niryuu/sni-each-converted huggingface dataset:
This report presents a plan to develop an intelligent Meta search engine,<lb>iral. This Meta search engine is aimed to provide comprehensive, efficient and<lb>relevant search results for given queries by combining results from different search<lb>engines. This will provide users to cover a larger database of World Wide Web for<lb>their queries and to get more relevant search results. On the other hand this Meta<lb>search engine will be creating a larger user base and attracting online marketing for<lb>Abster-iT. This will provide a new business set-up for Abster-iT.
Output: Intelligent Search Optimization using Artificial fuzzy logic
This paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6). We utilized recent advances in short text categorization using deep learning to create word-level and character-level models. The choice between word-level and characterlevel models in each particular case was informed through validation performance. Our final system is a combination of classifiers using word-level or character-level models. We also employed novel data augmentation techniques to expand and diversify our training dataset, thus making our system more robust. Our system achieved a macro-average precision, recall and F1-scores of 0.67, 0.61 and 0.635 respectively.
Output: DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
The Lumiere Project centers on harnessing probability and utility to provide assistance to computer software users. We review work on Bayesian user models that can be em­ ployed to infer a user's needs by consider­ ing a user's background, actions, and queries. Several problems were tackled in Lumiere research, including ( 1) the construction of Bayesian models for reasoning about the time-varying goals of computer users from their observed actions and queries, (2) gain­ ing access to a stream of events from soft­ ware applications, (3) developing a language for transforming system events into observa­ tional variables represented in Bayesian user models, ( 4) developing persistent profiles to capture changes in a user's expertise, and (5) the development of an overall architecture for an intelligent user interface. Lumiere proto­ types served as the basis for the Office Assis­ tant in the Microsoft Office '97 suite of pro­ ductivity applications.
Output: The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite programs. WithO(r3κ2n log n) random measurements of a positive semidefinite n×nmatrix of rank r and condition number κ, our method is guaranteed to converge linearly to the global optimum.
Output: A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
Deep neural networks trained on large supervised datasets have led to impressive results in recent years. However, since well-annotated datasets can be prohibitively expensive and time-consuming to collect, recent work has explored the use of larger but noisy datasets that can be more easily obtained. In this paper, we investigate the behavior of deep neural networks on training sets with massively noisy labels. We show that successful learning is possible even with an essentially arbitrary amount of noise. For example, on MNIST we find that accuracy of above 90 percent is still attainable even when the dataset has been diluted with 100 noisy examples for each clean example. Such behavior holds across multiple patterns of label noise, even when noisy labels are biased towards confusing classes. Further, we show how the required dataset size for successful training increases with higher label noise. Finally, we present simple actionable techniques for improving learning in the regime of high label noise.
Output: Deep Learning is Robust to Massive Label Noise
We study the problem of prediction with expert advice when the number of experts in question may be extremely large or even infinite. We devise an algorithm that obtains a tight regret bound of r OpǫT `N ` ? NT q, where N is the empirical ǫ-covering number of the sequence of loss functions generated by the environment. In addition, we present a hedging procedure that allows us to find the optimal ǫ in hindsight. Finally, we discuss a few interesting applications of our algorithm. We show how our algorithm is applicable in the approximately low rank experts model of Hazan et al., 2016, and discuss the case of experts with bounded variation, in which there is a surprisingly large gap between the regret bounds obtained in the statistical and online settings.
Output: Online Learning with Many Experts
Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to semi textindependent speaker verification tasks, i.e., the text of the speech is in a limited set of short phrases. We explore various settings of the DNN structure used for d-vector extraction, and present a phone-dependent training which employs the posterior features obtained from an ASR system. The experimental results show that it is possible to apply d-vectors on semi text-independent speaker recognition, and the phone-dependent training improves system performance.
Output: Deep Speaker Vectors for Semi Text-independent Speaker Verification
We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recognition performance. Given an abundance of DNN parameters but with only a limited amount of data, the effectiveness of the adapted DNN model can often be compromised. We formulate maximum a posteriori (MAP) adaptation of parameters of a specially designed CD-DNN-HMM with an augmented linear hidden networks connected to the output tied states, or senones, and compare it to feature space MAP linear regression previously proposed. Experimental evidences on the 20,000-word open vocabulary Wall Street Journal task demonstrate the feasibility of the proposed framework. In supervised adaptation, the proposed MAP adaptation approach provides more than 10% relative error reduction and consistently outperforms the conventional transformation based methods. Furthermore, we present an initial attempt to generate hierarchical priors to improve adaptation efficiency and effectiveness with limited adaptation data by exploiting similarities among senones.
Output: Maximum a Posteriori Adaptation of Network Parameters in Deep Models
Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models. This leads to new capabilities that standard LDA model does not have such as estimating infrequently occurring words or multi-language topic modeling. Experimental results demonstrate the effectiveness of our model compared with standard LDA.
Output: Modeling Word Relatedness in Latent Dirichlet Allocation
We introduce an exceptionally simple gated recurrent neural network (RNN) that achieves performance comparable to well-known gated architectures, such as LSTMs and GRUs, on the word-level language modeling task. We prove that our model has simple, predicable and non-chaotic dynamics. This stands in stark contrast to more standard gated architectures, whose underlying dynamical systems exhibit chaotic behavior.
Output: A RECURRENT NEURAL NETWORK WITHOUT CHAOS
The paper presents a new script classification method for the discrimination of the South Slavic medieval labels. It consists in the textural analysis of the script types. In the first step, each letter is coded by the equivalent script type, which is defined by its typographical features. Obtained coded text is subjected to the run-length statistical analysis and to the adjacent local binary pattern analysis in order to extract the features. The result shows a diversity between the extracted features of the scripts, which makes the feature classification more effective. It is the basis for the classification process of the script identification by using an extension of a state-of-the-art approach for document clustering. The proposed method is evaluated on an example of hand-engraved in stone and handprinted in paper labels in old Cyrillic, angular and round Glagolitic. Experiments demonstrate very positive results, which prove the effectiveness of the proposed method.
Output: An Approach to the Analysis of the South Slavic Medieval Labels Using Image Texture
To achieve acceptable performance for AI tasks, one can either use sophisticated feature extraction methods as the first layer in a twolayered supervised learning model, or learn the features directly using a deep (multilayered) model. While the first approach is very problem-specific, the second approach has computational overheads in learning multiple layers and fine-tuning of the model. In this paper, we propose an approach called wide learning based on arc-cosine kernels, that learns a single layer of infinite width. We propose exact and inexact learning strategies for wide learning and show that wide learning with single layer outperforms single layer as well as deep architectures of finite width for some benchmark datasets.
Output: To go deep or wide in learning?
Our hypothesis is that by equipping certain agents in a multi-agent system controlling an intelligent building with automated decision support, two important factors will be in­ creased. The first is energy saving in the building. The second is customer value-how the people in the building experience the ef­ fects of the actions of the agents. We give evi­ dence for the truth of this hypothesis through experimental findings related to tools for arti­ ficial decision making. A number of assump­ tions related to agent control, through moni­ toring and delegation of tasks to other kinds of agents, of rooms at a test site are relaxed. Each assumption controls at least one un­ certainty that complicates considerably the procedures for selecting actions part of each such agent. We show that in realistic deci­ sion situations, room-controlling agents can make bounded rational decisions even under dynamic real-time constraints. This result can be, and has been, generalized to other domains with even harsher time constraints.
Output: Artificial Decision Making Under Uncertainty in Intelligent Buildings
We propose a Multi-Layer Network based on the Bayesian framework of the Factor Graphs in Reduced Normal Form (FGrn) applied to a two-dimensional lattice. The Latent Variable Model (LVM) is the basic building block of a quadtree hierarchy built on top of a bottom layer of random variables that represent pixels of an image, a feature map, or more generally a collection of spatially distributed discrete variables. The multi-layer architecture implements a hierarchical data representation that, via belief propagation, can be used for learning and inference. Typical uses are pattern completion, correction and classification. The FGrn paradigm provides great flexibility and modularity and appears as a promising candidate for building deep networks: the system can be easily extended by introducing new and different (in cardinality and in type) variables. Prior knowledge, or supervised information, can be introduced at different scales. The FGrn paradigm provides a handy way for building all kinds of architectures by interconnecting only three types of units: Single Input Single Output (SISO) blocks, Sources and Replicators. The network is designed like a circuit diagram and the belief messages flow bidirectionally in the whole system. The learning algorithms operate only locally within each block. The framework is demonstrated in this paper in a three-layer structure applied to images extracted from a standard data set.
Output: Towards Building Deep Networks with Bayesian Factor Graphs
Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus the paper on the ‘trust’ component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three sub-topics: (1) reviewing and positioning the trust modelling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enables trusted autonomous systems to become a reality. The paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.
Output: A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data
There are few knowledge representation (KR) techniques available for efficiently representing knowledge. However, with the increase in complexity, better methods are needed. Some researchers came up with hybrid mechanisms by combining two or more methods. In an effort to construct an intelligent computer system, a primary consideration is to represent large amounts of knowledge in a way that allows effective use and efficiently organizing information to facilitate making the recommended inferences. There are merits and demerits of combinations, and standardized method of KR is needed. In this paper, various hybrid schemes of KR were explored at length and details presented. KeywordsKnowledge representation; hybrid system; hybrid schema structure.
Output: Hybrid Systems for Knowledge Representation in Artificial Intelligence
<lb>We consider online learning when the time horizon is unknown. We apply a<lb>minimax analysis, beginning with the fixed horizon case, and then moving on to<lb>two unknown-horizon settings, one that assumes the horizon is chosen randomly<lb>according to some known distribution, and the other which allows the adversary<lb>full control over the horizon. For the random horizon setting with restricted losses,<lb>we derive a fully optimal minimax algorithm. And for the adversarial horizon set-<lb>ting, we prove a nontrivial lower bound which shows that the adversary obtains<lb>strictly more power than when the horizon is fixed and known. Based on the mini-<lb>max solution of the random horizon setting, we then propose a new adaptive algo-<lb>rithm which “pretends” that the horizon is drawn from a distribution from a special<lb>family, but no matter how the actual horizon is chosen, the worst-case regret is of<lb>the optimal rate. Furthermore, our algorithm can be generalized in many ways,<lb>including handling other unknown information and other online learning settings.<lb>Experiments show that our algorithm outperforms many other existing algorithms<lb>in an online linear optimization setting.
Output: Online Learning with Unknown Time Horizon
Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled data structures to general weighted graphical models (e.g., probabilistic models). We present the AND/OR Multi-Valued Decision Diagram (AOMDD) which compiles a graphical model into a canonical form that supports polynomial (e.g., solution counting, belief updating) or constant time (e.g. equivalence of graphical models) queries. We provide two algorithms for compiling the AOMDD of a graphical model. The first is search-based, and works by applying reduction rules to the trace of the memory intensive AND/OR search algorithm. The second is inference-based and uses a Bucket Elimination schedule to combine the AOMDDs of the input functions via the the APPLY operator. For both algorithms, the compilation time and the size of the AOMDD are, in the worst case, exponential in the treewidth of the graphical model, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs). We introduce the concept of semantic treewidth, which helps explain why the size of a decision diagram is often much smaller than the worst case bound. We provide an experimental evaluation that demonstrates the potential of AOMDDs.
Output: AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models
CAPTCHAs or reverse Turing tests are real-time assessments used by programs (or computers) to tell humans and machines apart. This is achieved by assigning and assessing hard AI problems that could only be solved easily by human but not by machines. Applications of such assessments range from stopping spammers from automatically filling online forms to preventing hackers from performing dictionary attack. Today, the race between makers and breakers of CAPTCHAs is at a juncture, where the CAPTCHAs proposed are not even answerable by humans. We consider such CAPTCHAs as non user friendly. In this paper, we propose a novel technique for reverse Turing test we call it the Line CAPTCHAs that mainly focuses on user friendliness while not compromising the security aspect that is expected to be provided by such a system.
Output: User Friendly Line CAPTCHAs
This paper is a survey work for a bigger project for designing a Visual SLAM robot to generate 3D dense map of an unknown unstructured environment. A lot of factors have to be considered while designing a SLAM robot. Sensing method of the SLAM robot should be determined by considering the kind of environment to be modelled. Similarly the type of environment determines the suitable feature extraction method. This paper goes through the sensing methods used in some recently published papers. The main objective of this survey is to conduct a comparative study among the current sensing methodsandfeature extraction algorithms and to extract out the best for our work.
Output: A SURVEY ON SENSING METHODS
Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden), the segmentation of hashtags into constituent entities (“NSA” and “Edward Snowden” in this case) is not a trivial task. Most of the current state-of-the-art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. In this paper, we present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags such that the coherence between hashtag semantics and the tweet is maximized. To the best of our knowledge, no existing study addresses the issue of linking entities in hashtags for extracting semantic information. We evaluate our method on two different datasets, and demonstrate the effectiveness of our technique in improving the overall entity linking in tweets via additional semantic information provided by segmenting and linking entities in a hashtag.
Output: Towards Deep Semantic Analysis of Hashtags
Prediction markets provide an efficient means to assess uncertain quantities from forecasters. Traditional and competitive strictly proper scoring rules have been shown to incentivize players to provide truthful probabilistic forecasts. However, we show that when those players can cooperate, these mechanisms can instead discourage them from reporting what they really believe. When players with different beliefs are able to cooperate and form a coalition, these mechanisms admit arbitrage and there is a report that will always pay coalition members more than their truthful forecasts. If the coalition were created by an intermediary, such as a web portal, the intermediary would be guaranteed a profit.
Output: Strictly Proper Mechanisms with Cooperating Players
Tissue segmentation is an important pre-requisite for efficient and accurate diagnostics in digital pathology. However, it is well known that whole-slide scanners can fail in detecting all tissue regions, for example due to the tissue type, or due to weak staining because their tissue detection algorithms are not robust enough. In this paper, we introduce two different convolutional neural network architectures for whole slide image segmentation to accurately identify the tissue sections. We also compare the algorithms to a published traditional method. We collected 54 whole slide images with differing stains and tissue types from three laboratories to validate our algorithms. We show that while the two methods do not differ significantly they outperform their traditional counterpart (Jaccard index of 0.937 and 0.929 vs. 0.870, p < 0.01).
Output: COMPARISON OF DIFFERENT METHODS FOR TISSUE SEGMENTATION IN HISTOPATHOLOGICAL WHOLE-SLIDE IMAGES
Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the availability of large-scale data from social networks and sensor networks offers an unprecedented opportunity to predict state-changing events at the individual level. Examples of such events include disease transmission, opinion transition in elections, and rumor propagation. Unlike previous research focusing on the collective effects of social systems, this study makes efficient inferences at the individual level. In order to cope with dynamic interactions among a large number of individuals, we introduce the stochastic kinetic model to capture adaptive transition probabilities and propose an efficient variational inference algorithm the complexity of which grows linearly — rather than exponentially— with the number of individuals. To validate this method, we have performed epidemic-dynamics experiments on wireless sensor network data collected from more than ten thousand people over three years. The proposed algorithm was used to track disease transmission and predict the probability of infection for each individual. Our results demonstrate that this method is more efficient than sampling while nonetheless achieving high accuracy.
Output: Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model
Symbolic approaches to deep parsing often require large-coverage and fine-grained lexical information, such as a syntactic lexicon. LexiconGrammar tables (Gross 1975, 1994), carefully developed by linguists since the 70s, constitute such a syntactic resource. Each table represents a class of predicates sharing some syntactic features. Each row corresponds to a lexical entry (verb, predicative noun, predicative adjective, adverb, fixed expression) and each column corresponds to a syntactic feature (construction, argument distribution, and so on). However, they are not directly exploitable for NLP applications because pieces of information are not formally encoded although their informal descriptions are available in the literature. Some projects such as (Hathout et Namer 1998, Gardent et al. 2006, Sagot et Fort 2007, Danlos et Sagot 2009) attempted to reformat LexiconGrammar tables in a lexicon for NLP. In these projects, each class is assigned a specific configuration which encodes missing information and defines restructuration operations. For instance, each configuration in (Gardent et al. 2006) is represented by a graph that makes the class structure explicit and translates each column header into a feature structure. Nevertheless, Lexicon-Grammar tables are continually updated to be improved (e.g., addition and renaming of features) and this approach can be tedious to maintain. For example, if a same feature is added to several classes, all corresponding configurations have to be modified. In this paper, we describe LGExtract, a tool that uses a global approach. First, it relies on the so-called table of classes, which encodes pieces of information that are undefined in the original classes, especially features that are constant over a whole class. Next, as a syntactic feature has exactly one interpretation over the set of classes, our extraction script assigns to each feature a set of reformatting operations once. This paper is organized as follows. First, we briefly describe the LexiconGrammar classes and the table of classes, and their relevance to our work. Then, we present LGExtract in detail, illustrate it with a concrete example for French and discuss its main advantages and drawbacks.
Output: A generic tool to generate a lexicon for NLP from Lexicon-Grammar tables
In this paper, we study a novel approach for named entity recognition (NER) and mention detection in natural language processing. Instead of treating NER as a sequence labelling problem, we propose a new local detection approach, which rely on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixed-size representation. Afterwards, a simple feedforward neural network is used to reject or predict entity label for each individual fragment. The proposed method has been evaluated in several popular NER and mention detection tasks, including the CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016 Trilingual Entity Discovery and Linking (EDL) tasks. Our methods have yielded pretty strong performance in all of these examined tasks. This local detection approach has shown many advantages over the traditional sequence labelling methods.
Output: A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
There are many complex combinatorial problems which involve searching for an undirected graph satisfying given constraints. Such problems are often highly challenging because of the large number of isomorphic representations of their solutions. This paper introduces effective and compact, complete symmetry breaking constraints for small graph search. Enumerating with these symmetry breaks generates all and only non-isomorphic solutions. For small search problems, with up to 10 vertices, we compute instance independent symmetry breaking constraints. For small search problems with a larger number of vertices we demonstrate the computation of instance dependent constraints which are complete. We illustrate the application of complete symmetry breaking constraints to extend two known sequences from the OEIS related to graph enumeration.
Output: Breaking Symmetries in Graph Search with Canonizing Sets
We present new algorithms for learning Bayesian networks from data with missing values without the assumption that data are missing at random (MAR). An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create a new approximate algorithm using a hill-climbing technique. This algorithm scales to large domains so long as a suitable standard structure learning method for complete data is available. We perform a wide range of experiments to demonstrate the benefits of learning Bayesian networks without assuming MAR.
Output: Learning Bayesian Networks without Assuming Missing at Random
We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequences—a domain in which universal symbol inventories and cross-linguistically shared feature representations are a natural fit. Intrinsic evaluation on held-out perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show (i) that polyglot models better generalize to held-out data than comparable monolingual models and (ii) that polyglot phonetic feature representations are of higher quality than those learned monolingually.
Output: Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning
Our goal is to deploy a high-accuracy system starting with zero training examples. We consider an on-the-job setting, where as inputs arrive, we use real-time crowdsourcing to resolve uncertainty where needed and output our prediction when confident. As the model improves over time, the reliance on crowdsourcing queries decreases. We cast our setting as a stochastic game based on Bayesian decision theory, which allows us to balance latency, cost, and accuracy objectives in a principled way. Computing the optimal policy is intractable, so we develop an approximation based on Monte Carlo Tree Search. We tested our approach on three datasets—named-entity recognition, sentiment classification, and image classification. On the NER task we obtained more than an order of magnitude reduction in cost compared to full human annotation, while boosting performance relative to the expert provided labels. We also achieve a 8% F1 improvement over having a single human label the whole set, and a 28% F1 improvement over online learning. “Poor is the pupil who does not surpass his master.” – Leonardo da Vinci
Output: On-the-Job Learning with Bayesian Decision Theory
We describe recurrent neural networks (RNNs), which have attracted great attention on sequential tasks, such as handwriting recognition, speech recognition and image to text. However, compared to general feedforward neural networks, RNNs have feedback loops, which makes it a little hard to understand the backpropagation step. Thus, we focus on basics, especially the error backpropagation to compute gradients with respect to model parameters. Further, we go into detail on how error backpropagation algorithm is applied on long short-term memory (LSTM) by unfolding the memory unit.
Output: A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation
We propose a novel discriminative model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality. To that end, we introduce a modification of a pairwise contrastive estimation optimisation function as our training objective. We evaluate our embeddings on an image–sentence ranking (ISR), a semantic textual similarity (STS), and a neural machine translation (NMT) task. We find that the additional multilingual signals lead to improvements on both the ISR and STS tasks, and the discriminative cost can also be used in re-ranking n-best lists produced by NMT models, yielding strong improvements.
Output: Multilingual Multi-modal Embeddings for Natural Language Processing
The proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day, in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.
Output: Detection of Slang Words in e-Data using semi- Supervised Learning
Several authors have explained that the like­ lihood ratio measures the strength of the evi­ dence represented by observations in statisti­ cal problems. This idea works fine when the goal is to evaluate the strength of the avail­ able evidence for a simple hypothesis versus another simple hypothesis. However, the ap­ plicability of this idea is limited to simple hypotheses because the likelihood function is primarily defined on points simple hy­ potheses of the parameter space. In this paper we define a general weight of evidence that is applicable to both simple and compos­ ite hypotheses. It is based on the Dempster­ Shafer concept of plausibility and is shown to be a generalization of the likelihood ratio. Functional models are of a fundamental im­ portance for the general weight of evidence proposed in this paper. The relevant con­ cepts and ideas are explained by means of a familiar urn problem and the general analysis of a real-world medical problem is presented.
Output: From Likelihood to Plausibility
Concept Trees are a type of database that can organise arbitrary textual information using a very simple rule. Each tree tries to represent a single cohesive concept and the trees can link with each other for navigation and semantic purposes. The trees are therefore a type of semantic network and would benefit from having a consistent level of context for each of the nodes. The Concept Tree nodes have a mathematical basis allowing for a consistent build process. These would represent nouns or verbs in a text sentence, for example. New to the design can then be lists of descriptive elements for each of the nodes. The descriptors can also be weighted, but do not have to follow the strict counting rule of the tree nodes. With the new descriptive layers, a much richer type of knowledge can be achieved and still reasoned over automatically. The linking structure of the licas network is very relevant to building the concept trees now and forms the basis for their construction. The concept tree symbolic neural network relation is also extended further.
Output: Adding Context to Concept Trees
The utility of Twitter data as a medium to support populationlevel mental health monitoring is not well understood. In an effort to better understand the predictive power of supervised machine learning classifiers and the influence of feature sets for efficiently classifying depression-related tweets on a large-scale, we conducted two feature study experiments. In the first experiment, we assessed the contribution of feature groups such as lexical information (e.g., unigrams) and emotions (e.g., strongly negative) using a feature ablation study. In the second experiment, we determined the percentile of top ranked features that produced the optimal classification performance by applying a three-step feature elimination approach. In the first experiment, we observed that lexical features are critical for identifying depressive symptoms, specifically for depressed mood (-35 points) and for disturbed sleep (-43 points). In the second experiment, we observed that the optimal F1-score performance of top ranked features in percentiles variably ranged across classes e.g., fatigue or loss of energy (5th percentile, 288 features) to depressed mood (55th percentile, 3,168 features) suggesting there is no consistent count of features for predicting depressive-related tweets. We conclude that simple lexical features and reduced feature sets can produce comparable results to larger feature sets.
Output: Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health
In this paper, we propose a new autonomous braking system based on deep reinforcement learning. The proposed autonomous braking system automatically decides whether to apply the brake at each time step when confronting the risk of collision using the information on the obstacle obtained by the sensors. The problem of designing brake control is formulated as searching for the optimal policy in Markov decision process (MDP) model where the state is given by the relative position of the obstacle and the vehicle’s speed, and the action space is defined as whether brake is stepped or not. The policy used for brake control is learned through computer simulations using the deep reinforcement learning method called deep Q-network (DQN). In order to derive desirable braking policy, we propose the reward function which balances the damage imposed to the obstacle in case of accident and the reward achieved when the vehicle runs out of risk as soon as possible. DQN is trained for the scenario where a vehicle is encountered with a pedestrian crossing the urban road. Experiments show that the control agent exhibits desirable control behavior and avoids collision without any mistake in various uncertain environments.
Output: Autonomous Braking System via Deep Reinforcement Learning
Multiagent planning and coordination problems are common and known to be computationally hard. We show that a wide range of two-agent problems can be formulated as bilinear programs. We present a successive approximation algorithm that significantly outperforms the coverage set algorithm, which is the state-of-the-art method for this class of multiagent problems. Because the algorithm is formulated for bilinear programs, it is more general and simpler to implement. The new algorithm can be terminated at any time and–unlike the coverage set algorithm–it facilitates the derivation of a useful online performance bound. It is also much more efficient, on average reducing the computation time of the optimal solution by about four orders of magnitude. Finally, we introduce an automatic dimensionality reduction method that improves the effectiveness of the algorithm, extending its applicability to new domains and providing a new way to analyze a subclass of bilinear programs.
Output: A Bilinear Programming Approach for Multiagent Planning
Achieving joint objectives by teams of cooperative planning agents requires significant coordination and communication efforts. For a singleagent system facing a plan failure in a dynamic environment, arguably, attempts to repair the failed plan in general do not straightforwardly bring any benefit in terms of time complexity. However, in multi-agent settings the communication complexity might be of a much higher importance, possibly a high communication overhead might be even prohibitive in certain domains. We hypothesize that in decentralized systems, where coordination is enforced to achieve joint objectives, attempts to repair failed multi-agent plans should lead to lower communication overhead than replanning from scratch. The contribution of the presented paper is threefold. Firstly, we formally introduce the multi-agent plan repair problem and formally present the core hypothesis underlying our work. Secondly, we propose three algorithms for multi-agent plan repair reducing the problem to specialized instances of the multi-agent planning problem. Finally, we present results of experimental validation confirming the core hypothesis of the paper.
Output: Decentralized Multi-agent Plan Repair in Dynamic Environments∗
In this paper, we attempt to solve the problem of Prepositional Phrase (PP) attachments in English. The motivation for the work comes from NLP applications like Machine Translation, for which, getting the correct attachment of prepositions is very crucial. The idea is to correct the PPattachments for a sentence with the help of alignments from parallel data in another language. The novelty of our work lies in the formulation of the problem into a dual decomposition based algorithm that enforces agreement between the parse trees from two languages as a constraint. Experiments were performed on the EnglishHindi language pair and the performance improved by 10% over the baseline, where the baseline is the attachment predicted by the MSTParser model trained for English.
Output: Prepositional Attachment Disambiguation Using Bilingual Parsing and Alignments
Image captioning has so far been explored mostly in English, as most available datasets are in this language. However, the application of image captioning should not be restricted by language. Only few studies have been conducted for image captioning in a cross-lingual seing. Dierent from these works that manually build a dataset for a target language, we aim to learn a cross-lingual captioning model fully from machine-translated sentences. To conquer the lack of uency in the translated sentences, we propose in this paper a uency-guided learning framework. e framework comprises a module to automatically estimate the uency of the sentences and another module to utilize the estimated uency scores to eectively train an image captioning model for the target language. As experiments on two bilingual (English-Chinese) datasets show, our approach improves both uency and relevance of the generated captions in Chinese, but without using any manually wrien sentences from the target language.
Output: Fluency-Guided Cross-Lingual Image Captioning
In this article, an agent-based negotiation model for negotiation teams that negotiate a deal with an opponent is presented. Agent-based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process. The model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process: which offer is sent to the opponent, and whether or not the offers received from the opponent are accepted. The main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round. This work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations. An empirical evaluation is also performed to study the impact of the different parameters of the model.
Output: Reaching Unanimous Agreements within Agent-Based Negotiation Teams with Linear and Monotonic Utility Functions
Analysing sentiment of tweets is important as it helps to determine the users’ opinion. Knowing people’s opinion is crucial for several purposes starting from gathering knowledge about customer base, e-governance, campaignings and many more. In this report, we aim to develop a system to detect the sentiment from tweets. We employ several linguistic features along with some other external sources of information to detect the sentiment of a tweet. We show that augmenting the 140 character-long tweet with information harvested from external urls shared in the tweet as well as Social Media features enhances the sentiment prediction accuracy significantly.
Output: Sentiment Analysis for Twitter : Going Beyond Tweet Text
We propose a novel algorithm for optimizing multivariate linear threshold functions as split functions of decision trees to create improved Random Forest classifiers. Standard tree induction methods resort to sampling and exhaustive search to find good univariate split functions. In contrast, our method computes a linear combination of the features at each node, and optimizes the parameters of the linear combination (oblique) split functions by adopting a variant of latent variable SVM formulation. We develop a convex-concave upper bound on the classification loss for a one-level decision tree, and optimize the bound by stochastic gradient descent at each internal node of the tree. Forests of up to 1000 Continuously Optimized Oblique (CO2) decision trees are created, which significantly outperform Random Forest with univariate splits and previous techniques for constructing oblique trees. Experimental results are reported on multi-class classification benchmarks and on Labeled Faces in the Wild (LFW) dataset.
Output: CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits
This paper presents a system which creates and visualizes probabilistic semantic links between concepts in a thesaurus and classes in a classification system. For creating the links, we build on the Polylingual Labeled Topic Model (PLL-TM) [6]. PLL-TM identifies probable thesaurus descriptors for each class in the classification system by using information from the natural language text of documents, their assigned thesaurus descriptors and their designated classes. The links are then presented to users of the system in an interactive visualization, providing them with an automatically generated overview of the relations between the thesaurus and the classification system. Lisa Posch GESIS – Leibniz Institute for the Social Sciences Cologne, Germany Institute for Web Science and Technologies University of Koblenz-Landau, Germany E-mail: lisa.posch@gesis.org Philipp Schaer GESIS – Leibniz Institute for the Social Sciences Cologne, Germany E-mail: philipp.schaer@gesis.org Arnim Bleier GESIS – Leibniz Institute for the Social Sciences Cologne, Germany E-mail: arnim.bleier@gesis.org Markus Strohmaier GESIS – Leibniz Institute for the Social Sciences Cologne, Germany Institute for Web Science and Technologies University of Koblenz-Landau, Germany E-mail: markus.strohmaier@gesis.org
Output: A System for Probabilistic Linking of Thesauri and Classification Systems
One difficulty faced in knowledge engineering for Bayesian Network (BN) is the quantification step where the Conditional Probability Tables (CPTs) are determined. The number of parameters included in CPTs increases exponentially with the number of parent variables. The most common solution is the application of the so-called canonical gates. The Noisy-OR (NOR) gate, which takes advantage of the independence of causal interactions, provides a logarithmic reduction of the number of parameters required to specify a CPT. In this paper, an extension of NOR model based on the theory of belief functions, named Belief Noisy-OR (BNOR), is proposed. BNOR is capable of dealing with both aleatory and epistemic uncertainty of the network. Compared with NOR, more rich information which is of great value for making decisions can be got when the available knowledge is uncertain. Specially, when there is no epistemic uncertainty, BNOR degrades into NOR. Additionally, different structures of BNOR are presented in this paper in order to meet various needs of engineers. The application of BNOR model on the reliability evaluation problem of networked systems demonstrates its effectiveness.
Output: THE BELIEF NOISY-OR MODEL APPLIED TO NETWORK RELIABILITY ANALYSIS