Reviewerly/exHarmony huggingface dataset:
Event Fusion and Decision Approach Based on Node Reliability in Dam Safety Monitoring

The sensor networks are widely deployed in the large-scale dams to monitor the structural deformation. The deformation of multi-type dam structure results in the transition of the corresponding events states, so it can evaluate the dam safety through the analysis of multi-type events. The dam safety evaluation includes two phases: the events classification in each single region and the features fusion for all regions. In the real applications, individual node's measurement is unreliable due to the network instability. The current approaches do not distinguish the impacts on the results of the event classification among the different nodes, it may result in the classification deviation. With regard to the accurate and real-time decision for operating conditions of the structure, we propose a Sensor Network Decision Framework based on regional division. In the event classification phase, it can improve the accuracy of the event classification via computing the node decision vectors based on the node reliabilities. In the event fusion phase, the attention mechanism is adopted to extract the key features of single regions and the DNN is established to perform global safety evaluation. Experimental results illustrate that SNDF can improve the accuracy by 17% and reduce the decision time by 8.15 seconds compared with the Expert Weighting Method.
TGMCF: A Tree-Guided Multi-Modality Correlation Filter for Visual Tracking

For updating the tracking models, most existing approaches have an assumption that the target changes smoothly over time.Despite their success in some cases, these approaches struggle in dealing with occlusion, illumination changes and abrupt motion which may break the temporal smoothness assumption.To tackle this problem, in this paper we propose a tree-guided visual tracking model based on the multimodality correlation filter which could estimate the target state according to the most reliable information in previous frames.We maintain a representative target state set in a tree model over the whole tracking process.Ideally, the tree model is able to capture all the landmark states of the target, and provides a confident template for the correlation filter.Therefore, we propose an optimal updating strategy to record the most recent stable and representative states for tree updating.By utilizing stable target-states for template training, the multi-modality correlation filter is able to output a more accurate target position than the baseline and the SOTA (state-of-the-art) methods.Tested on the OTB50 (object tracking benchmark) and OTB100 dataset, the proposed TGMCF has demonstrated outstanding performance on several typical tracking difficulties and overall comparative results with the SOTA trackers are obtained on several public tracking benchmarks.
A Key-Phrase Aware End2end Neural Response Generation Model

Previous Seq2Seq models for chitchat assume that each word in the target sequence has direct corresponding relationship with words in the source sequence, and all the target words are equally important. However, it is invalid since sometimes only parts of the response are relevant to the message. For models with the above mentioned assumption, irrelevant response words might have a negative impact on the performance in semantic association modeling that is a core task for open-domain dialogue modeling. In this work, to address the challenge of semantic association modeling, we automatically recognize key-phrases from responses in training data, and then feed this supervision information into an enhanced key-phrase aware seq2seq model for better capability in semantic association modeling. This model consists of an encoder and a two-layer decoder, where the encoder and the first layer sub-decoder is mainly for learning semantic association and the second layer sub-decoder is for responses generation. Experimental results show that this model can effectively utilize the key phrase information for semantic association modeling, and it can significantly outperform baseline models in terms of response appropriateness and informativeness .
Research on Khalkha Dialect Mongolian Speech Recognition Acoustic Model Based on Weight Transfer

Due to the lack of labeled training data, the performance of acoustic models in low-resource speech recognition systems such as Khalkha dialect Mongolian is poor. Transfer Learning can solve the data-sparse problem by learning the source domain (high resource) knowledge to guides the training of the target domain (low resource) model. In this paper, we investigate the modeling method of using different transfer learning ways in the Khalkha dialect Mongolian ASR system. First, the English and Chahar dialect are used as the source domains, and the trained acoustic model on the above source domains are conducted to initialize the Khalkha acoustic model parameter. Furthermore, the different training strategies, the portability of different hidden layers, and the impact of the pre-training model on the transfer model were applied to validate their effectiveness in the Khalkha dialect ASR task. The experimental results show that the optimal acoustic model is chain TDNN based on weight transfer method with Chahar dialect as the source domain. The final WER is 15.67%, which is relatively reduced by 38% compared to the random initialization model.
A Relation Proposal Network for End-to-End Information Extraction

Information extraction is an important task in natural language processing. In this paper, we introduce our solution on NLPCC 2019 shared task 3 Information Extraction which has provided with the largest industry Schema based Knowledge Extraction (SKE) data-set. Our proposed method is an end-to-end framework which first catches the relation hints in raw text with a relation proposal layer, then follows by an entity tagging design which is targeted to decode the corresponding triplet entities with the given relation proposal. Compared with previous works, our method is efficient and can well handle overlapping and multiple triplets in one sentence. With a simple model ensemble, our solution achieves 0.8903 F1-Score on final leaderboard which ranks forth among all participants.
A note on the consistency of the maximum likelihood estimator under multivariate linear cluster-weighted models

This letter illustrates simple assumptions for proving consistency of the maximum likelihood estimator under multivariate Gaussian and Student’s t linear cluster-weighted models, which allow density estimation, clustering and linear regression analysis with continuous random predictors in presence of unobserved heterogeneity.
Characterization of Eye Gaze and Pupil Diameter Measurements from Remote and Mobile Eye-Tracking Devices

Eye-tracking technology allows to capture real-time visual behavior information and to provide insights about cognitive processes and autonomic function, by measuring gaze position and pupillary response to delivered stimuli. Over the recent years, the development of easy-to-use devices led to a large increase in the use of eye-tracking in a broad spectrum of applications, e.g. clinical diagnostics and psychological research. Given the lack of extensive material to characterize the performance of different eye-trackers, especially latest generation devices, the present study aimed at comparing a screen-mounted eye-tracker (remote) and a pair of wearable eye-tracking glasses (mobile). Seventeen healthy subjects were asked to look at a moving target on a screen for 90 s, while point of regard (POR) and pupil diameter (PD) were recorded by the two devices with a sampling rate of 30 Hz. First, data were preprocessed to remove artifacts, then correlation coefficients (for both signals) and magnitude-squared coherence (for PD) were calculated to assess signals agreement in time and frequency domain. POR measurements from remote and mobile devices resulted highly comparable (ρ > 0.75). PD showed lower correlation and major dispersion (ρ > 0.50), besides a higher number of invalid samples from the mobile device with respect to the remote one. Results provided evidence that the two instruments do share the same content at the level of information generally used to characterize subjects behavioral and physiological reactions. Future analysis of additional features and devices with higher sampling frequencies will be planned to further support their clinical use.
Question Generation Based Product Information

With the continuous development of the Internet, the field of e-commerce generates many comments on products. It is of great significance for both merchants and customers to generate product-related questions by utilizing a large amount of review information of products. In order to get rid of the traditional constraints of generating models based on artificial rules and make the question generation more accurate, this paper proposes a question generation model based on product information. Compared with the existing approaches, this model can generate questions more relevant to the products, and more fluent. In particular, the model can not only avoid the problem that the vocabulary exceeds the dictionary, but also extract the vocabulary needed for question generation from the original text and the dictionary. The experimental results show that in the task of generating short text based on comments, compared with the existing neural network model, the effectiveness has been greatly improved.
The F<sub>M</sub>-linear Complexity of M-ary Sidel’nikov Sequences of Period p − 1 = f • M<sup>λ</sup>

The linear complexity is a measure for the unpredictability of a sequence over a finite field. Sequences with good pseudo-random properties and large linear complexity are widely used in the CDMA spread spectrum communication and cryptography. In recent years, many researchers have focused on the linear complexity of cyclotomic sequences such as Sidel'nikov sequence. This paper studies the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> -linear complexity of M-ary Sidel'nikov sequence of period p-1 using the Hasse derivative of its generating function, where M|(p-1). The tth Hasse derivative formulas are generalized in terms of cyclotomic numbers, and then the exact F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> -linear complexities of the ternary Sidel'nikov sequences are determined for p = 2·3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sup> +1(1 ≤ λ ≤ 20). It turns out that all of the linear complexities of the considered sequences are very close to their periods.
Communicating design-related intellectual influence

Prototype-driven design research often involves collecting and analyzing designed artifacts in annotated portfolios and design workbooks. These collections constitute important sources of intellectual influence for researchers, yet communicating this influence presents unique challenges, such as the difficulty of translating the aesthetic, material, or interactive qualities of a designed artifact into written text. Building on discourses of visual thinking and visual imagery in science communication and HCI research, this paper introduces, and elaborates, a novel research communication design concept called "visual references," which combine bibliographic information with photographic images, textual annotations, and diagrammatic annotations in order to communicate design-related intellectual influence.
Mining Frequent Patterns with Temporal Effect: A Case of Accident Path Analysis

Frequent item-set generation is usually applied on large databases to mine item-sets recurring together. The objective of such analysis is to identify patterns in dataset which are of managerial interests. In cases where the dataset is spread over significant period of time, the results can sometime be misleading. The reasons are twofold: (i) pattern existing with high frequency at certain time period and later experiencing decreasing trend will appear in result and (ii) pattern present in recent time periods with increasing trend but not with significantly high frequency will not appear in result. Due to this the results become bias to time effect. In the proposed work, we have included the time effect in data during pattern mining by exponentially weighing the dataset i.e., the more recent the data, higher the weight assigned to it. The method is applied to accident path components from incident records of an integrated steel plant. The results from the proposed work and traditional frequent item-set mining are compared and results are inferred as: (i) the patterns present only in traditional method's result are the accident patterns which are under control and have effective risk control system (RCS), (ii) patterns present in both the results are paths where either RCS is absent or ineffective, and (iii) the patterns present only in proposed method's result will have higher probability to recur in future. The proposed method provides new direction in interpretation of pattern mining from large dataset of long period.
A Voice-Based Information Extraction System

Designing intelligent expert systems capable of answering different human queries is a challenging and emerging area of research. A huge amount of Web resources are available these days and majority of which are in the form of unstructured documents covering articles, corporate reports, online news, medical records, social media communication data, etc. A user in need of certain information has to assess all the relevant documents to obtain the answer of their queries which is a time-consuming and tedious work. Also, sometimes it becomes quite difficult to obtain the exact information from a list of documents quickly as and when required. This work aims to designing an intelligent information extraction system, which accesses the document contents quickly and provides the relevant answers to the user queries in a structured format just like a human expert answers to the questions.
Classification over Clustering: Augmenting Text Representation with Clusters Helps!

Considering that words with different characteristic in the text have different importance for classification, grouping them together separately can strengthen the semantic expression of each part. Thus we propose a new text representation scheme by clustering words according to their latent semantics and composing them together to get a set of cluster vectors, which are then concatenated as the final text representation. Evaluation on five classification benchmarks proves the effectiveness of our method. We further conduct visualization analysis showing statistical clustering results and verifying the validity of our motivation.
Not just users

Open video game development systems provide a useful model for designing an engaging user experience (UX) research project. While UX research has typically framed people simultaneously as research subjects and users of a technology, some work has also problematized each of these categorizations. For instance, UX practitioners have questioned the framing of people as generic users, and participatory design has repositioned participants as co-owners of the results of research. This article offers a complimentary perspective by applying the concept of user roles to the activity of participation in open development. Open development, which is the prolonged process where incomplete games are publicly released and iterated on based on player feedback, is fundamentally a UX research process. In open development projects, user-participants adopt a variety of roles, including acting as consumers, players, bug reporters, player support services, community moderators, reviewers, developer advocates, playtesters, quality assurance testers, content creators, and ideators. This paper builds definitions for the user-participant roles and offers examples from the online forums for an open development game. Finally, it argues that we can design communications systems to support these roles.
Overview of the NLPCC 2019 Shared Task: Open Domain Conversation Evaluation

This paper presents an overview of the Open Domain Conversation Evaluation task in NLPCC 2019. The evaluation consists of two sub-tasks: Single-turn conversation and Multi-turn conversation. Each of the reply is judged from four to five dimensions, from syntax, contents to deep semantics. We illustrate the detailed problem definition, evaluation metrics, scoring strategy as well as datasets. We have built our dataset from commercial chatbot logs and public Internet. It covers a variety of 16 topical domains and two non-topical domains. We prepared to annotate all the data by human annotators, however, no teams submit their systems. This may due to the complexity of such conversation systems. Our baseline system achieves a single-round score of 55 out of 100 and a multi-round score of 292 out of 400. This indicates the system is more of an answering system rather than a chatting system. We would expect more participation in the succeeding years.
Using Restart Heuristics to Improve Agent Performance in Angry Birds

Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However none of these agents attempt one of the key strategies which humans employ to solve Angry Birds levels, which is restarting levels. Restarting is important in Angry Birds because sometimes the level is no longer solvable or some given shot made has little to no benefit towards the ultimate goal of the game. This paper proposes a framework and experimental evaluation for when to restart levels in Angry Birds. We demonstrate that restarting is a viable strategy to improve agent performance in many cases.
Optimizing Bounds on the Classical Information Rate of Quantum Channels with Memory

We are interested in classical communication over a quantum channel with memory, in particular, we are interested in quantum-auxiliary-channel-based upper and lower bounds on the information rate. Toward improving these bounds, we propose efficient algorithms for optimizing the auxiliary channel. From a practical perspective, the lower bounds are of significant interest because they represent mismatched information rates, i.e., information rates that are achievable by a communication system where the decoder is matched to the auxiliary channel, instead of the original channel. Moreover, the auxiliary channels are learned in a data-driven approach, i.e., only input/output sequences of the true channel are needed, but not the channel model of the true channel.
IT IN EDUCATION: FORESIGHT 2020: ICT COMPETENCY, ENHANCING CITIZENS’ MEDIA AND COMPUTER LITERACY

IT in education: Foresight 2020: ICT competency, enhancing citizens’ media and computer literacy
Augmented Reality Toolkit for a Smart Robot-Assisted MIS Platform

Minimally invasive surgery (MIS) offers important benefits to the patient, however it introduces additional complexity and constraints to the surgical workflow. Thus, the development of guidance systems which use Augmented Reality (AR) in order to provide clinicians with important information, has become a popular research topic in recent years. In this work, a visualization toolkit is presented, in which pre-operative 3D structures are overlaid to the intra-operative surgical view, during Robot-Assisted MIS. These structures are extracted from pre-operative Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans and are registered into the endoscopic view through a semi-automatic pipeline, that includes minimal user interaction.
Taxonomy design methodologies: Emergent research for knowledge management domains

A knowledge management research study was integrated into a consulting internship for three students in an MLIS programme, working under the direction of a faculty member. The project scope was to organise knowledge across all academic disciplines, as represented in university structures, in support of the consulting client’s software analytics tools for scholarly journal publishers. The study team’s original research contribution was a four-phase design and validation approach to taxonomy creation, using extant research methods in concert. The students learned to bridge their coursework knowledge into a knowledge management environment in industry and to apply data collection and analysis methods to a novel research project.
Measured element phase-shifting interferometry with a single piezoelectric transducer and low-friction workbench

Moving a large-aperture reference mirror (RM) with a significant weight through piezoelectric transducers (PZTs) for phase-shifting introduces considerable challenges and potential problems in conventional reference mirror phase-shifting interferometry (RMPSI). In this report, we present measured element phase-shifting interferometry (MEPSI), which provides an alternative approach for RMPSI without the restrictions of both the aperture and weight of the RM. The proposed MEPSI actuates a low-friction workbench with an ME on board by single PZT to realize high-accuracy phase-shifting, which is not affected by the aperture or the RM weight of. On this basis, a tilt-shift immune phase-shifting algorithm is further applied to restrain the influence of residual tilt-shift errors and other possible error sources on the measuring accuracy. Theoretical analysis and preliminary experiments indicate that the proposed MEPSI can achieve the same level of accuracy as RMPSI and a ZYGO interferometer, and the repeatability of MEPSI, which is not affected by the weight of the ME, is superior to 0.002 λ (PV). MEPSI solves the problems of phase-shifting with large-aperture and heavy-weight RM, thereby providing a simple and feasible alternative approach for conventional RMPSI with the same level of accuracy and with no phase-shift calibration.
COMPLEX FUNCTION PROJECTIVE SYNCHRONIZATION IN FRACTIONAL-ORDER COMPLEX NETWORKS AND ITS APPLICATION IN FRACTAL PATTERN RECOGNITION

Based on the stability theory of fractional-order systems, a projective synchronization scheme with different coefficients is realized in [Formula: see text] complex networks to build a model for fractal pattern recognition. In the proposed complex function projection synchronization scheme, first a drive-response network is constructed with [Formula: see text] fractional-order complex nodes; And then reasonable controllers are designed to realize the function projection synchronization with different projection coefficients, which are used to encode the fractal pattern; Finally, using the synchronization error system, the input variables for identifying fractal patterns can be solved, and the fractal pattern can be recognized. From the perspective of neurodynamics, the proposed model provides a better understanding of human perception system.
Exception Handling and Classical Logic

We present λtry, an extension of the λ-calculus with named exception handling, via try, throw and catch, and present a basic notion of type assignment expressing recoverable exception handling and show that it is sound. We define an interpretation for λtry to Parigot's λμ-calculus, and show that reduction (both lazy and call by value) is preserved by the interpretation. We will show that also types assignable in the basic system are preserved by the interpretation.