Reviewerly/exHarmony huggingface dataset:
Relevant and Informative Response Generation using Pointwise Mutual Information

A sequence-to-sequence model tends to generate generic responses with little information for input utterances.To solve this problem, we propose a neural model that generates relevant and informative responses.Our model has simple architecture to enable easy application to existing neural dialogue models.Specifically, using positive pointwise mutual information, it first identifies keywords that frequently co-occur in responses given an utterance.Then, the model encourages the decoder to use the keywords for response generation.Experiment results demonstrate that our model successfully diversifies responses relative to previous models.
The Detection Performance of the Dual-Sequence-Frequency-Hopping Signal via Stochastic Resonance Processing under Color Noise

Can the Dual-Sequence-Frequency-Hopping (DSFH) as a military emergency communication mode work under strong color noise?And is there any detection improvement of the DSFH signal via stochastic resonance (SR) processing under color noise?To deal with this problem, we analyze the physical feature of the DSFH signal.Firstly, the signal models of transmission, reception and the intermediate frequency (IF) are constructed.And the scale transaction is used to adjust the IF signal to fit the SR.Secondly, the non-markovian Langevin Equation (LE) is transformed into a markovian one by expand the 1-D LE to a 2-D one.Thirdly, the non-autonomous Fokker-Plank Equation (FPE) is transformed into an autonomous one by assuming that the SR transition of magnetic particles is instantaneous and introducing the decision time.Therefore, the analytical periodic steady solution of the probability density function (PDF) with the parameter of the correlation time of the color noise is obtained.Finally, the detection probability, false alarm probability and Receiver Operating Characteristics (ROC) curve are obtained, under the criterion of the maximum a posterior probability (MAP).Theoretical and simulation results show as below: 1) whether the DSFH can work under strong color noise is decided by the correlation time of the color noise; 2) when the power intensity of the color noise is constant, the smaller the correlation time with the bigger local SNR, the greater PDF difference of the SR output under two hypothesis, leading to better detection performance.
Using Functional Schemas to Understand Social Media Narratives

We propose a novel take on understanding narratives in social media, focusing on learning "functional story schemas", which consist of sets of stereotypical functional structures.We develop an unsupervised pipeline to extract schemas and apply our method to Reddit posts to detect schematic structures that are characteristic of different subreddits.We validate our schemas through human interpretation and evaluate their utility via a text classification task.Our experiments show that extracted schemas capture distinctive structural patterns in different subreddits, improving classification performance of several models by 2.4% on average.We also observe that these schemas serve as lenses that reveal community norms.
Multi-strategy and Dimension Perturbation Ensemble of Artificial Bee Colony

Ensemble learning is a popular machine learning technique, which employs multiple learning methods to obtain better performance than any single constituent method. Recently, ensemble learning was successfully used in several bio-inspired optimization algorithms to achieve good performance. Artificial bee colony (ABC) is an efficient optimization technique inspired by the social behavior of bees. To enhance the optimization ability of ABC, this paper proposes a new ABC variant based on ensemble learning. The proposed approach is called multi-strategy and dimension perturbation ensemble of ABC (MPEABC), in which multiple distinct solution search strategies are used to balance the exploration and exploitation. To accelerate the search, each solution search strategy is assigned an independent probability to control the frequency of dimension perturbation. To avoid manually setting the probability, an adaptive method is used to dynamically adjust its value. Experimental verifications are conducted on a set of well-known benchmark functions. Results show that MPEABC achieves better solutions than the standard ABC, multi-strategy ensemble ABC (MEABC) and several improved ABC variants.
Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning

We present our work on the problem of detection Multiple Admissibility (MA) in language learning.Multiple Admissibility occurs when more than one grammatical form of a word fits syntactically and semantically in a given context.In second-language education-in particular, in intelligent tutoring systems/computeraided language learning (ITS/CALL), systems generate exercises automatically.MA implies that multiple alternative answers are possible.We treat the problem as a grammaticality judgement task.We train a neural network with an objective to label sentences as grammatical or ungrammatical, using a "simulated learner corpus": a dataset with correct text and with artificial errors, generated automatically.While MA occurs commonly in many languages, this paper focuses on learning Russian.We present a detailed classification of the types of constructions in Russian, in which MA is possible, and evaluate the model using a test set built from answers provided by users of the Revita language learning system.
Online aggression from a sociological perspective: An integrative view on determinants and possible countermeasures

The present paper introduces a theoretical model for explaining aggressive online comments from a sociological perspective.It is innovative as it combines individual, situational, and social-structural determinants of online aggression and tries to theoretically derive their interplay.Moreover, the paper suggests an empirical strategy for testing the model.The main contribution will be to match online commenting data with survey data containing rich background data of non-/aggressive online commentators.
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention

This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop.We enhance tweet representation with a language model and distinguish the importance of different words with Multi-Head Self-Attention.In addition, transfer learning is exploited to make up for the data shortage.Our system achieved competitive results on both tasks with an F1-score of 0.5718 for task 1 and 0.653 (overlap) / 0.357 (strict) for task 2.
Cascaded Dense-UNet for Image Super-Resolution

Recently, deep convolutional neural networks (CNNs) have achieved great success in single image super-resolution (SISR). Especially, dense skip connections and residual learning structures promote better performance. While most existing deep CNN-based networks exploit the interpolation of upsampled original images, or do transposed convolution in the reconstruction stage, which do not fully employ the hierarchical features of the networks for final reconstruction. In this paper, we present a novel cascaded Dense-UNet (CDU) structure to take full advantage of all hierarchical features for SISR. In each Dense-UNet block (DUB), many short, dense skip pathways can facilitate the flow of information and integrate the different receptive fields. A series of DUBs are concatenated to acquire high-resolution features and capture complementary contextual information. Upsampling operators are in DUBs. Furthermore, residual learning is introduced to our network, which can fuse shallow features from low resolution (LR) image and deep features from cascaded DUBs to further boost super-resolution (SR) reconstruction results. The proposed method is evaluated quantitatively and qualitatively on four benchmark datasets, our network achieves comparable performance to state-of-the-art super-resolution approaches and obtains pleasant visualization results.
Storage and Allocation of English Teaching Resources Based on k-Nearest Neighbor Algorithm

The boom of Internet technology gives a boost to the informatization of education in China. Internet resources serve as a new carrier of knowledge, offering teachers and students an alternative to books. However, the exponential growth of Internet resources has greatly complicated the storage and allocation of resources. This paper attempts to fully utilize English teaching resources through effective resource management and allocation. Specifically, the features of English teaching resources were analyzed, and then the term frequency-inverse document frequency (TF-IDF) weight method and k-nearest neighbor (kNN) algorithm were improved to make resource allocation more efficient and effective. The improved methods were then verified through a case analysis. The results show that the improved kNN provides a feasible way to allocate English teaching resources. The research findings provide reference to the storage and allocation of teaching resources.
A Computational Model of Early Language Acquisition from Audiovisual Experiences of Young Infants

Earlier research has suggested that human infants might use statistical dependencies between speech and non-linguistic multimodal input to bootstrap their language learning before they know how to segment words from running speech.However, feasibility of this hypothesis in terms of real-world infant experiences has remained unclear.This paper presents a step towards a more realistic test of the multimodal bootstrapping hypothesis by describing a neural network model that can learn word segments and their meanings from referentially ambiguous acoustic input.The model is tested on recordings of real infant-caregiver interactions using utterancelevel labels for concrete visual objects that were attended by the infant when caregiver spoke an utterance containing the name of the object, and using random visual labels for utterances during absence of attention.The results show that beginnings of lexical knowledge may indeed emerge from individually ambiguous learning scenarios.In addition, the hidden layers of the network show gradually increasing selectivity to phonetic categories as a function of layer depth, resembling models trained for phone recognition in a supervised manner.
A Case Study on Meaning Representation for Vietnamese

This paper presents a case study on meaning representation for Vietnamese.Having introduced several existing semantic representation schemes, we select AMR (Abstract Meaning Representation) as a basis for our work on Vietnamese.From it, we define a meaning representation label set by adapting the English schema and taking into account the specific characteristics of Vietnamese.
Complete evolution equation for the joint amplitude in photon-pair generation through spontaneous four-wave mixing

As four-wave-mixing-based photon-pair sources mature, accurate modeling of the photon-pair properties becomes important. Unlike spontaneous parametric down-conversion, four-wave mixing is accompanied by a number of parasitic effects such as nonlinear self- and cross-phase modulation. Currently, most modelings of photon-pair states are analytic in nature, which limits the number and type of effects that can be taken into account. In this work, we derive a complete, dual-pump evolution equation for the joint amplitude of photon pairs, wherein any desired effects can be included. We describe how to efficiently obtain numerical solution to this equation using a split-step approach. Lastly, we cover a few analytical solutions and compare two schemes for pure-photon generation under three different parasitic effects. We show how one scheme is highly sensitive to parasitic effects, while the other is very robust.
$$D^2ehazing$$ : Real-Time Dehazing in Traffic Video Analytics by Fast Dynamic Bilateral Filtering

Traffic videoDas, Apurba analytics is highly dependent on accurate determinationPai, Shashidhar of traffic density in terms of stateShenoy, Vinayak S., position, and velocityVinay, Tanush of each vehicle in each lane. The accuracy of vehicle detection gets impacted by adverse weather conditionShylaja, S. S. like fog/haze and rain. There are many high-accuracy algorithms available for haze detection and removal for still images using bilateral filtering like guided filtering. But unfortunately they are slow and far from real-time dehazing and detection performance in video. This current work has proposed a detailed algorithm and system architecture based on R-CNN and fast bilateral filtering. The proposal of dynamic triggering of detector and dehaze engine has promised to achieve real-time performance in intelligent traffic video analytics in haze environment. The results have demonstrated that the proposed Dynamic Dehazing ( $$D^2ehazing$$ ) algorithm is competitive with existing algorithms in terms of both accuracy and performance.
Adversarial Approximate Inference for Speech to Electroglottograph Conversion

Speech produced by human vocal apparatus conveys substantial non-semantic information including the gender of the speaker, voice quality, affective state, abnormalities in the vocal apparatus etc. Such information is attributed to the properties of the voice source signal, which is usually estimated from the speech signal. However, most of the source estimation techniques depend heavily on the goodness of the model assumptions and are prone to noise. A popular alternative is to indirectly obtain the source information through the Electroglottographic (EGG) signal that measures the electrical admittance around the vocal folds using dedicated hardware. In this paper, we address the problem of estimating the EGG signal directly from the speech signal, devoid of any hardware. Sampling from the intractable conditional distribution of the EGG signal given the speech signal is accomplished through optimization of an evidence lower bound. This is constructed via minimization of the KL-divergence between the true and the approximated posteriors of a latent variable learned using a deep neural auto-encoder that serves an informative prior. We demonstrate the efficacy of the method at generating the EGG signal by conducting several experiments on datasets comprising multiple speakers, voice qualities, noise settings and speech pathologies. The proposed method is evaluated on many benchmark metrics and is found to agree with the gold standard while proving better than the state-of-the-art algorithms on a few tasks such as epoch extraction.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> -uniform mixed states

We investigate the maximum purity that can be achieved by $k$-uniform mixed states of $N$ parties. Such $N$-party states have the property that all their $k$-party reduced states are maximally mixed. A scheme to construct explicitly $k$-uniform states using a set of specific $N$-qubit Pauli matrices is proposed. We provide several different examples of such states and demonstrate that in some cases the state corresponds to a particular orthogonal array. The obtained states, despite being mixed, reveal strong nonclassical properties such as genuine multipartite entanglement or violation of Bell inequalities.
SC-M*: A Multi-Agent Path Planning Algorithm with Soft-Collision Constraint on Allocation of Common Resources

Multi-agent path planning (MAPP) is increasingly being used to address resource allocation problems in highly dynamic, distributed environments that involve autonomous agents. Example domains include surveillance automation, traffic control and others. Most MAPP approaches assume hard collisions, e.g., agents cannot share resources, or co-exist at the same node or edge. This assumption unnecessarily restricts the solution space and does not apply to many real-world scenarios. To mitigate this limitation, this paper introduces a more general class of MAPP problems—MAPP in a soft-collision context. In soft-collision MAPP problems, agents can share resources or co-exist in the same location at the expense of reducing the quality of the solution. Hard constraints can still be modeled by imposing a very high cost for sharing. This paper motivates and defines the soft-collision MAPP problem, and generalizes the widely-used M* MAPP algorithm to support the concept of soft-collisions. Soft-collision M* (SC-M*) extends M* by changing the definition of a collision, so paths with collisions that have a quality penalty below a given threshold are acceptable. For each candidate path, SC-M* keeps track of the reduction in satisfaction level of each agent using a collision score, and it places agents whose collision scores exceed its threshold into a soft-collision set for reducing the score. Our evaluation shows that SC-M* is more flexible and more scalable than M*. It can also handle complex environments that include agents requesting different types of resources. Furthermore, we show the benefits of SC-M* compared with several baseline algorithms in terms of path cost, success rate and run time.
Terrorism Prediction Using Artificial Neural Network

The main purpose of this research is to develop a hybrid computational intelligent algorithm (framework) as a decision support (DS) tool for terrorism phenomenon that has been defeated for years by governments, countries, and different multiple institutions and hence it needs multiple and integrated research from different science disciplines with a hope of being eliminated in the future.The proposed hybrid prediction algorithm based on integrated different Operations Research (OR) and Decision support tools with Data Mining (DM) techniques especially prediction and classification algorithms as well as different directions of modification and improvements in a number of recent and popular metaheuristics inspired algorithms.The proposed system has been developed, implemented, and evaluated according to different set of assessment measures.Through this study, it was found that, the proposed system is capable of predicting the terrorist group (s) responsible of terror attacks on different regions (countries).The findings of this research may serve as an alarm tool to determine the terrorist groups' networks and so minimize the terrorist attacks.
Numerical Simulation Based Targeting of the Magushan Skarn Cu–Mo Deposit, Middle-Lower Yangtze Metallogenic Belt, China

The Magushan Cu–Mo deposit is a skarn deposit within the Nanling–Xuancheng mining district of the Middle-Lower Yangtze River Metallogenic Belt (MLYRMB), China. This study presents the results of a new numerical simulation that models the ore-forming processes that generated the Magushan deposit and enables the identification of unexplored areas that have significant exploration potential under areas covered by thick sedimentary sequences that cannot be easily explored using traditional methods. This study outlines the practical value of numerical simulation in determining the processes that operate during mineral deposit formation and how this knowledge can be used to enhance exploration targeting in areas of known mineralization. Our simulation also links multiple subdisciplines such as heat transfer, pressure, fluid flow, chemical reactions, and material migration. Our simulation allows the modeling of the formation and distribution of garnet, a gangue mineral commonly found within skarn deposits (including within the Magushan deposit). The modeled distribution of garnet matches the distribution of known mineralization as well as delineating areas that may well contain high garnet abundances within and around a concealed intrusion, indicating this area should be considered a prospective target during future mineral exploration. Overall, our study indicates that this type of numerical simulation-based approach to prospectivity modeling is both effective and economical and should be considered an additional tool for future mineral exploration to reduce exploration risks when targeting mineralization in areas with thick and unprospective sedimentary cover sequences.
Local Regression Ranking for Saliency Detection

Saliency detection is an important and challenging research topic due to the variety and complex of the background and saliency regions. In this paper, we present a novel unsupervised saliency detection approach by exploiting a learning-based ranking framework. First, the local linear regression model is adopted to simulate the local manifold structure of every image element, which is approximately linear. Using the background queries from the boundary prior, we construct a unified objective function to globally minimize all the errors of the local models for the whole image element points. The Laplacian matrix is learned via optimizing the unified objective function. Low-level image features as well as high-level semantic information extracted from deep neural networks are used for the Laplacian matrix learning. Based on the learnt Laplacian matrix, the saliency of the image element is measured as the relevance ranking to the background queries. The foreground queries are obtained from the background-based saliency and the relevance ranking to the foreground queries is calculated in the same way as the background-based saliency. Second, we calculate an enhanced similarity matrix by fusing two different-level deep feature metrics through cross diffusion. A propagation algorithm uses this enhanced similarity matrix to better exploit the intrinsic relevance of similar regions and improve the saliency ranking results effectively. Results on four benchmark datasets with pixel-wise accurate labelling demonstrate that the proposed unsupervised method shows better performance compared with the newest state-of-the-art methods and is competitive with deep learning-based methods.
An investigation of pre-service English teachers’ level of technopedagogical content knowledge

The purpose of this study is to identify pre-service English teachers' technopedagogic content knowledge competence levels.The Technological Pedagogical Content Knowledge Model (TPACK) is unique in comparison to other technology-based models in that it incorporates content knowledge as well in addition to technological competences.The participants are 182 pre-service English teachers that study in Istanbul and Sakarya, Turkey.The data was collected via Technological Pedagogical Content Knowledge Scale (TPACK-Deep) that was developed by Kabakçı Yurdakul, Odabaşı, Kılıçer, Çoklar, Birinci and Kurt (2012).The data was analyzed through SPSS and the variables grade, gender, daily amount of time spent on the internet and social media were considered in addition to identifying participants' competence levels.The findings demonstrated that the participants had a high level of TPACK model in general and also in the three dimensions except for merely the Ethics dimension in which they had a medium level of competence.Out of the variables, none of them was observed to have a significant effect in the TPACK model in a general, but gender and daily amount of time spent on the internet were observed to have a significant effect on the Design dimension.The analysis offers insights into pre-service teachers' competence into technology use, content knowledge, pedagogical knowledge and their integration.The findings are expected to contribute to pre-service teacher training and the successful integration of technology into content knowledge for pedagogic purposes.