Reviewerly/exHarmony huggingface dataset:
Query Performance Prediction for Multifield Document Retrieval

The goal of the query performance prediction (QPP) task is to estimate retrieval effectiveness in the absence of relevance judgments. We consider a novel task of predicting the performance of multifield document retrieval. In this setting, documents are assumed to consist of several different textual descriptions (fields) on which the query is being evaluated. Overall, we study three predictor types. The first type applies a given basic QPP method directly on the retrieval's outcome. Building on the idea of reference-lists, the second type utilizes several pseudo-effective (PE) reference-lists. Each such list is retrieved by further evaluating the query over a specific (single) document field. The third predictor is built on the assumption that, a high agreement among the single-field PE reference-lists attests to a more effective retrieval. Using three different multifield document retrieval tasks we demonstrate the merits of our extended QPP methods. Specifically, we show the important role that the intrinsic agreement among the single-field PE reference-lists plays in this extended QPP task.
Deep Convolutional Neural Network Based Medical Concept Normalization

Medical concept normalization is a critical problem in biomedical research and clinical applications. In this article, we focus on normalizing diagnostic and operation names in Chinese discharge summaries to standard concepts, which is formulated as a semantic matching problem. However, non-standard Chinese expressions, short-text normalization, heterogeneity of tasks and flexible input of disambiguation mentions pose critical challenges in our problem. We propose two models, the basic model and flexible model, to tackle these problems. The basic model solves the core problem (the first three challenges) in ambiguous mentions normalization, while the flexible model deals with flexible input of ambiguous mentions and further explores the correlation among them. Specifically, in the basic model, we present a general framework to disambiguate a diagnosis and its corresponding operation simultaneously, which introduces a tensor generator and a novel multi-view convolutional neural network (CNN) with a multi-task shared structure. We propose that the key to address non-standard expressions and the short-text problem is to incorporate a matching tensor with multiple granularities. Then a multi-view CNN is adopted to extract semantic matching patterns. Finally, the multi-task shared structure allows the model to exploit medical correlations between diagnosis and operation mentions to better perform disambiguation tasks. Subsequently, we design a flexible model based on the basic model. Specifically, we add a flexible attention layer to all procedure representation vectors, and then apply a flexible multi-task scheme to share the correlated information. Comprehensive experimental analysis indicates that our model outperforms existing baselines, demonstrating the effectiveness and robustness of our model.
Improving the Quality of Sound Recovered Using the Visual Microphone with Frame-wise Image Denoising Preprocessing

Abstract The visual microphone is a passive remote sound recovery technique by analysing subtle vibrations in a video of an object vibrating due to sound. Multiple research endeavours have been done in improving the quality of the recovered sound, but so far none has been done on investigating the effect of denoising the videos frame by frame prior to the sound recovery using the visual microphone. This work fills in that gap, and evaluates the quality of the recovered sound based on human intelligibility metrics and simple signal-to-noise ratio metrics. Our best results indicated performing image denoising before sound recovery can reduce the noise power by up to 47.16 %, increase the intelligibility by 9.11 % and signal-to-noise ratio by 17.97 %. It is important to perform image denoising before sound recovery when the recorded videos are very noisy.
Towards Automated Spatio-Temporal Trajectory Recovery in Wide-Area Camera Networks

Much recent research in person re-identification has focused on improving the accuracy of matching query images from one camera view to candidates from another camera view. However, in a practical scenario, real-world surveillance system operators often must continually re-identify a person of interest through multiple views in a wide-area camera network, spatially and temporally “following” the person's trajectory. This aspect is substantially different from a traditional re-id algorithm that can only tell whether two images belong to the same person. To address this gap, we present a new algorithm to automatically reconstruct the time-stamped spatial trajectory of a person of interest moving in a camera network. With this output, a surveillance system user can easily tell where in the camera network the person of interest was located at any specific time. Since existing datasets lack the kind of annotated data needed to address this problem, we present a new dataset, RPIfield, which includes extensive trajectory annotations. We then present a novel algorithm with topology-informed transition time modeling and candidate space pruning strategies that lead to efficient trajectory reconstruction. To evaluate our method, we introduce three new evaluation metrics directly informed by practical system-level considerations and conduct extensive experiments on RPIfield.
FPGA Accelerator Design for License Plate Recognition Based on 1BIT Convolutional Neural Network

Abstract Aiming at the problem that convolutional neural network is difficult to deploy on small embedded devices due to its high complexity and large storage space requirement, this paper propose a convolutional neural network FPGA accelerator architecture based on binarization. Using the gray scale processing, binarization processing, threshold setting to reduce the number of parameters. Designing Parallel structures of convolution kernels, feature maps, and matrix blocks to accelerate. The designed architecture can be deployed on the AX7103 FPGA development platform with limited resources. The experimental results show that the convolutional neural network after parallel acceleration design can achieve a recognition accuracy rate of 98.73% on the premise of reducing the data bit width from 32 bits to 8 bits, the recognition speed is about 0.21 seconds/time.
Adopting New Educational Technologies and the Need for Digital Pedagogies

This chapter investigates ways of adopting new educational technologies as part of learning in formal, informal and non-formal educational environments. Towards that goal, there is a need to revisit digital pedagogies to align with new technological capabilities and new user needs. In fact, digital pedagogies need to consider the idea of integrating technology in education as more than just a tool for learning but as an active part of the pedagogy, especially as the need for distance learning delivery and virtual learning environments is increasing. In addition to the idea of digital pedagogies, which are part of more formal environments, concepts such as digital citizenship, whole-school and whole-child approaches are presented to emphasise the widespread need for improved experience with education when it comes to any form of learning (informal, non-formal).
Intelligence Methods of Forecasting

Since in most different statements of forecasting problems it is believed that the initial data contains some random component that can degrade the quality of the constructed predictive model, it is desirable to carry out some pre-processing data in order to reduce this component.
An Algorithm Based on Simple CNN and BI_LSTM Network for Chinese Word Segmentation

Abstract In dealing with most Chinese NLP tasks, word segmentation is an indispensable and critical work, and it also affects the accuracy of subsequent tasks. Neither the traditional BI_LSTM network can extract features effectively, nor the CNN network can deal with the timing problem effectively. As a result, this paper proposes a new algorithm to solve these problems. First, the Chinese characters’ feature in a whole sentence is extracted and recombined using the CNN network; then, the recombined feature is combined in BI_LSTM network; finally, each word of the whole sentence which classified by weight sharing and the corresponding classification of each word is the consequent output. Not only the advantages of CNN network are utilized for feature extraction, but also the advantages of BI_LSTM network are retained for timing processing. In conclusion, the learning ability of BI_LSTM network is increased, and the accuracy of output is improved to 98%.
Utilizing Axiomatic Perturbations to Guide Neural Ranking Models

Axiomatic approaches aim to utilize reasonable retrieval constraints to guide the search for optimal retrieval models. Existing studies have shown the effectiveness of axiomatic approaches in improving the performance through either the derivation of new basic retrieval models or modifications of existing ones. Recently, neural network models have attracted more attention in the research community. Since these models are learned from training data, it would be interesting to study how to utilize the axiomatic approaches to guide the training process so that the learned models can satisfy retrieval constraints and achieve better retrieval performance.
A Dimension-Wise Particle Swarm Optimization Algorithm Optimized via Self-Tuning

This article proposes an improvement to the traditional Particle Swarm Optimization (PSO) via modifications w.r.t. how particles move and are attracted to optimal positions. The performance is evaluated based on how well the algorithm is able to perform w.r.t. finding the global maxima of the Sinc, Marr-Wavelet, and Drop-Wave functions in multi-dimensional problem spaces. Each algorithm is put through a session of self-tuning with a sufficient number of iterations to ensure convergence so as to demonstrate that the evaluation of each algorithm is done with justified optimal parameters.
Variance of particle location in the stochastic model of PSO with inertia weight

In the Particle Swarm Optimization (PSO) method, the behavior of particles depends on movement parameters. Effective application of the PSO method in real-world problems requires stable behavior of particles in the swarm. In the stochastic particle stability analysis, recurrent formulas of expected value and variance of particle location are used. An explicit formula for the variance can also be obtained, however, it cannot be applied in practice due to its complexity. In our research, we propose assumptions guaranteeing a simple explicit formula for location variance. For the formula and given assumptions, we show stability areas in the particle configuration space, which guarantee order-2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> stability of particles also for probability distributions of movement parameters other than uniform. The areas are verified in simulations.
Feature Selection using Evolutionary Computation Techniques for Software Product Line Testing

Software product lines are an excellent mechanism in the development of software. Testing software product lines is an intensive process where selecting the right features where to focus it can be a critical task. Selecting the best combination of features from a software product line is a complex problem addressed in the literature. In this paper, we address the problem of finding the combination of features with the highest probability of being requested from a software product line with probabilities. We use Evolutive Computation techniques to address this problem. Specifically, we use the Ant Colony Optimization algorithm to find the best combination of features. Our results report that our framework overcomes the limitations of the brute force algorithm.
Related Blogs’ Summarization With Natural Language Processing

Abstract There is plethora of information present on the web, on a given topic, in different forms i.e. blogs, articles, websites, etc. However, not all of the information is useful. Perusing and going through all of the information to get the understanding of the topic is a very tiresome and time-consuming task. Most of the time we end up investing in reading content that we later understand was not of importance to us. Due to the lack of capacity of the human to grasp vast quantities of information, relevant and crisp summaries are always desirable. Therefore, in this paper, we focus on generating a new blog entry containing the summary of multiple blogs on the same topic. Different approaches of clustering, modelling, content generation and summarization are applied to reach the intended goal. This system also eliminates the repetitive content giving savings on time and quantity, thereby making learning more comfortable and effective. Overall, a significant reduction in the number of words in the new blog generated by the system is observed by using the proposed novel methodology.
Real-time Image Aesthetic Score Prediction for Portable Devices

With rapid advancements in technology, in recent times we have witnessed tremendous strides taken in camera and imaging sciences. Digital processing has also added to exceptional details in photography. The advancements in imaging have created a gap between capturing a moment centered around aesthetics and random captures without meaning. In this work, we aim to provide to users an insightful feedback on the aesthetics of the images captured. The feedback concentrates on the rule of thirds, high dynamic range, color, content and various quality aspects for each image captured. To achieve this, we propose a hierarchical real time image aesthetic score prediction technique. We use a modified YOLO-CNN and Mobilenet for object detection and style prediction, followed by a multi-threaded aesthetic predictor to analyze and score the images on portable devices.
Simulating Information Retrieval Test Collections

Simulated test collections may find application in situations where real datasets cannot easily be accessed due to confidentiality concerns or practical inconvenience. They can potentially support Information Retrieval (IR) experimentation, tuning, validation, performance prediction, and hardware sizing. Naturally, the accuracy and usefulness of results obtained from a simulation depend upon the fidelity and generality of the models which underpin it. The fidelity of emulation of a real corpus is likely to be limited by the requirement that confidential information in the real corpus should not be able to be extracted from the emulated version. We present a range of methods exploring trade-offs between emulation fidelity and degree of preservation of privacy.
Clustering-Based Data Reduction Approach to Speed up SVM in Classification and Regression Tasks

Support Vector Machine (SVM) is a popular machine learning algorithm, being able to tackle non-linear problem by use of appropriate kernels. However, the use of SVM can become unfeasible for many applications where relatively large datasets are used. Its application becomes particularly more prohibitive when considering environments where the hardware has strict memory and processing limitations. By appropriately reducing the size of the training set we can in succession speed up the learning and diagnosis of SVM. In this work we implement a data reduction approach using clustering to build a smaller and representative set. The approach is extended for both classification and regression problems. Results evaluated on both normal PC and low resource edge device showed a better performance with only a small loss in diagnosis accuracy for most cases. Still, on cases where a high loss was observed, the reduction approach allowed to regain the accuracy with a faster hyper-parameter optimization.
Constrained Minimization Problem for Image Restoration Based on Non-Convex Hybrid Regularization

It is widely known that the classic total variation(TV) model has been proven to be very effective in preserving sharp edges. However, the TV model suffers from the staircase effects which produce blocking artifacts in the restored images. In this paper, we propose a new hybrid regularization model by combining non-convex second order total variation with wavelet transform to restrain staircase effects and protect some details of the images. To compute the new model effectively, we propose an alternating minimization method for recovering images from the blurry and noisy observations. The new model is first transformed into several sub-problems, and the generalized iterated shrinkage algorithm, the Fourier transform method and projection method are used to solve these sub-problems, respectively. Numerical experiments show that the proposed model can restrain blocking artifacts while projecting sharp edges, and the restoration quality outperforms several state-of-the-art methods.
Design of Conversational Agents for CSCL: Comparing Two Types of Agent Intervention Strategies in a University Classroom

The use of Conversational Agents (CAs) in computer-supported collaborative learning (CSCL) has shown promising results regarding students’ productive dialogue and learning. Yet, limited work has explored the connection between the configuration of the CA behavior, the nature of the learning task, and the student behavior in authentic educational settings. In this work, we describe a pedagogical design space of CAs for collaborative learning composed of three dimensions: task design, domain model, and agent intervention strategies. We conduct an initial field study in a university classroom comparing two types of agent intervention strategies based on student participation, dialogue, and satisfaction. 54 university students worked in pairs in the same collaborative brainstorming task with a CA tool and were randomly assigned in two CA conditions with a) knowledge-based prompts to connect two domain concepts, b) social prompts to link their partners’ contributions. The results show that students who received knowledge-based prompts significantly exchanged more messages with evidence of explicit reasoning and were more satisfied with the agent and their discussion during the task. Students from both conditions reported problems like the lack of context-awareness and timely interventions by the agent. We discuss the relation between the agent intervention strategies and the task design towards seeking design recommendations for CAs in CSCL.
Integrated Learning Method for Anomaly Detection Combining KLSH and Isolation Principles

Aiming at the problem that the Isolated Forest (iForest) has low local anomaly detection accuracy in highdimensional and massive data sets, this paper proposes an anomaly detection method that combines locality-sensitive hashing algorithm based on Gaussian Kernel Function (KLSH) and means-optimized iForest algorithm. In this method (KLSH+iForest), the kernel function is used to map the data from the linearly indivisible data space to the linearly separable feature space, and local anomalies are converted into global anomalies. Based on above, iForest is constructed to perform anomaly detection on the Kernelized data sets. To solve the problem of how to select the optimal segmentation attributes and values for iForest, this paper proposes a mean optimization strategy. While maintaining the ability of iForest to detect global anomalies, KLSH+iForest also improves the accuracy of local anomaly detection. We compare KLSH+iForest with the LOF algorithm and the improved algorithms based on LSH on public data sets. Experimental results show that KLSH+iForest has significantly improved the accuracy and efficiency of anomaly detection in highdimensional and massive data sets.
Guiding Socio-Technical Reflection of Ethical Principles in TEL Software Development: The SREP Framework

In society and politics, there is a rising interest in considering ethical principles in technological innovation, especially in the intersection of education and technology. We propose a first iteration of a theory-derived framework to analyze ethical issues in technology-enhanced learning (TEL) software development. The framework understands ethical issues as an expression of the overall socio-technical system that are rooted in the interactions of human actors with technology, so-called socio-technical interactions (STIs). For guiding ethical reflection, the framework helps to explicate this human involvement, and to elicit discussions of ethical principles on these STIs. Prompts in the form of reflection questions can be inferred to reflect on the technology functionality from relevant human perspectives, and in relation to a list of fundamental ethical principles. We illustrate the framework and discuss its implications for TEL.
Using a New Circular Prediction Algorithm to Design an IMM Filter for Low Update Rate Radar System

For radar systems with low update rates; such as track-while-scan (TWS) systems using rotating phased array antennas; reducing the prediction error is a very important issue. A good interacting multiple models (IMM) hybrid filter combined with circular and linear filters that are defined in relation to three measurements has been proposed in the literature. However; the algorithm requires three previous measurements; and too much prior information will result in a reduced ability to predict the future position of a highly maneuvering target. A new circular prediction algorithm for maneuvering target tracking is proposed as a non-linear prediction filter in this paper. Based on this new predictor; we also proposed a new type of IMM filter that has good estimation performance for high maneuvering targets. The proposed hybrid filter is entirely defined in relation to two measurements in a three-dimensional space to obtain a better maneuver following capability than the three measurements hybrid filter. Two target profiles are included for a comparison of the performance of our proposed scheme with that of the conventional circular; linear and IMM filters. The simulation results show that under low update rates; the proposed filter has a faster and more stable estimation response than other filters.
Pedagogical considerations of the curricular incorporation of ICT

The potential for the incorporation of Information and Communication Technologies (ICT) in education faces several challenges to consider, ranging from literacy and digital gaps, to varied simple or advanced levels of appropriation, from simple way of automating teaching activities for expository purposes or exchanging information to those teaching experiences that could hardly be developed without them. The process of incorporating ICT in the teaching task is a gradual, reflective and continuous process of permanent training that allows the teacher to develop educational experiences that modify the traditional way of teaching instruction, transformations that allow redesigning their educational practice with the purpose of creating meaningful learning in learners. With the intention of offering some of the initial considerations to be taken into account, this paper presents certain aspects to keep in mind in the inclusion of ICT in the design of activities for academic purposes.