varunr14/newsummary huggingface dataset:
Today s power systems are experiencing a transition from primarily fossil fuel based generation toward greater shares of renewable energy sources . It becomes increasingly costly to manage the resulting uncertainty and variability in power system operations solely through flexible generation assets . Incorporating demand side flexibility through appropriately designed incentive structures can add an additional lever to balance demand and supply . Based on a supply model using empirical wind generation data and a discrete model of flexible demand with temporal constraints we design and evaluate a local online market mechanism for matching flexible load and uncertain supply . Under this mechanism truthful reporting of flexibility is a dominant strategy for consumers reducing payments and increasing the likelihood of allocation . Suppliers during periods of scarce supply benefit from elevated critical value payments as a result of flexibility induced competition on the demand side . We find that for a wide range of the key parameters the cost of ensuring incentive compatibility in a smart grid market relative to the welfare optimal matching is relatively small . This suggests that local matching of demand and supply can be organized in a decentral manner in the presence of a sufficiently flexible demand side . Extending the stylized demand model to include complementary demand structures we demonstrate that decentral matching induces only minor efficiency losses if demand is sufficiently flexible . Furthermore by accounting for physical grid limitations we show that flexibility and grid capacity exhibit complementary characteristics . 


Summary: We present a model of flexible loads and renewable supply in the smart grid. Supply and demand are matched locally via an incentive compatible online mechanism. Demand flexibility reduces payments and increases allocation probability. Increasing demand flexibility will increase suppliers profits. The cost of establishing incentive compatibility is decreasing in flexibility.
This manuscript reviews recent advances in deterministic global optimization for Mixed Integer Nonlinear Programming as well as Constrained Derivative Free Optimization . This work provides a comprehensive and detailed literature review in terms of significant theoretical contributions algorithmic developments software implementations and applications for both MINLP and CDFO . Both research areas have experienced rapid growth with a common aim to solve a wide range of real world problems . We show their individual prerequisites formulations and applicability but also point out possible points of interaction in problems which contain hybrid characteristics . Finally an inclusive and complete test suite is provided for both MINLP and CDFO algorithms which is useful for future benchmarking . 


Summary: We review the recent advances in global optimization for Mixed Integer Nonlinear Programming MINLP. We review the recent advances in global optimization for Constrained Derivative Free optimization CDFO. We present theoretical contributions software implementations and applications for both MINLP and CDFO. We discuss possible interactions between the two areas of MINLP and CDFO. We present a complete test suite for MINLP and CDFO algorithms.
Language is being increasingly harnessed to not only create natural human machine interfaces but also to infer social behaviors and interactions . In the same vein we investigate a novel spoken language task of inferring social relationships in two party conversations whether the two parties are related as family strangers or are involved in business transactions . For our study we created a corpus of all incoming and outgoing calls from a few homes over the span of a year . On this unique naturalistic corpus of everyday telephone conversations which is unlike Switchboard or any other public domain corpora we demonstrate that standard natural language processing techniques can achieve accuracies of about 88 82 74 and 80 in differentiating business from personal calls family from non family calls familiar from unfamiliar calls and family from other personal calls respectively . Through a series of experiments with our classifiers we characterize the properties of telephone conversations and find that 30 words of openings are sufficient to predict business from personal calls which could potentially be exploited in designing context sensitive interfaces in smart phones our corpus based analysis does not support Schegloff and Sack s manual analysis of exemplars in which they conclude that pre closings differ significantly between business and personal calls closing fared no better than a random segment and the distribution of different types of calls are stable over durations as short as 1 2 months . In summary our results show that social relationships can be inferred automatically in two party conversations with sufficient accuracy to support practical applications . 


Summary: We introduce a novel task that of inferring social relationships from everyday conversations. We collected a corpus of natural telephone conversations unlike any other publicly available corpora. We show that 30 words of the beginning of a conversation is sufficient to infer the relationship accurately. We show that classifiers are useful in estimating the social engagement using conversations spanning 3 months.
In recent years the use of rhythm based features in speech processing systems has received growing interest . This approach uses a wide array of rhythm metrics that have been developed to capture speech timing differences between and within languages . However the reliability of rhythm metrics is being increasingly called into question . In this paper we propose two modifications to this approach . First we describe a model that is based on auditory cues that simulate the external middle and inner parts of the ear . We evaluate this model by performing experiments to discriminate between native and non native Arabic speech . Data are from the West Point Arabic Speech Corpus testing is done on standard classifiers based on Gaussian Mixture Models Support Vector Machines and a hybrid GMM SVM . Results show that the auditory based model consistently outperforms a traditional rhythm metric approach that includes both duration and intensity based metrics . Second we propose a framework that combines the rhythm metrics and the auditory based cues in the context of a Logistic Regression method that can optimize feature combination . Further results show that the proposed LR based method improves performance over the standard classifiers in the discrimination between the native and non native Arabic speech . 


Summary: New models of speech rhythm and auditory knowledge are proposed. Use both duration and intensity metrics to classify native and non native accents. Perform accent classification by logistic regression LR and with baseline systems. LR based approach provides the best classification of native non native Arabic speech. Combination of auditoryc indicative features and rhythm metrics provides the best classification.
Sentiment analysis is the natural language processing task dealing with sentiment detection and classification from texts . In recent years due to the growth in the quantity and fast spreading of user generated contents online and the impact such information has on events people and companies worldwide this task has been approached in an important body of research in the field . Despite different methods having been proposed for distinct types of text the research community has concentrated less on developing methods for languages other than English . In the above mentioned context the present work studies the possibility to employ machine translation systems and supervised methods to build models able to detect and classify sentiment in languages for which less no resources are available for this task when compared to English stressing upon the impact of translation quality on the sentiment classification performance . Our extensive evaluation scenarios show that machine translation systems are approaching a good level of maturity and that they can in combination to appropriate machine learning algorithms and carefully chosen features be used to build sentiment analysis systems that can obtain comparable performances to the one obtained for English . 


Summary: We study the possibility to employ Machine Translation MT systems and supervised methods for multilingual sentiment analysis. Experiments are done for English German Spanish and French. We use three MT systems Google Bing and Moses different supervised learning algorithms and various types of features. We show how meta classifiers can be employed to mitigate the noise introduced by translation. Our extensive evaluations show that MT systems can be used for multilingual sentiment analysis.
Phonological studies suggest that the typical subword units such as phones or phonemes used in automatic speech recognition systems can be decomposed into a set of features based on the articulators used to produce the sound . Most of the current approaches to integrate articulatory feature representations into an automatic speech recognition system are based on a deterministic knowledge based phoneme to AF relationship . In this paper we propose a novel two stage approach in the framework of probabilistic lexical modeling to integrate AF representations into an ASR system . In the first stage the relationship between acoustic feature observations and various AFs is modeled . In the second stage a probabilistic relationship between subword units and AFs is learned using transcribed speech data . Our studies on a continuous speech recognition task show that the proposed approach effectively integrates AFs into an ASR system . Furthermore the studies show that either phonemes or graphemes can be used as subword units . Analysis of the probabilistic relationship captured by the parameters has shown that the approach is capable of adapting the knowledge based phoneme to AF representations using speech data and allows different AFs to evolve asynchronously . 


Summary: Approach for AF based ASR in framework of probabilistic lexical modeling is proposed. Most approaches in literature use a knowledge based deterministic phoneme to AF map. Approach incorporates a probabilistic phoneme to AF map learned through acoustic data. Analysis has shown that the approach allows different AFs to evolve asynchronously. Approach has potential to reduce word error rates by incorporating AFs in an ASR system.
In this work we present a comprehensive study on the use of deep neural networks for automatic language identification . Motivated by the recent success of using DNNs in acoustic modeling for speech recognition we adapt DNNs to the problem of identifying the language in a given utterance from its short term acoustic features . We propose two different DNN based approaches . In the first one the DNN acts as an end to end LID classifier receiving as input the speech features and providing as output the estimated probabilities of the target languages . In the second approach the DNN is used to extract bottleneck features that are then used as inputs for a state of the art i vector system . Experiments are conducted in two different scenarios the complete NIST Language Recognition Evaluation dataset 2009 and a subset of the Voice of America data from LRE 09 in which all languages have the same amount of training data . Results for both datasets demonstrate that the DNN based systems significantly outperform a state of art i vector system when dealing with short duration utterances . Furthermore the combination of the DNN based and the classical i vector system leads to additional performance improvements . 


Summary: This work presents a comprehensive study on the use of deep neural networks for automatic language identification. It includes a detailed performance analysis for different data selection strategies and DNN architectures. Proposed systems are tested on the NIST Language Recognition Evaluation 2009 against an state of the art i vector baseline. It also presents a novel approach that combines DNN and i vector systems by using bottleneck features. The combination of i vector and bottleneck systems outperforms our baseline system by 45 in EER and Cavg on 3s and 10s.
In this paper we present a survey on the application of recurrent neural networks to the task of statistical language modeling . Although it has been shown that these models obtain good performance on this task often superior to other state of the art techniques they suffer from some important drawbacks including a very long training time and limitations on the number of context words that can be taken into account in practice . Recent extensions to recurrent neural network models have been developed in an attempt to address these drawbacks . This paper gives an overview of the most important extensions . Each technique is described and its performance on statistical language modeling as described in the existing literature is discussed . Our structured overview makes it possible to detect the most promising techniques in the field of recurrent neural networks applied to language modeling but it also highlights the techniques for which further research is required . 


Summary: We explain in detail the different steps in computing a language model based on a recurrent neural network. We survey the applications and findings based on the current literature. We survey the methods for reducing computational complexity.
In this paper we suggest a list of high level features and study their applicability in detection of cyberpedophiles . We used a corpus of chats downloaded from http www.perverted justice.com and two negative datasets of different nature cybersex logs available online and the NPS chat corpus . The classification results show that the NPS data and the pedophiles conversations can be accurately discriminated from each other with character n grams while in the more complicated case of cybersex logs there is need for high level features to reach good accuracy levels . In this latter setting our results show that features that model behaviour and emotion significantly outperform the low level ones and achieve a 97 accuracy . 


Summary: High level features for cyberpedophilia detection are proposed. The fixated discourse model is suggested. Experiments on distinguishing between pedophiles and non pedophiles chats are performed. Feature analysis is presented.
Discriminative confidence based on multi layer perceptrons and multiple features has shown significant advantage compared to the widely used lattice based confidence in spoken term detection . Although the MLP based framework can handle any features derived from a multitude of sources choosing all possible features may lead to over complex models and hence less generality . In this paper we design an extensive set of features and analyze their contribution to STD individually and as a group . The main goal is to choose a small set of features that are sufficiently informative while keeping the model simple and generalizable . We employ two established models to conduct the analysis one is linear regression which targets for the most relevant features and the other is logistic linear regression which targets for the most discriminative features . We find the most informative features are comprised of those derived from diverse sources and the two models deliver highly consistent feature ranks . STD experiments on both English and Spanish data demonstrate significant performance gains with the proposed feature sets . 


Summary: Feature analysis for spoken term detection STD on English meeting domain and Spanish read speech data in a discriminative confidence estimation framework. Feature analysis is based on groups that are defined according to their information sources lattice based features duration based features lexical features Levenshtein distance based features position and prosodic features pitch and energy . Feature analysis employs two well known and established models linear regression a generative approach and logistic linear regression a discriminative approach . Individual and incremental analyses are presented for both models. Results demonstrate significant improvement with the 3 5 most informative features compared with using the single best feature for STD confidence estimation. The best feature set comprises features from different groups lattice based and lexical features are among the most informative groups in general and duration and energy are more informative for read speech data.
Globalization has dramatically increased the need of translating information from one language to another . Frequently such translation needs should be satisfied under very tight time constraints . Machine translation techniques can constitute a solution to this overly complex problem . However the documents to be translated in real scenarios are often limited to a specific domain such as a particular type of medical or legal text . This situation seriously hinders the applicability of MT since it is usually expensive to build a reliable translation system no matter what technology is used due to the linguistic resources that are required to build them such as dictionaries translation memories or parallel texts . In order to solve this problem we propose the application of automatic post editing in an online learning framework . Our proposed technique allows the human expert to translate in a specific domain by using a base translation system designed to work in a general domain whose output is corrected by means of an automatic post editing module . This automatic post editing module learns to make its corrections from user feedback in real time by means of online learning techniques . We have validated our system using different translation technologies to implement the base translation system as well as several texts involving different domains and languages . In most cases our results show significant improvements in terms of BLEU with respect to the baseline systems . The proposed technique works effectively when the n grams of the document to be translated presents a certain rate of repetition situation which is common according to the document internal repetition property . 


Summary: We present a method to customize machine translation systems when in domain data is not available. For that we perform an online learning automatic post editing from ready to use generic machine translation systems. The results show that the method is very effective on rule based machine translation systems. On statistical machine translation systems the method performs well if no in domain data was used in the training. Finally if there is not enough repetition our method has limited use.
Natural languages are known for their expressive richness . Many sentences can be used to represent the same underlying meaning . Only modelling the observed surface word sequence can result in poor context coverage and generalization for example when using n gram language models . This paper proposes a novel form of language model the paraphrastic LM that addresses these issues . A phrase level paraphrase model statistically learned from standard text data with no semantic annotation is used to generate multiple paraphrase variants . LM probabilities are then estimated by maximizing their marginal probability . Multi level language models estimated at both the word level and the phrase level are combined . An efficient weighted finite state transducer based paraphrase generation approach is also presented . Significant error rate reductions of 0.5 0.6 absolute were obtained over the baseline n gram LMs on two state of the art recognition tasks for English conversational telephone speech and Mandarin Chinese broadcast speech using a paraphrastic multi level LM modelling both word and phrase sequences . When it is further combined with word and phrase level feed forward neural network LMs a significant error rate reduction of 0.9 absolute and 0.5 absolute were obtained over the baseline n gram and neural network LMs respectively . 


Summary: Paraphrastic language models proposed. Statistical paraphrase learning from standard texts. Improved LM context coverage and generalization performance. Combination with word and phrase level neural network LMs. Significant error rate reductions of 5 9 relative.
SAMAR is a system for subjectivity and sentiment analysis for Arabic social media genres . Arabic is a morphologically rich language which presents significant complexities for standard approaches to building SSA systems designed for the English language . Apart from the difficulties presented by the social media genres processing the Arabic language inherently has a high number of variable word forms leading to data sparsity . In this context we address the following 4 pertinent issues how to best represent lexical information whether standard features used for English are useful for Arabic how to handle Arabic dialects and whether genre specific features have a measurable impact on performance . Our results show that using either lemma or lexeme information is helpful as well as using the two part of speech tagsets . However the results show that we need individualized solutions for each genre and task but that lemmatization and the ERTS POS tagset are present in a majority of the settings . 


Summary: We present a system for subjectivity and sentiment analysis SSA for Arabic social media data. Individual settings are required per genre and task. Using either lemmas or lexemes improves SSA results. Using a POS tagset leads improved results as do standard features. Processing dialects does not improve when it is know which sentences are in dialect. Genre specific features tend to be helpful for sentiment analysis but not for subjectivity.
Non verbal communication involves encoding transmission and decoding of non lexical cues and is realized using vocal or visual channels during conversation . These cues perform the function of maintaining conversational flow expressing emotions and marking personality and interpersonal attitude . In particular non verbal cues in speech such as paralanguage and non verbal vocal events are used to nuance meaning and convey emotions mood and attitude . For instance laughters are associated with affective expressions while fillers are used to hold floor during a conversation . In this paper we present an automatic non verbal vocal events detection system focusing on the detect of laughter and fillers . We extend our system presented during Interspeech 2013 Social Signals Sub challenge for frame wise event detection and test several schemes for incorporating local context during detection . Specifically we incorporate context at two separate levels in our system the raw frame wise features and the output decisions . Furthermore our system processes the output probabilities based on a few heuristic rules in order to reduce erroneous frame based predictions . Our overall system achieves an Area Under the Receiver Operating Characteristics curve of 95.3 for detecting laughters and 90.4 for fillers on the test set drawn from the data specifications of the Interspeech 2013 Social Signals Sub challenge . We perform further analysis to understand the interrelation between the features and obtained results . Specifically we conduct a feature sensitivity analysis and correlate it with each feature s stand alone performance . The observations suggest that the trained system is more sensitive to a feature carrying higher discriminability with implications towards a better system design . 


Summary: We present a sequential algorithm for detecting laughters and fillers in speech. The algorithm performs stepwise probability prediction context inclusion masking. We test several architectures for each of the above steps. Our models are more sensitive to change in feature carrying higher predictive power.