Question: Not every dialogue is the same: instance weighting for neural conversational models.

This paper introduces a persona-based neural conversation model designed to address the challenge of speaker consistency in neural response generation. Our approach employs speaker models that encode personas through distributed embeddings, effectively capturing individual traits like background details and speaking style. Additionally, we propose a dyadic speaker-addressee model that focuses on the dynamics between two conversational partners, enhancing the model's ability to generate contextually appropriate and consistent responses. The persona-based models demonstrate significant qualitative improvements in perplexity and BLEU scores compared to baseline sequence-to-sequence models. Furthermore, human evaluations confirm that these models significantly enhance speaker consistency, a crucial aspect in creating natural and engaging dialogues.

• Neural network language model and distributed representation for words (Vector representation) • Capture syntactic and remantic regularities in language • Outperform state-of-the-art

Inquiry is fundamental to communication, and machines cannot effectively collaborate with humans unless they can ask questions. In this thesis work, we explore how can we teach machines to ask clarification questions when faced with uncertainty, a goal of increasing importance in today’s automated society. We do a preliminary study using data from StackExchange, a plentiful online resource where people routinely ask clarifying questions to posts so that they can better offer assistance to the original poster. We build neural network models inspired by the idea of the expected value of perfect information: a good question is one whose expected answer is going to be most useful. To build generalizable systems, we propose two future research directions: a template-based model and a sequence-to-sequence based neural generative model.

We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.

When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat (IRC) dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. This is, to our knowledge, the first such corpus for internet chat. We propose a graph-theoretic model for disentanglement, using discourse-based features which have not been previously applied to this task. The model’s predicted disentanglements are highly correlated with manual annotations.

Existing spoken dialogue systems are typically designed to operate in a static and well-defined domain, and are not well suited to tasks in which the concepts and values change dynamically. To handle dynamically changing domains, techniques will be needed to transfer and reuse existing dialogue policies and rapidly adapt them using a small number of dialogues in the new domain. As a first step in this direction, this paper addresses the problem of automatically extending a dialogue system to include a new previously unseen concept (or slot) which can be then used as a search constraint in an information query. The paper shows that in the context of Gaussian process POMDP optimisation, a domain can be extended through a simple expansion of the kernel and then rapidly adapted. As well as being much quicker, adaptation rather than retraining from scratch is shown to avoid subjecting users to unacceptably poor performance during the learning stage.

Recurrent Neural Networks (RNN) are a type of statistical model designed to handle sequential data. The model reads a sequence one symbol at a time. Each symbol is processed based on information collected from the previous symbols. With existing RNN architectures, each symbol is processed using only information from the previous processing step. To overcome this limitation, we propose a new kind of RNN model that computes a recurrent weighted average (RWA) over every past processing step. Because the RWA can be computed as a running average, the computational overhead scales like that of any other RNN architecture. The approach essentially reformulates the attention mechanism into a stand-alone model. The performance of the RWA model is assessed on the variable copy problem, the adding problem, classification of artificial grammar, classification of sequences by length, and classification of the MNIST images (where the pixels are read sequentially one at a time). On almost every task, the RWA model is found to fit the data significantly faster than a standard LSTM model.

We motivate and describe a new freely available human-human dialogue data set for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as “burchak” for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include selfand other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rulebased system built previously.

We introduce a method of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Most current question answering models cannot scale to document or multi-document input, and naively applying these models to each paragraph independently often results in them being distracted by irrelevant text. We show that it is possible to significantly improve performance by using a modified training scheme that teaches the model to ignore non-answer containing paragraphs. Our method involves sampling multiple paragraphs from each document, and using an objective function that requires the model to produce globally correct output. We additionally identify and improve upon a number of other design decisions that arise when working with document-level data. Experiments on TriviaQA and SQuAD shows our method advances the state of the art, including a 10 point gain on TriviaQA.

Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presumed that humans should take the role to lead the conversation and introduce new content when a stalemate occurs, and that the computer only needs to “respond.” In this paper, we propose STALEMATEBREAKER, a conversation system that can proactively introduce new content when appropriate. We design a pipeline to determine when, what, and how to introduce new content during human-computer conversation. We further propose a novel reranking algorithm BiPageRank-HITS to enable rich interaction between conversation context and candidate replies. Experiments show that both the content-introducing approach and the reranking algorithm are effective. Our full STALEMATEBREAKER model outperforms a state-of-the-practice conversation system by +14.4% p@1 when a stalemate occurs.

A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.


Question: Not every conversation is the same: Instance weighting for neural conversational systems.

This paper explores the application of discriminative learning in scenarios where the distribution of training instances significantly deviates from the test distribution, a challenge often termed classification under covariate shift. Our approach avoids modeling either distribution explicitly, focusing instead on a purely discriminative solution. We present an integrated optimization framework to tackle the general issue of learning under covariate shift. Within this framework, we develop a kernel logistic regression classifier specifically tailored to handle differing training and test distributions, demonstrating its effectiveness in addressing the complexities of classification under varying data distributions.

We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long shortterm memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-ofthe-art despite having 60% fewer parameters. On languages with rich morphology (Czech, German, French, Spanish, Russian), the model consistently outperforms a Kneser-Ney baseline and word-level/morpheme-level LSTM baselines, again with far fewer parameters. Our results suggest that on many languages, character inputs are sufficient for language modeling.

We propose an interactive multimodal framework for language learning. Instead of being passively exposed to large amounts of natural text, our learners (implemented as feed-forward neural networks) engage in cooperative referential games starting from a tabula rasa setup, and thus develop their own language from the need to communicate in order to succeed at the game. Preliminary experiments provide promising results, but also suggest that it is important to ensure that agents trained in this way do not develop an adhoc communication code only effective for the game they are playing.

We present the components of the ColumbiaGWU contribution to the 2016 TAC KBP BeSt Evaluation.

We describe an open-source toolkit for neural machine translation (NMT). The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining competitive performance and reasonable training requirements. The toolkit consists of modeling and translation support, as well as detailed pedagogical documentation about the underlying techniques.

The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper. In particular, we show that additional information available in the form of dialogue acts –when used along with context given in the form of dialogue history– improves the performance irrespective of the underlying model being generative or discriminative. In order to show the model agnostic behavior of dialogue acts, we experiment with several well-known models such as sequence-to-sequence encoder-decoder model, hierarchical encoder-decoder model, and Siamese-based models with and without hierarchy; and show that in all models, incorporating dialogue acts improves the performance by a significant margin. We, furthermore, propose a novel way of encoding dialogue act information, and use it along with hierarchical encoder to build a model that can use the sequential dialogue act information in a natural way. Our proposed model achieves an MRR of about 84.8% for the task of next utterance selection on a newly introduced DailyDialog dataset, and outperform the baseline models. We also provide a detailed analysis of results including key insights that explain the improvement in MRR because of dialogue act information.

This paper presents a task for machine listening comprehension in the argumentation domain and a corresponding dataset in English. We recorded 200 spontaneous speeches arguing for or against 50 controversial topics. For each speech, we formulated a question, aimed at confirming or rejecting the occurrence of potential arguments in the speech. Labels were collected by listening to the speech and marking which arguments were mentioned by the speaker. We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset. All data used in this work is freely available for research.

We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in state-of-the-art-performance.

Asynchronous conversations are conversations where participants collaborate with each other at different times (e.g., email, blog, forum). The huge amount of textual data generated everyday in these conversations calls for automated methods of conversational text analysis. Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many NLP applications including summarization, information extraction and conversation visualization. Topic segmentation in asynchronous conversation refers to the task of clustering the sentences into a set of coherent topical clusters. In [6, 7], we presented unsupervised and supervised topic segmentation models for asynchronous conversations, which to our knowledge are the state-of-the-art. This study concerns topic labeling, that is, given a set of topical clusters in a conversation, the task is to assign appropriate topic labels to the clusters. For example, five topic labels in a Slashdot [2] blog conversation about releasing a new game called Daggerfall are game contents and size, game design, bugs/faults, other gaming options and performance/speed issues. Such topic labels can serve as a concise summary of the conversation and can also be used for indexing. Ideally, topic labels should be meaningful, semantically similar to the underlying topic, general (i.e., broad coverage of the topic) and discriminative (or exclusive) when there are multiple topics [10]. Traditionally, the top K terms in a multinomial topic model (e.g., LDA [3]) are used to represent a topic. However, as pointed out by [10], at the word-level, topic labels may become too general that it can impose cognitive difficulties on a user to interpret the meaning of the topic by associating the words together. On the other hand, if the labels are expressed at the sentence-level, they may become too specific to cover the whole theme of the topic. Based on these observations, [10] and other recent studies (e.g., [9]) advocate for phrase-level topic labels. This is also consistent with the monologue corpora built as part of the Topic Detection and Tracking (TDT) project [1] as well as with our own email and blog conversational corpora in which human annotators without specific instructions spontaneously generated labels at the phrase-level. Considering all these factors, we also treat phrase-level as the right level of granularity for a label in this work. Few prior studies have addressed the topic labeling problem in different settings [10, 9]. Common to their approaches is that they first mine topics in the form of topic-word distributions from the whole corpus using topic models like LDA. Then, they try to label the topics (i.e., topic-word distributions) with an appropriate label using the statistical association metrics (e.g., point-wise mutual information, t-test) computed from either the source corpus or an external knowledge base (e.g., Wikipedia). In contrast, our task is to label the topical clusters in a given conversation, where topics are closely related and distributional variations are subtle (e.g., game contents and size, game design). Therefore, corpus-based statistical association metrics are not reliable in our case. Also at the conversation-level, the topics are too specific to find their labels in an external source. To our knowledge, none has studied this problem before. Therefore, there is no standard corpus and no agreed-upon evaluation metrics available. Our contributions aim to remedy these problems. First, we present a blog and an email corpora annotated with topics. Second, we propose to generate topic labels using an extractive approach, that finds the most representative phrases from the text without relying on an external source. Since, graph-based key phrase ranking has proved to be the state-of-the-art (unsupervised) method [11], we adopt the same framework. We propose a novel biased random walk model that exploits the fact that the leading sentences in a topic often carry the most informative clues for its label. However, the phrases extracted only by considering the sentences in a topic may ignore the global aspect of the conversation. As another contribution, we propose to re-rank the phrases extracted from the whole conversation with respect to the individual topics and include the relevant ones. Experimental results show that our approach outperforms other ranking models including a general random walk model (i.e., TextRank) proposed by [11], a leadoriented model and a frequency-based model, and including the relevant conversation-level phrases improves the performance.

Telling stories about our daily lives is one of the most ubiquitous, consequential and seamless ways in which we socialize. Current narrative generation methods mostly require specification of a priori knowledge or comprehensive domain models, which are not generalizable across contexts. Hence, such approaches do not lend themselves well to new and unpredictable domains of observation and interaction, in which social stories usually occur. In this paper, we describe a methodology for categorizing event descriptions as being socially interesting. The event sequences are drawn from crowd-sourced Plot Graphs. The models include low-level natural language and higher-level features. The results from classification and regression tasks look promising overall, indicating that general metrics of social interestingness of stories could be modeled for sociable agents.

In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief “opinionated survey” of broadcoverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR–some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on imple-


Question: Not every dialogue is the same: Instance weighting for neural conversational models.

This paper presents an innovative unsupervised method for modeling dialogue acts in open domains, specifically targeting the unique environment of Twitter conversations. Unlike previous approaches, our technique identifies dialogue acts by clustering raw utterances from a large, unfiltered Twitter corpus. By capturing the sequential dynamics of these dialogue acts, our model offers a nuanced understanding of communication patterns in this emerging medium. To validate the efficacy of our model, we employ a qualitative visualization method and conduct an intrinsic conversation ordering task. This research is driven by a massive dataset comprising 1.3 million Twitter conversations, which we will release to the public, emphasizing the significance of adaptability to new communication platforms like Twitter, where the boundaries between casual chatting and formal publishing are blurred.

**Question**: Evaluating the Impact of Textual Complexity on Reading Comprehension: A Multimodal Approach

**Documents**: The Role of Visual and Textual Cues in Reading Comprehension
This study investigates the impact of visual and textual cues on reading comprehension in a multimodal setting. We explore how the integration of these cues affects the understanding of complex texts. Participants were asked to read and comprehend passages with varying levels of textual complexity and visual support. The results show that the presence of visual cues significantly enhances comprehension, particularly for more complex texts. This suggests that multimodal approaches can be beneficial in educational contexts, where the combination of textual and visual information can aid in the processing and retention of complex information.

**Similar Documents**: This research examines the influence of visual and textual cues on reading comprehension within a multimodal framework. Our investigation focuses on how the interplay between these cues influences the understanding of texts with varying degrees of complexity. In our study, participants were tasked with reading and comprehending passages that featured different levels of textual intricacy and visual supplementation. The findings indicate that the inclusion of visual cues markedly improves comprehension, especially for texts of higher complexity. These results support the notion that multimodal strategies, incorporating both textual and visual elements, can be highly advantageous in educational settings by facilitating the comprehension and retention of complex information.

Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the taskspecific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.

In this work, we propose a novel method to incorporate corpus-level discourse information into language modelling. We call this larger-context language model. We introduce a late fusion approach to a recurrent language model based on long short-term memory units (LSTM), which helps the LSTM unit keep intra-sentence dependencies and inter-sentence dependencies separate from each other. Through the evaluation on three corpora (IMDB, BBC, and PennTree Bank), we demonstrate that the proposed model improves perplexity significantly. In the experiments, we evaluate the proposed approach while varying the number of context sentences and observe that the proposed late fusion is superior to the usual way of incorporating additional inputs to the LSTM. By analyzing the trained largercontext language model, we discover that content words, including nouns, adjectives and verbs, benefit most from an increasing number of context sentences. This analysis suggests that larger-context language model improves the unconditional language model by capturing the theme of a document better and more easily.

Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate.

We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method discovers dialogue acts by clustering raw utterances. Because it accounts for the sequential behaviour of these acts, the learned model can provide insight into the shape of communication in a new medium. We address the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. This work is inspired by a corpus of 1.3 million Twitter conversations, which will be made publicly available. This huge amount of data, available only because Twitter blurs the line between chatting and publishing, highlights the need to be able to adapt quickly to a new medium.

As a fundamental task in the natural language processing field, language modeling aims to estimate the distribution of the word sequences. However, the most existing algorithms have focused on the main texts while often ignoring the vastly-accessible semantic attributes of the documents, e.g., titles, authors, sentiments and tags. To address this issue, we propose Semantic Attribute Modulated (SAM) language modeling, a novel language modeling framework that incorporates the various semantic attributes. Attributes are selected automatically with an attribute attention mechanism. We build three text datasets with a diversity of semantic attributes. On the three text datasets, we empirically examine the language model perplexities of several typical corpora, and then demonstrate the superiority of our model with the different combinations of the attributes. Extensive qualitative results, including word semantic analysis, attention values and an interesting lyric generation, further demonstrate the effectiveness of our SAMmethod.

Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years. Attention has improved image classification, image captioning, speech recognition, generative models, and learning algorithmic tasks, but it had probably the largest impact on neural machine translation. Recently, similar improvements have been obtained using alternative mechanisms that do not focus on a single part of a memory but operate on all of it in parallel, in a uniform way. Such mechanism, which we call active memory, improved over attention in algorithmic tasks, image processing, and in generative modelling. So far, however, active memory has not improved over attention for most natural language processing tasks, in particular for machine translation. We analyze this shortcoming in this paper and propose an extended model of active memory that matches existing attention models on neural machine translation and generalizes better to longer sentences. We investigate this model and explain why previous active memory models did not succeed. Finally, we discuss when active memory brings most benefits and where attention can be a better choice.

We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-toend, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively1.

The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.

Recently, a number of authors have proposed treating dialog ue systems as Markov decision processes (MDPs). However, the practical applica tion of MDP algorithms to dialogue systems faces a number of severe technical chall enges. We have built a general software tool (RLDS, for Reinforcement Learning fo r Dialogue Systems) based on the MDP framework, and have applied it to dialogue co rpora gathered from two dialogue systems built at AT&T Labs. Our experiment s demonstrate that RLDS holds promise as a tool for “browsing” and understandin g correlations in complex, temporally dependent dialogue corpora.


Question: Not every conversation is the same: Instance weighting for neural conversational systems.

This paper introduces the Neural Responding Machine (NRM), a cutting-edge response generation model designed for short-text conversations. NRM adopts the conventional encoder-decoder framework, employing recurrent neural networks (RNN) for both encoding and decoding processes. The model decodes responses based on the latent representation of the input text, enabling the generation of contextually relevant and grammatically accurate replies. Trained on a substantial dataset of one-round conversations sourced from a microblogging platform, NRM has been shown to produce suitable responses to over 75% of input texts. This performance surpasses that of current state-of-the-art models in the same domain, including retrieval-based and statistical machine translation (SMT)-based systems, highlighting NRM's effectiveness in generating high-quality conversational responses.

An interactive Question Answering (QA) system frequently encounters non-sentential (incomplete) questions. These non-sentential questions may not make sense to the system when a user asks them without the context of conversation. The system thus needs to take into account the conversation context to process the incomplete question. In this work, we present a recurrent neural network (RNN) based encoder decoder network that can generate a complete (intended) question, given an incomplete question and conversation context. RNN encoder decoder networks have been show to work well when trained on a parallel corpus with millions of sentences, however it is extremely hard to obtain conversation data of this magnitude. We therefore propose to decompose the original problem into two separate simplified problems where each problem focuses on an abstraction. Specifically, we train a semantic sequence model to learn semantic patterns, and a syntactic sequence model to learn linguistic patterns. We further combine syntactic and semantic sequence models to generate an ensemble model. Our model achieves a BLEU score of 30.15 as compared to 18.54 using a standard RNN encoder decoder model.

Dialogue systems are often regarded as being tedious and inflexible. We believe that one reason is rigid and inadaptable system utterances. A good dialogue system should automatically choose a formulation that reflects the user’s expectations. However, current dialogue system development environments only allow the definition of questions with unchangeable formulations. In this paper we present a new approach to the generation of system questions by only defining basic concepts. This is the basis for realising adaptive, user-tailored, and human-like system questions in dialogue systems.

Neural language models learn word representations that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models. We show that translation-based embeddings outperform those learned by cutting-edge monolingual models at single-language tasks requiring knowledge of conceptual similarity and/or syntactic role. The findings suggest that, while monolingual models learn information about how concepts are related, neural-translation models better capture their true ontological status. It is well known that word representations can be learned from the distributional patterns in corpora. Originally, such representations were constructed by counting word co-occurrences, so that the features in one word’s representation corresponded to other words [11, 17]. Neural language models, an alternative means to learn word representations, use language data to optimise (latent) features with respect to a language modelling objective. The objective can be to predict either the next word given the initial words of a sentence [4, 14, 8], or simply a nearby word given a single cue word [13, 15]. The representations learned by neural models (sometimes called embeddings) generally outperform those acquired by co-occurrence counting models when applied to NLP tasks [3]. Despite these clear results, it is not well understood how the architecture of neural models affects the information encoded in their embeddings. Here, we explore this question by considering the embeddings learned by architectures with a very different objective function to monolingual language models: neural machine translation models. We show that translation-based embeddings outperform monolingual embeddings on two types of task: those that require knowledge of conceptual similarity (rather than simply association or relatedness), and those that require knowledge of syntactic role. We discuss what the findings indicate about the information content of different embeddings, and suggest how this content might emerge as a consequence of the translation objective. 1 Learning embeddings from language data Both neural language models and translation models learn real-valued embeddings (of specified dimension) for words in some pre-specified vocabulary, V , covering many or all words in their training corpus. At each training step, a ‘score’ for the current training example (or batch) is computed based on the embeddings in their current state. This score is compared to the model’s objective function, and the error is backpropagated to update both the model weights (affecting how the score is computed from the embeddings) and the embedding features. At the end of this process, the embeddings should encode information that enables the model to optimally satisfy its objective. 1.1 Monolingual models In the original neural language model [4] and subsequent variants [8], each training example consists of n subsequent words, of which the model is trained to predict the n-th word given the first n − 1 ar X iv :1 41 0. 07 18 v2 [ cs .C L ] 1 3 N ov 2 01 4 1 words. The model first represents the input as an ordered sequence of embeddings, which it transforms into a single fixed length ‘hidden’ representation by, e.g., concatenation and non-linear projection. Based on this representation, a probability distribution is computed over the vocabulary, from which the model can sample a guess at the next word. The model weights and embeddings are updated to maximise the probability of correct guesses for all sentences in the training corpus. More recent work has shown that high quality word embeddings can be learned via models with no nonlinear hidden layer [13, 15]. Given a single word in the corpus, these models simply predict which other words will occur nearby. For each word w in V , a list of training cases (w, c) : c ∈ V is extracted from the training corpus. For instance, in the skipgram approach [13], for each ‘cue word’ w the ‘context words’ c are sampled from windows either side of tokens of w in the corpus (with c more likely to be sampled if it occurs closer to w).1 For each w in V , the model initialises both a cue-embedding, representing the w when it occurs as a cue-word, and a context-embedding, used when w occurs as a context-word. For a cue word w, the model can use the corresponding cueembedding and all context-embeddings to compute a probability distribution over V that reflects the probability of a word occurring in the context of w. When a training example (w, c) is observed, the model updates both the cue-word embedding of w and the context-word embeddings in order to increase the conditional probability of c. 1.2 Translation-based embeddings Neural translation models generate an appropriate sentence in their target language St given a sentence Ss in their source language [see, e.g., 16, 6]. In doing so, they learn distinct sets of embeddings for the vocabularies Vs and Vt in the source and target languages respectively. Observing a training case (Ss, St), such a model represents Ss as an ordered sequence of embeddings of words from Vs. The sequence for Ss is then encoded into a single representation RS .2 Finally, by referencing the embeddings in Vt, RS and a representation of what has been generated thus far, the model decodes a sentence in the target language word by word. If at any stage the decoded word does not match the corresponding word in the training target St, the error is recorded. The weights and embeddings in the model, which together parameterise the encoding and decoding process, are updated based on the accumulated error once the sentence decoding is complete. Although neural translation models can differ in low-level architecture [7, 2], the translation objective exerts similar pressure on the embeddings in all cases. The source language embeddings must be such that the model can combine them to form single representations for ordered sequences of multiple words (which in turn must enable the decoding process). The target language embeddings must facilitate the process of decoding these representations into correct target-language sentences. 2 Comparing Mono-lingual and Translation-based Embeddings To learn translation-based embeddings, we trained both the RNN encoder-decoder [RNNenc, 7] and the RNN Search architectures [2] on a 300m word corpus of English-French sentence pairs. We conducted all experiments with the resulting (English) source embeddings from these models. For comparison, we trained a monolingual skipgram model [13] and its Glove variant [15] for the same number of epochs on the English half of the bilingual corpus. We also extracted embeddings from a full-sentence language model [CW, 8] trained for several months on a larger 1bn word corpus. As in previous studies [1, 5, 3], we evaluate embeddings by calculating pairwise (cosine) distances and correlating these distances with (gold-standard) human judgements. Table 1 shows the correlations of different model embeddings with three such gold-standard resources, WordSim-353 [1], MEN [5] and SimLex-999 [10]. Interestingly, translation embeddings perform best on SimLex-999, while the two sets of monolingual embeddings perform better on modelling the MEN and WordSim353. To interpret these results, it should be noted that SimLex-999 evaluation quantifies conceptual similarity (dog wolf ), whereas MEN and WordSim-353 (despite its name) quantify more general relatedness (dog collar) [10]. The results seem to indicate that translation-based embeddings better capture similarity, while monolingual embeddings better capture relatedness. 1 Subsequent variants use different algorithms for selecting the (w, c) from the training corpus [9, 12] Alternatively, subsequences (phrases) of Ss may be encoded at this stage in place of the whole sentence [2].

We propose the “advanced” n-grams as a new technique for simulating user behaviour in spoken dialogue systems, and we compare it with two methods used in our prior work, i.e. linear feature combination and “normal” n-grams. All methods operate on the intention level and can incorporate speech recognition and understanding errors. In the linear feature combination model user actions (lists of〈 speech act, task 〉 pairs) are selected, based on features of the current dialogue state which encodes the whole history of the dialogue. The user simulation based on “normal” n-grams treats a dialogue as a sequence of lists of 〈 speech act, task 〉 pairs. Here the length of the history considered is restricted by the order of the n-gram. The “advanced” n-grams are a variation of the normal ngrams, where user actions are conditioned not only on speech acts and tasks but also on the current status of the tasks, i.e. whether the information needed by the application (in our case flight booking) has been provided and confirmed by the user. This captures elements of goal-directed user behaviour. All models were trained and evaluated on the C OMMUNICATOR corpus, to which we added annotations for user actions and dialogue context. We then evaluate how closely the synthetic responses resemble the real user responses by comparing the user response generated by each user simulation model in a given dialogue context (taken from the annotated corpus) with the actual user response. We propose the expected accuracy, expected precision, and expected recall evaluation metrics as opposed to standard precision and recall used in prior work. We also discuss why they are more appropriate metrics for evaluating user simulation models compared to their standard counterparts. The advanced n-grams produce higher scores than the normal n-grams for small values of n, which proves their strength when little amount of data is available to train larger ngrams. The linear model produces the best expected accuracy but with respect to expected precision and expected recall it is outperformed by the large n-grams even though it is trained using more information. As a task-based evaluation, we also run each of the user simulation models against a system policy trained on the same corpus. Here the linear feature combination model outperforms the other methods and the advanced n-grams outperform the normal ngrams for all values of n, which again shows their potential. We also calculate the perplexity of the different user models.

Cognitive science researchers have emphasized the importance of ordering a complex task into a sequence of easy to hard problems. Such an ordering provides an easier path to learning and increases the speed of acquisition of the task compared to conventional learning. Recent works in machine learning have explored a curriculum learning approach called selfpaced learning which orders data samples on the easiness scale so that easy samples can be introduced to the learning algorithm first and harder samples can be introduced successively. We introduce a number of heuristics that improve upon selfpaced learning. Then, we argue that incorporating easy, yet, a diverse set of samples can further improve learning. We compare these curriculum learning proposals in the context of four non-convex models for QA and show that they lead to real improvements in each of them.

We present a novel technique for segmenting chat conversations using the information bottleneck method (Tishby et al., 2000), augmented with sequential continuity constraints. Furthermore, we utilize critical non-textual clues such as time between two consecutive posts and people mentions within the posts. To ascertain the effectiveness of the proposed method, we have collected data from public Slack conversations and Fresco, a proprietary platform deployed inside our organization. Experiments demonstrate that the proposed method yields an absolute (relative) improvement of as high as 3.23% (11.25%). To facilitate future research, we are releasing manual annotations for segmentation on public Slack conversations.

A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring its performance close to the state-of-the-art and experimentally analyze their contribution: including the ASR confidence scores, abstracting scarcely represented values, including transcriptions in the training data, and model averaging.

Recently Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) acoustic models have demonstrated superior performance over deep neural networks (DNN) models in speech recognition and many other tasks. Although a lot of work have been reported on DNN model adaptation, very little has been done on LSTM model adaptation. In this paper we present our extensive studies of speaker adaptation of LSTM-RNN models for speech recognition. We investigated different adaptation methods combined with KL-divergence based regularization, where and which network component to adapt, supervised versus unsupervised adaptation and asymptotic analysis. We made a few distinct and important observations. In a large vocabulary speech recognition task, by adapting only 2.5% of the LSTM model parameters using 50 utterances per speaker, we obtained 12.6% WERR on the dev set and 9.1% WERR on the evaluation set over a strong LSTM baseline model.

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights. Considering that slot and intent have the strong relationship, this paper proposes a slot gate that focuses on learning the relationship between intent and slot attention vectors in order to obtain better semantic frame results by the global optimization. The experiments show that our proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively1.

RNN language models have achieved state-ofthe-art results on various tasks, but what exactly they are representing about syntax is as yet unclear. Here we investigate whether RNN language models learn humanlike word order preferences in syntactic alternations. We collect language model surprisal scores for controlled sentence stimuli exhibiting major syntactic alternations in English: heavy NP shift, particle shift, the dative alternation, and the genitive alternation. We show that RNN language models reproduce human preferences in these alternations based on NP length, animacy, and definiteness. We collect human acceptability ratings for our stimuli, in the first acceptability judgment experiment directly manipulating the predictors of syntactic alternations. We show that the RNNs’ performance is similar to the human acceptability ratings and is not matched by an n-gram baseline model. Our results show that RNNs learn the abstract features of weight, animacy, and definiteness which underlie soft constraints on syntactic alternations. The best-performing models for many natural language processing tasks in recent years have been recurrent neural networks (RNNs) (Elman, 1990; Sutskever et al., 2014; Goldberg, 2017), but the black-box nature of these models makes it hard to know exactly what generalizations they have learned about their linguistic input: Have they learned generalizations stated over hierarchical structures, or only dependencies among relatively local groups of words (Linzen et al., 2016; Gulordava et al., 2018; Futrell et al., 2018)? Do they represent structures analogous to syntactic dependency trees (Williams et al., 2018), and can they represent complex relationships such as filler–gap dependencies (Chowdhury and Zamparelli, 2018; Wilcox et al., 2018)? In order to make progress with RNNs, it is crucial to determine what RNNs actually learn given currently standard practices; then we can design network architectures, objective functions, and training practices to build on strengths and alleviate weaknesses (Linzen, 2018). In this work, we investigate whether RNNs trained on a language modeling objective learn certain syntactic preferences exhibited by humans, especially those involving word order. We draw on a rich literature from quantitative linguistics that has investigated these preferences in corpora and experiments (e.g., McDonald et al., 1993; Stallings et al., 1998; Bresnan et al., 2007; Rosenbach, 2008). Word order preferences are a key aspect of human linguistic knowledge. In many cases, they can be captured using local co-occurrence statistics: for example, the preference for subject–verb– object word order in English can often be captured directly in short word strings, as in the dramatic preference for I ate apples over I apples ate. However, some word order preferences are more abstract and can only be stated in terms of higherorder linguistic units and abstract features. For example, humans exhibit a general preference for word orders in which words linked in syntactic dependencies are close to each other: such sentences are produced more frequently and comprehended more easily (Hawkins, 1994; Futrell et al., 2015; Temperley and Gildea, 2018). We are interested in whether RNNs learn abstract word order preferences as a way of probing their syntactic knowledge. If RNNs exhibit these preferences for appropriately controlled stimuli, then on some level they have learned the abstractions required to state them. Knowing whether RNNs show human-like word order preferences also bears on their suitability as language generation systems. White and Rajkumar (2012) have shown that language gener-


Question: Not every conversation is the same: using instance weighting for neural conversational systems.

Introducing OpenSubtitles2016: A Comprehensive Resource for Parallel Corpora from Film and Television Subtitles

This paper unveils a significant update to the OpenSubtitles collection, a vast resource of parallel corpora. Compiled from an extensive database of movie and television subtitles, this latest release comprises 1689 parallel text datasets, encompassing a staggering 2.6 billion sentences across 60 languages. Notably, the release features several improvements in the preprocessing and alignment of subtitle data. These enhancements include automated correction mechanisms for optical character recognition (OCR) errors and the utilization of meta-data to evaluate the quality of subtitles, enabling the assignment of quality scores to subtitle pairs. These refinements aim to enhance the overall accuracy and usability of the OpenSubtitles2016 resource, making it an invaluable tool for researchers and practitioners in the field of natural language processing and machine translation.

Machine translation systems based on deep neural networks are expensive to train. Curriculum learning aims to address this issue by choosing the order in which samples are presented during training to help train better models faster. We adopt a probabilistic view of curriculum learning, which lets us flexibly evaluate the impact of curricula design, and perform an extensive exploration on a German-English translation task. Results show that it is possible to improve convergence time at no loss in translation quality. However, results are highly sensitive to the choice of sample difficulty criteria, curriculum schedule and other hyperparameters.

We present an approach to interactivepredictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations. Secondly, human effort is further reduced by using the entropy of word predictions as uncertainty criterion to trigger feedback requests. Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly. We show in simulation experiments that reward signals on partial translations significantly improve character F-score and BLEU compared to feedback on full translations only, while human effort can be reduced to an average number of 5 feedback requests for every input.

Search-oriented conversational systems rely on information needs expressed in natural language (NL). We focus here on the understanding of NL expressions for building keywordbased queries. We propose a reinforcementlearning-driven translation model framework able to 1) learn the translation from NL expressions to queries in a supervised way, and, 2) to overcome the lack of large-scale dataset by framing the translation model as a word selection approach and injecting relevance feedback as a reward in the learning process. Experiments are carried out on two TREC datasets. We outline the effectiveness of our approach.

Automatic question generation (QG) is a useful yet challenging task in NLP. Recent neural network-based approaches represent the state-of-the-art in this task, but they are not without shortcomings. Firstly, these models lack the ability to handle rare words and the word repetition problem. Moreover, all previous works optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation measure). In this paper, we present a novel deep reinforcement learning based framework for automatic question generation. The generator of the framework is a sequence-to-sequence model, enhanced with the copy mechanism to handle the rare-words problem and the coverage mechanism to solve the word repetition problem. The evaluator model of the framework evaluates and assigns a reward to each predicted question. The overall model is trained by learning the parameters of the generator network which maximizes the reward. Our framework allows us to directly optimize any task-specific score including evaluation measures such as BLEU, GLEU, ROUGE-L, etc., suitable for sequence to sequence tasks such as QG. Our comprehensive evaluation shows that our approach significantly outperforms state-of-the-art systems on the widely-used SQuAD benchmark in both automatic and human evaluation.

Statistical Spoken Dialogue Systems have been around for many years. However, access to these systems has always been difficult as there is still no publicly available end-to-end system implementation. To alleviate this, we present PyDial, an opensource end-to-end statistical spoken dialogue system toolkit which provides implementations of statistical approaches for all dialogue system modules. Moreover, it has been extended to provide multidomain conversational functionality. It offers easy configuration, easy extensibility, and domain-independent implementations of the respective dialogue system modules. The toolkit is available for download under the Apache 2.0 license.

Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora. We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models. A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the taskspecific inputs. Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach. Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.

We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.

The purpose of this paper is to serve as a reference guide for the development of chatterbots implemented with the AIML language. In order to achieve this, the main concepts in Pattern Recognition area are described because the AIML uses such theoretical framework in their syntactic and semantic structures. After that, AIML language is described and each AIML command/tag is followed by an application example. Also, the usage of AIML embedded tags for the handling of sequence dialogue limitations between humans and machines is shown. Finally, computer systems that assist in the design of chatterbots with the AIML language are classified and described.

Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semistructured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.

A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.


Question: Design, creation, and evaluation of an intelligent lighting system.

This document explores the intricacies of designing and tuning a Proportional-Integral-Derivative (PID) controller, a task that appears straightforward in theory but can be quite complex in practical applications, particularly when aiming to balance multiple, often competing, objectives such as rapid response and high stability. The initial design phase often necessitates iterative adjustments through extensive computer simulations to achieve or compromise on the desired closed-loop system performance. This challenge has spurred the development of sophisticated tools to aid engineers in attaining optimal overall PID control across the entire operating range. This evolution has further prompted the integration of advanced tuning algorithms into PID hardware modules. In light of these advancements, this paper offers a contemporary review of the features and tuning methodologies found in patents, software packages, and commercial hardware solutions. It is evident that a plethora of PID variants have been devised to enhance transient performance, though standardization and modularization of PID control remain desirable yet challenging goals. The incorporation of system identification and "intelligent" strategies in software-based PID systems significantly automates the design and tuning process, achieving a considerable level of utility. This development is expected to facilitate the future creation of "plug-and-play" PID controllers, characterized by broad applicability, ease of setup, and optimal operation, thereby boosting productivity, enhancing quality, and minimizing maintenance needs.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HG

One of the central research challenges in the Internet of Things and Ubiquitous Computing domains is how users can be enabled to “program” their personal and industrial smart environments by combining services that are provided by devices around them. We present a service composition system that enables the goal-driven configuration of smart environments for end users by combining semantic metadata and reasoning with a visual modeling tool. In contrast to process-driven approaches where service mashups are statically defined, we make use of embedded semantic API descriptions to dynamically create mashups that fulfill the user's goal. The main advantage of our system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support users in expressing their goals, we integrated a visual programming tool with our system that allows to model the desired state of a smart environment graphically, thereby hiding the technicalities of the underlying semantics. Possible applications of the presented system include the management of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains.

This paper presents the analysis of the Internet of Things (IoT) approach and its application for the development of embedded monitoring and automatic control systems (EMACS) for technological objects and processes that are included in complex industrial systems. The functional structure and main components of EMACS based on IoT approach are given. The examples of IoT applications in design of specialized EMACS for such complex technical objects as gas turbine engines and floating docks are presented. Considerable attention is given to particular qualities of the functional structures, software and hardware implementation as well as multi-level human-machine interfaces of the EMACS for main process parameters. The developed EMACS provide: (a) high precision control of real-time operating processes of gas turbine engines and floating docks, (b) monitoring and automatic control of current technological parameters with high quality indicators, that leads to significant increasing of energy and economic efficiency of both industrial objects.

How can we innovate smart systems for smart cities, to make data available homogeneously, inexpensively, and flexibly while supporting an array of applications that have yet to exist or be specified?

The article based on embedded database of greenhouse temperature and humidity control system intelligent. Put forward by embedded database system set up in an ideal environment for data greenhouse temperature and humidity control, greenhouse crops in the process of growth under control. To address the growth of greenhouse crops in the process of temperature and humidity controlled environment is not ideal, at the same time improve the efficiency of control and cost-effective system. The article focuses on the structure of the control system, hardware, software design and system control strategy. The control system has a simple hardware structure, cost-effective, easy to use and maintenance, temperature and humidity data, and other advantages of good stability. Key-Words: Embedded database, Temperature and Humidity, Control ; data filtering, Greenhouse,

This paper presents challenges and issues in smart buildings and the Internet of Things (IoT), which we identified in years of research in real buildings. To tackle these challenges, a decentralized service-oriented architecture based on a message-oriented middleware has been implemented for the domain of smart buildings. It uses a network-transparent IoT message bus and provides the means for composing applications from auxiliary services, which facilitate device abstraction, protocol adaption, modularity, and maintainability. We demonstrate the flexibility of our architecture by describing how three distinct applications---privacy-enhancing energy data visualization, automated building energy management, and a generic user interface---can be integrated and operated simultaneously in our real smart building laboratory. We compare the advantages of our architecture to conventional ones and provide a best-practice solution for the Intranet of Things and Energy in smart buildings.

Ubisense has developed a platform for building Smart Space applications. The platform addresses the key requirements for building Smart Spaces: accurate 3D positioning, scalable realtime performance, development and deployment tools. This paper deepens the key requirements and describes how the Ubisense platform components meets them. The demonstration exemplifies the smart space platform by tracking players in a game application. The Ubisense Smart Space Platform Ubisense has developed a platform for building Smart Space applications. Our platform addresses the key requirements for building Smart Spaces: Accurate 3D positioning supports applications that can perceive the physical relationships between people and objects in the environment Scalable real-time performance enables arbitrary numbers of applications, used by arbitrary numbers of people over an arbitrarily-large area Development and deployment tools make it easy to design, implement, and manage Smart Space applications. The demonstration shows a Smart Space containing applications that integrate with external software (a simple game that users control by moving around), and devices (a PTZ camera that keeps users in shot while they are playing the game) Fig. 1 Smart space demonstration setup 1 Ubisense, St Andrews House, 90 St Andrews Road, Chesterton, CB4 1DL, United Kingdom, http://www.ubisense.net

This article presents a methodology based on design thinking and user experience design methods for creating what we call `people-aware' IoT applications, where user needs, not technological opportunities, drive the development. This methodology is divided into 7 steps: discovery, capturing, research, design, prototype, evaluate and refine. The tools used include conventional user experience procedures such as problem identification, group brainstorming, surveys, or interviews, mixed with more IoT-specific design specificities. The results of the methodology include well-described and user-oriented scenarios meeting user's needs and also a complete toolbox to assist the implementation and the testing of abovementioned scenarios in an IoT perspective. The article describes the methodology in detail with the help of a use case conducted in a business environment available for the project that leads to the identification and partial design of concrete people-aware IoT applications in the context of a smart meeting room.

This paper introduces a new class of interactive interfaces that can be moved around to appear on ordinary objects and surfaces anywhere in a space. By dynamically adapting the form, function, and location of an interface to suit the context of the user, such steerable interfaces have the potential to offer radically new and powerful styles of interaction in intelligent pervasive computing spaces. We propose defining characteristics of steerable interfaces and present the first steerable interface system that combines projection, gesture recognition, user tracking, environment modeling and geometric reasoning components within a system architecture. Our work suggests that there is great promise and rich potential for further research on steerable interfaces.

The last few decades witnessed the birth and growth of a new sensibility to transportation efficiency. In particular, the need for efficient and improved people and goods mobility pushed researchers to address the problem of intelligent transportation systems. This paper surveys the most advanced approaches to the (partial) customization of road following task, using on-board systems based on artificial vision. The functionalities of lane detection, obstacle detection and pedestrian detection are described and classified, and their possible application on future road vehicles is discussed.

Precision Agriculture Monitor System (PAMS) is an intelligent system which can monitor the agricultural environments of crops and provides service to farmers. PAMS based on the wireless sensor network (WSN) technique attracts increasing attention in recent years. The purpose of such systems is to improve the outputs of crops by means of managing and monitoring the growth period. This paper presents the design of a WSN for PAMS, shares our real-world experience, and discusses the research and engineering challenges in implementation and deployments.


Question: Design, creation, and evaluation of an intelligent lighting system.

This document presents an innovative approach to the design and tuning of Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers with a focus on transient performance optimization. PID controllers are prevalent in industrial control systems due to their simplicity and the limited number of parameters requiring adjustment. Traditionally, the Ziegler–Nichols method has been the go-to technique for tuning these controllers, relying exclusively on data from the plant's step response. However, this method has limitations, particularly its applicability to systems with monotonic step responses and the tendency to produce a high percent overshoot in the compensated system's step response. To address these issues, we propose a novel tuning methodology for PI and PID controllers that, similar to the Ziegler–Nichols method, utilizes only parameters derived from the plant's step response. This methodology is particularly advantageous for plants with underdamped step responses, offering a systematic way to adjust the controller gain to meet specified transient performance criteria. Moreover, the entire development process of this methodology is grounded in concepts typically taught in a frequency-domain-based control course, thereby enhancing its educational value.

We propose a novel wireless access monitoring and control system based on the digital door lock, which is explosively used as a digital consumer device. Digital door lock is an electronic locking system operated by the combination of digital key, security password or number codes. This paper presents a prototype of the proposed system and shows a scheme for the implementation. To implement the system with ZigBee network protocol, four types of modules are developed, ZigBee module, digital door lock module, human detection module, and ZigBee relay module. ZigBee module is designed to support wireless sensor network and also used for the ZigBee tag to identify the access objects. Digital door lock module is implemented as a digital consumer device to control the access system as well as locking system. It is very convenient system for the consumers and has extensible and flexible characteristics. That is, it can be used as a home security system by the ZigBee network with additional sensor devices. Therefore, it can be a good practical product for the realization of an access monitoring and control system. It also can be applied to the real market for home networking system. Furthermore, the system can be extended to another service such as a connection between mobile phone and home networking system.

Several cooking support systems have been studied to give users instructions based on the recipes stepby-step, using multi-media contents. These systems usually disturb users’ cooking process forcing them to provide information to the system in order to give them beneficial information. In this sense, these systems are considered to be ”system centric”. We propose a system called ”Smart Kitchen” considered to be in ”user centric”, in which a user can cook normally without being concerned about the system. Smart Kitchen can understand cooking processes, in other words, what the user is doing. In this paper, we discuss the design of the Smart Kitchen system and explain three essential modules of tracking food, recognizing food material, and recognizing cooking action.

The main prospective aim of modern research related to Artificial Intelligence is the creation of technical systems that implement the idea of Strong Intelligence. According our point of view the path to the development of such systems comes through the research in the field related to perceptions. Here we formulate the model of the perception of external world which may be used for the description of perceptual activity of intelligent beings. We consider a number of issues related to the development of the set of patterns which will be used by the intelligent system when interacting with environment. The key idea of the presented perception model is the idea of subjective reality. The principle of the relativity of perceived world is formulated. It is shown that this principle is the immediate consequence of the idea of subjective reality. In this paper we show how the methodology of subjective reality may be used for the creation of different types of Strong AI systems.

This paper presents the concepts of social and emotional intelligence as elements of human intelligence that are complementary to the intelligence assessed by the Turing Test. We argue that these elements are gaining importance as human users are increasingly conceptualising machines as social entities. We describe an implementation of Sensitive Artificial Listeners which provides a hands-on example of technology with some emotional and social skills, and discuss first elements of test methodologies for such technology.

An artificial intelligence system, designed for operations in a real-world environment faces a nearly infinite set of possible performance scenarios. Designers and developers, thus, face the challenge of validating proper performance across both foreseen and unforeseen conditions, particularly when the artificial intelligence is controlling a robot that will be operating in close proximity, or may represent a danger, to humans. While the manual creation of test cases allows limited testing (perhaps ensuring that a set of foreseeable conditions trigger an appropriate response), this may be insufficient to fully characterize and validate safe system performance. An approach to validating the performance of an artificial intelligence system using a simple artificial intelligence test case producer (AITCP) is presented. The AITCP allows the creation and simulation of prospective operating scenarios at a rate far exceeding that possible by human testers. Four scenarios for testing an autonomous navigation control system are presented: single actor in two-dimensional space, multiple actors in two-dimensional space, single actor in three-dimensional space, and multiple actors in three-dimensional space. The utility of using the AITCP is compared to that of human testers in each of these scenarios.

The most prevailing approach now for parking lot vacancy detecting system is to use sensor-based techniques. The main impediments to the camera-based system in applying to parking lots on rooftop and outside building are the glaring sun light and dark shadows in the daytime, and low-light intensity and back-lighting in the nighttime. To date, no camera-based detecting systems for outdoor parking lots have been in practical use. A few engineering firms provide the camera-based system, which is only for underground and indoor parking lots. This paper reports on the new camera based system called ParkLotD for detecting vacancy/occupancy in parking lots. ParkLotD uses a classifier based on fuzzy c-means (FCM) clustering and hyper-parameter tuning by particle swarm optimization (PSO). The test result of the detection error rate for the indoor multi-story parking lot has improved by an order of magnitude compared to the current system based on the edge detection approach. ParkLotD demonstrates high detection performance and enables the camera-based system to achieve the practical use in outdoor parking lots.

Internet Of Things (IoT)is a shared network of objects or things which can interact with each other provided the Internet connection. IoT plays an important role in agriculture industry which can feed 9.6 billion people on the Earth by 2050. Smart Agriculture helps to reduce wastage, effective usage of fertilizer and thereby increase the crop yield. In this work, a system is developed to monitor crop-field using sensors (soil moisture, temperature, humidity, Light) and automate the irrigation system. The data from sensors are sent to Web server database using wireless transmission. In server database the data are encoded in JSON format. The irrigation is automated if the moisture and temperature of the field falls below the brink. In greenhouses light intensity control can also be automated in addition to irrigation. The notifications are sent to farmers' mobile periodically. The farmers' can able to monitor the field conditions from anywhere. This system will be more useful in areas where water is in scarce. This system is 92% more efficient than the conventional approach.

Intelligent appliances with multimedia capability have been emerging into our daily life, thanks to the fast advance of computing technology and the wide use of the Interne. Smart home is one of the most prominent areas of intelligent appliances. Kitchen is one of the places where such intelligent appliances have been used. Since modern life style is driving people spending less time on cooking healthy food at home, an enjoyable and healthy life style can be assisted with an intelligent kitchenware such as a smart fridge. In this paper we introduce a novel idea of the smart fridge which can enable better nutrition and enhance health. It is designed for managing items stored in it and advising its users with cooking methods depending on what kind of food is stored. More importantly, it can perform other actions such as dietary control, nutrition monitoring, eating habit analysis, etc. The characteristics, functions and design details of the smart fridge are presented in details. We are confident that such a smart fridge will be an important component in future smart home.

Despite the availability of high-quality graphics systems, architects and designers still build scaled physical models of buildings and products. These physical models have many advantages, however they are typically static in structure and surface characteristics. They are inherently lifeless. In contrast, high-quality graphics systems are tremendously flexible, allowing viewers to see alternative structures, facades, textures, cut-away views, and even dynamic effects such as changing lighting, moving automobiles, people, etc. We introduce a combination of these approaches that builds on our previously-published projector-based Spatially-Augmented Reality techniques. The basic idea is to aim multiple ceiling-mounted light projectors inward to graphically augment table-top scaled physical models of buildings or products. This approach promises to provide very compelling hybrid visualizations that afford the benefits of both traditional physical models, and modern computer graphics, effectively "bringing to life" table-top

This paper explores the problem of designing enclosures (or physical cases) that are needed for prototyping electronic devices. We present a novel interface that uses electronic components as handles for designing the 3D shape of the enclosure. We use the .NET Gadgeteer platform as a case study of this problem, and implemented a proof-of-concept system for designing enclosures for Gadgeteer components. We show examples of enclosures designed and fabricated with our system.


Question: Design, creation, and evaluation of an intelligent lighting system.

This document presents an innovative approach to home automation through the integration of a ZigBee-based system and a Wi-Fi network, facilitated by a home gateway. The objective is to enhance connectivity among household devices, enabling home automation and potentially allowing remote control and monitoring over the internet. Despite the growing presence of network-enabled technology in homes, the uptake of home automation systems has been sluggish. This paper explores the factors behind this slow adoption and assesses the capability of ZigBee technology to overcome these issues by proposing a flexible home automation framework. The integration of the ZigBee system and Wi-Fi network is achieved through a home gateway, which ensures compatibility between different networks, offers a user-friendly interface, and supports remote access to the home automation system. For enhanced security and safety, a dedicated virtual home environment is created. To validate the practicality and efficacy of the proposed solution, several devices have been developed and tested within the home automation system, including a smart light switch, a radiator valve, a safety sensor, and a ZigBee remote control. These components illustrate the system's potential for controlling and monitoring various aspects of the home environment.

The squeeze of an arm during a tense moment in a story -- an absentminded caress on the back of the hand while listening to an engaging tale -- the physical presence and interpersonal touch of a loved one can be an important part of reading to a child. However the thrill of the tale is often lost and the intimacy diluted when separated families have to resort to the flatness of a video-call or mobile app to share a bedtime story with their loved one. This project is setting out to create a physical story portal, a magical object which uses technologies to link teller and listener through sound, light and touch to help bridge this gap when families are separated. While the development of the InTouch reading system is still in the preliminary stages, a prototype handheld device has been created to allow adults and children to share touch-based messages during story time. It has been trialled with several families in the lab, and the initial results have pointed to both guidance and design considerations that should be taken into account for such a system to be successfully deployed.

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities.

For decades, researchers have debated the pros and cons of adaptive user interfaces with enthusiastic AI practitioners often confronting skeptical HCI experts (Shneiderman & Maes, 1997). This paper summarizes the SUPPLE project’s six years of work analyzing the characteristics of successful adaptive interfaces and developing efficient algorithms for their automatic generation (Gajos & Weld, 2004; Gajos et al., 2005; Gajos & Weld, 2005; Gajos et al., 2006; Gajos, Wobbrock, & Weld, 2007; Gajos et al., 2008; Gajos, Wobbrock, & Weld, 2008). We conclude that personalized user interfaces, which are adapted to a person’s devices, tasks, preferences and abilities, can improve user satisfaction and performance. Further, we demonstrate that automatic generation of these interfaces is computationally feasible. We developed two systems which autonomously construct a broad range of personalized adaptive interfaces: 1) SUPPLE/ARNAULD uses decision-theoretic optimization to automatically generate user interfaces adapted to target device, usage patterns, and a learned model of user preferences, and 2) SUPPLE++ first performs a one-time assessment of a person’s motor abilities and then automatically generates user interfaces adapted to that user’s abilities. Our experiments show that these automatically generated interfaces significantly improve speed, accuracy and satisfaction of users with motor impairments compared to manufacturers’ defaults. We also provide the first characterization of the design space of adaptive interfaces and demonstrate how such interfaces can significantly improve the quality and efficiency of daily interactions for typical users.

The IEEE Standards Working Group, IEEE PAR1789 "Recommending practices for modulating current in High Brightness LEDs for mitigating health risks to viewers" has been formed to advise the lighting industry and standards groups about the emerging concern of flicker in LED lighting. This paper intends to introduce new measures and definitions of lamp flicker in lighting. The discussion represents on-going work in IEEE PAR1789 that is vital to designing safe LED lamp drivers.

There has been inconvenience in controlling each digital home appliance which requires its own remote controller. In this paper, we present an advanced universal remote controller (URC) with the total solution for home automation and security. All kinds of home appliances can be controlled with the URC, which can be also connected to a PC dealing with Internet as well. To use the URC, we need several receivers with wired or wireless communication methods to be connected to all appliances. The receivers have many channels and IDs to control many appliances at the same time and to support multi-zone services. In addition, we propose a PC-based interface for end-users to use the URC conveniently. With the proposed URC, we can easily construct a ubiquitous home automation and security environment with the total solution. Furthermore, this solution can be applied to the automated control of all kinds of appliances installed within buildings for companies, schools, hospitals, and so on.

—This paper presents design and implementation concepts for a wireless real-time home automation system based on Arduino Uno microcontroller as central controllers. The proposed system has two operational modes. The first one is denoted as a manually–automated mode in which the user can monitor and control the home appliances from anywhere over the world using the cellular phone through Wi-Fi communication technology. The second one is referred to a self-automated mode that makes the controllers to be capable of monitoring and controlling different appliances in the home automatically in response to the signals comes from the related sensors. To support the usefulness of the proposed technique, a hardware implementation with Matlab-GUI platform for the proposed system is carried out and the reliability of the system is introduced. The proposed system is shown to be a simple, cost effective and flexible that making it a suitable and a good candidate for the smart home future. I. INTRODUCTION ecently, man's work and life are increasingly tight with the rapid growth in communications and information technology. The informationized society has changed human being's way of life as well as challenged the traditional residence. Followed by the rapid economic expansion, living standard keeps raising up day by day that people have a higher requirement for dwelling functions. The intellectualized society brings diversified information where safe, economic, comfortable and convenient life has become the ideal for every modern family [1].

How can designers of programming interfaces, interactive tools, and rich social environments enable more people to be more creative more often?

Mint is an automatic cleaning robot that sweeps and mops hard-surface floors using dusting and mopping cloths. Thanks to the Northstar navigation technology it systematically cleans and navigates in people's homes. Since it first became commercially available in mid 2010, hundreds of thousands of Mint cleaners are nowadays in use at home. In this paper we investigate the product's social impact with respect to the attitude of customers towards a systematic floor cleaner and how such a robot influences their lifestyle. We first report feedback from users owning the product, and demonstrate how Mint changed their everyday life at home. We then evaluate the results of a survey launched in 2012 that addresses the technical understanding of the product and what impact it has on the social life of their users. Our findings suggest that Mint, on average, saves more than one hour of people's time per week, floors are cleaner leading to healthier homes and lives, systematic cleaning is seen as an important feature, and modifications to the environment to support the navigation of the robot are largely accepted.

In this paper, a user activities outlier detection system is introduced. The proposed system is implemented in a smart home environment equipped with appropriate sensory devices. An activity outlier detection system consist of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based System (FRBS). In the first stage, the Hamming distance is used to measure the distances between the activities. PCA is then applied to the distance measures to find two indices of Hotelling's T2 and Squared Prediction Error (SPE). In the second stage of the process, the calculated indices are provided as inputs to FRBSs to model them heuristically. They are used to identify the outliers and classify them. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers and helps in distinguishing between the normal and abnormal behaviour patterns of the Activities of Daily Living (ADL).

This paper aims to present an intelligent system for parking space detection based on image processing technique. The proposed system captures and processes the rounded image drawn at parking lot and produces the information of the empty car parking spaces. In this work, a camera is used as a sensor to take photos to show the occupancy of car parks. The reason why a camera is used is because with an image it can detect the presence of many cars at once. Also, the camera can be easily moved to detect different car parking lots. By having this image, the particular car parks vacant can be known and then the processed information was used to guide a driver to an available car park rather than wasting time to find one. The proposed system has been developed in both software and hardware platform. An automatic parking system is used to make the whole process of parking cars more efficient and less complex for both drivers and administrators.