a local feature detector or descriptors of local features, and then encoding spatiotemporal neighborhoods around the features identified using a local feature descriptor. Local feature capture shape as well as details on motion in a local neighborhood surrounding points of interest and trajectories. One of such most popular descriptors are HoG features. In , Seemanthini et al., the gradient orientation are computed in which they occur in a coined region of an image. At many image regions, patches of multiple scaled image are analyzed by the gradient histogram algorithm. They are designed to offer robustness for local appearance and position changes. Edge gradients and orientations are calculated at each pixel within the given local area in order to measure the HoG features.
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Zero-Shot Learning Zero-shot learning has been studied in the area of natural language pro- cess. Hamaguchi et al. (2017) use a neighbor- hood knowledge graph as a bridge between out of knowledge base entities to train the knowledge graph. Levy et al. (2017) connect nature language question with relation query to tackle zero shot re- lation extraction problem. Elsahar et al. (2018) ex- tend the copy actions (Luong et al., 2015) to solve the rare words problem in text generation. Some attempts have been made to build machine transla- tion systems for language pairs without direct par- allel data, where they relying on one or more other languages as the pivot (Firat et al., 2016; Ha et al., 2016; Chen et al., 2017). In this paper, we use knowledge graph embedding as a bridge between seen and unseen relations, which shares the same spirit with previous work. However, less study has been done in relation detection.
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Multi-Word Expressions Multi-word expressions range from fixed expressions (in short, by and large), semi-fixed expressions (spill the beans, kick the bucket), and syntacti- cally flexible expressions (break up, make a mistake) (Sag et al., 2001). Our framework can handle these three cat- egories. Fixed expressions are considered as words-with- spaces in the lexicalization entry and thus can anchor syn- tactic trees as whole units. For instance machine `a caf´e (coffee machine) is a single lexicalization unit. Semi-fixed and flexible expressions are dealt with a primary anchor and co-anchors in the syntactic trees, separated by an under- score in the lexicalization entry. For instance the concept of H OUSE M OVING is related to a multi-word expression d´em´enagement faire in which d´em´enagement is the pri- mary anchor and faire is the verbal co-anchor. By conven- tion, the first word is the primary anchor, the other anchors are ordered as how they appear in the canonical tree of the syntactic family associated to the lexical entry. Hence the entry d´em´enagement faire can be realized as faire un d´em´enagement, literally to do a house moving. Note that thanks to the synonymy, we can also realize the concept of H OUSE M OVING with the single verb d´em´enager (to move out).
We are trying to answer this question by noting that a human Student has an Intelligent Teacher 3 and that Teacher-Student interactions are based not only on brute force methods of function estimation. In this paper, we show that Teacher-Student interactions can include special learning mechanisms that can significantly accelerate the learning process. In order for a learning machine to use fewer observations, it can use these mechanisms as well.
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curing bones related injuries and pain. In many cases, due to sudden jerks or accidents, the patient might suffer from severe pain. Therefore, it is the miracle medication for curing patients. Our aim here is to build a framework using Artificial Intelligence and Machine Learning for providing users with a digitalized system for physiotherapy. Even though varied computer-aided assessment of physiotherapy rehabilitation exist still recent approaches for computer aided monitoring and performance lack versatility and robustness. In our approach we have come up with proposition of an application which will record user’s physiotherapy exercises and also it will provide personalized advice based on user performance for refinement of therapy. By using Open Pose Library our system will detect angle between the joints and depending upon the range of motion it will guide patient in accomplishing physiotherapy at home. It will also suggest patients about different physio-exercises. With the help of Open Pose it is possible to render the patient’s images or real-time video.
lot of duplicate questions i.e. questions that convey the same meaning. Since it is open to all users, anyone can pose a question any number of times this increases the count of duplicate questions. This paper uses a dataset comprising of question pairs (taken from the Quora website) in different columns with an indication of whether the pair of questions are duplicates or not. Traditional comparison methods like Sequence matcher perform a letter by letter comparison without understanding the contextual information, hence they give lower accuracy. Machine learning methods predict the similarity using features extracted from the context. Both the traditional methods as well as the machine learning methods were compared in this study. The features for the machine learning methods are extracted using the Bag of Words models- Count-Vectorizer and TFIDF-Vectorizer. Among the traditional comparison methods, Sequence matcher gave the highest accuracy of 65.29%. Among the machine learning methods XGBoost gave the highest accuracy, 80.89% when Count-Vectorizer is used and 80.12% when TFIDF-Vectorizer is used.
Knowledge graphs (KGs) are built to store struc- tured facts which are encoded as triples, e.g., (Beijing, CapitalOf, China) (Lehmann et al., 2015). Each triple (h, r, t) consists of two entities h, t and a relation r, indicating there is a relation r between h and t. Large-scale KGs such as YAGO (Suchanek et al., 2007), Freebase (Bollacker et al., 2008) and WordNet (Miller, 1995) contain billions of triples and have been widely applied in vari- ous fields (Riedel et al., 2013; Dong et al., 2015). However, a common problem with these KGs is
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Abstract:- Accurate diagnosis and prediction is very important for appropriate disease treatment. Cancer is a leading cause of death worldwide, almost a million people around the globe die due to cancer every year. Cancer mortality can be reduced if it is diagnosed and treated at an early stage to save lives of cancer patients avoiding delays in care. This can be achieved with the help of machine learning. Machine learning techniques like Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), Random Forest (RMs) and Decision Trees (DTs) is broadly being used in cancer research to develop predictive models for effective and accurate prediction of cancer. We presented a review of recent ML approaches used in the modeling of cancer progression and prediction.
development of faster or more accurate motor responses. The most frequently used task in this paradigm is a typical choice response time task with a one-to- one m apping between an equal number of stimuli and responses. Participants are instructed to respond to a number of stimuli (ranging from three to six) usually presented in different spatial locations on a computer screen by pressing keys corresponding to the locations. The experiment starts with a lengthy practice session that involves the presentation of a structured stimulus sequence. Participants are simply told that the experiment is concerned with response times and that they are required to respond as fast as possible to the presented stimuli. Learning of the structure is established in two ways: (a) a greater response time speed-up for a group that practices with the structured sequence in comparison to a group that practices with a control pseudo-random sequence (b) a deterioration in performance when the group that practised with the structured sequence is transferred to a new (usually random) sequence.
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The above diagram depicts on the graphical representation of the various machine learning ensemble algorithms and their Root Mean Square Errors. the CVParameterSelection classifier has 2.5 Root Mean Square Error Value. It has high Root Mean Square Error value compare with others. The Random Committee has 0.29 Root Mean Square Error. It has very lowest Mean absolute error value compare with others.
Machine-learning algorithms are very often used to detect features in a variety of different applications (Datta et al., 2008). The full range of algorithms can be found in Datta et al. (2008), Pouyanfar et al. (2018) and Murthy and Koolagudi (2018), but what problems are the algorithms applied to in the context of multimedia IR? Key problems which are addressed in many applications are classification, object detection and annotation. Examples include images where super-human performance has been recorded in the 2015 large scale visual recognition challenge (ILSVRC15) using deep learning methods (Poyyanfar et al., 2018), which has come about due to much improved object recognition (improving the ability to detect objects improves classification techniques). This has also led to techniques that can automatically annotate and tag images, including online services such as Imagga (https://imagga.com/). In music, techniques to apply classification and temporal annotation have been developed at low level (e.g. timbre), mid level (e.g. pitch and rhythm) and high level (e.g. artist and genre) in many music applications (Srinivasa et al., 2018). In video (which is moving images together with sound), problems addressed include event detection by locating scene changes and segmentation of the object into stories e.g. scenes and threads in a TV programme or film (Lew et al., 2006). A quick review of the literature shows that machine learning has been applied to many problems in multimedia successfully, but there are many issues to which the technique cannot be addressed (see above). The key therefore to augmenting any application that uses knowledge organisation as its core with machine learning is to identify the features with which the technique can be used. The features that have been used successfully in the field are the ones that are known to bare fruit given the empirical evidence available. It is to these that we turn to next.
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Book Info: Presents the key algorithms and theory that form the core of machine learning. Discusses such theoretical issues as How does learning performance vary with the number of training examples presented? and Which learning algorithms are most appropriate for various types of learning tasks? DLC: Computer algorithms.
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We used the mind map conception as a starting point when having developed our Knowledge Galaxy. (Figure 2) The concepts appear on four levels in the Knowledge Galaxy, the highest are the topics; each topic contains keywords; the keywords are described by attributes; and attributes have their places to be found, we call them occurrences. “ O ur cognitive system s have limited capacity. Since there are too many sources of information competing for this limited capacity, the learner must select those that best match his or her goals. We know this selection process can be guided by instructional methods that direct the learner’s attention.”  We believe, based on M iller‟s  investigation, that this capacity of short-term memory is 7±2 terms for one node of the mind map. Putting 7±2 terms at each node results more than 200 attributes in the galaxy. Knowledge Galaxy is a decision-conducted knowledge visualiza- tion tool which links together the semantic map of available knowledge (Figure 2 left) and the cognitive map of needed knowledge (Figure 2 right).
based on SF mentioned in Equation 4 respectively. epc, P , D, and 1 is the number of epochs per chap- ter, model parameters, training set, and an indica- tor function which is one if first argument is >= the second argument or else zero; respectively. 5.2 Joint-Training for Knowledge Transfer While joint-training methods offer knowledge transfer by exploiting similarities and regularities across different tasks or datasets, the asymmet- ric nature of transfer and skewed proportion of datasets is usually not handled in a sound way. Here, we devise a training loss function L ˆ to re- lieve both of these involved issues while doing joint-training with a target dataset (TD) with fewer training samples and a source dataset (SD) having label information for higher number of examples; as mentioned in Equation 6.
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TensorFlow  is an open source software library released in 2015 by Google to make it easier for developers to design, build, and train deep learning models. TensorFlow originated as an internal library that Google developers used to build models in-house, and we expect additional functionality to be added to the open source version as they are tested and vetted in the internal flavour. The name TensorFlow derives from the operations that such neural networks perform on multidimensional data arrays. These arrays are referred to as "tensors". In June 2016, Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which only 5 were from Google
To further analyse the effects of intents and emo- tions on the event representation learning, we present case studies in Table 2, which directly shows the changes of similarity scores before and after incorporating intent and sentiment. For ex- ample, the original similarity score of two events “chef cooked pasta” and “chef cooked books” is very high (0.89) as they have high lexical overlap. However, their intents differ greatly. The intent of “chef cooked pasta” is “to hope his customer enjoying the delicious food”, while the intent of “chef cooked books” is “to falsify their financial statements”. Enhanced with the intents, the sim- ilarity score of the above two events dramatically drops to 0.45. For another example, as the event pair “man clears test” and “he passed exam” share the same sentiment polarity, their similarity score is boosted from -0.08 to 0.40.
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The reports conferred on top of illustrated that Deep Learning encompasses a heap of potential, however must overcome a number of challenges before changing into additional versatile tool. The interest and enthusiasm for the sector is, however, growing and already nowadays we have a tendency to see unimaginable real-world applications of this technology. Additional applications like the serving to voices of Siri and Cortana, Google Photo’s people tagging feature and sportify’s music recommendations.
More and more data are becoming part of people’s lives. With the popularization of tech- nologies like sensors, and the Internet of Things, data gathering is becoming possible and accessible for users. With these data in hand, users should be able to extract insights from them, and they want results as soon as possible. Average users have little or no experience in data analytics and machine learning and are not great observers who can collect enough data to build their own machine learning models. With large quantities of similar data being generated around the world and many machine learning models being used, it should be possible to use additional data and existing models to create accurate machine learning models for these users. This thesis proposes Agora, a Web-based marketplace where users can share their data and machine learning models with other users with small datasets and little experience. This thesis includes an overview of all the components that make up Agora, as well as details of two of its main components: Hephaestus and Sibyl.
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