Reviewerly/exHarmony huggingface dataset:
How Citation Is Signaled: A Corpus-Based Study on Reporting Verbs in Chinese Academic Papers

Reporting verbs (RVs) are an important language resource for academic writing, and using such verbs is a language feature of academic articles. Through investigating 30 research articles in the three disciplines of economics, politics, and linguistics, this paper examines the forms and sentence patterns, functions and classification, and frequency distribution of Chinese RVs. In addition, a comparison is made between Chinese RVs and English RVs. The study has the following findings: (1) Chinese RVs are characterized by four forms, five basic sentence patterns, and three categories, i.e., research verbs, discourse verbs, and cognition verbs; (2) the function of RVs reflects the attention of a referrer to the research of a referee and shows the referrer’s evaluation attitudes; (3) regarding the distribution of RVs, Chinese writers use RVs less frequently than English writers. In addition, Chinese articles have more research verbs than English, which reflects that Chinese writers are more likely to cite other people’s research behaviours and report their research results instead of evaluating them. The findings on RVs in this study can not only provide support for Chinese academic writing, but also facilitate the exploration, application, and education of Chinese for Academic Purposes.
A Proposed Partnership Model for University Libraries in Zimbabwe

Literature suggests that partnerships among libraries are important as they help with resource sharing and cost-cutting in the areas of expertise, information and communication technologies, library resources, subscriptions, publications, and library space. The chapter explores the literature on how libraries have been partnering for strategic reasons. Challenges and opportunities related to partnerships were also identified. The article seeks to find out whether there is a need to conduct further studies on the reception of the proposed Creative Library Partnership model by ZULC. Borrowing from success stories realized elsewhere, the authors make recommendations for the Zimbabwe University Libraries Consortium (ZULC) and other library consortia to adopt the Creative Library Partnership model. A follow-up survey asks: how Zimbabwe university libraries have been partnering, challenges, and opportunities associated with those partnerships.
A Smart Search-Based Ontology Visualization Tool Using SPARQL Patterns

We are proposing a semantic approach that aims to identify valid linguistic web services composition scenarios. It targets both linguistic and software engineering experts. It is based on an OWL2 multilingual ontology, named LingOnto which models and reasons about linguistic knowledge. However, users especially non-ontology experts have difficulty to make sense of LingOnto as they do not understand its syntax. Hence, we decide to visualize LingOnto to attempt this issue. Nevertheless, the heterogeneity and the amount number of linguistic knowledge make the visualisation hard to comprehend due to visual clutter and information overload. In this paper, we propose a user-friendly ontology visualisation tool, named Ling-Graph. It targets both ontology and non-ontology experts and addresses the readability and understandability requirements. Ling-Graph is based on a “smart” search interaction technique, to extract and visualize, from LingOnto, a dynamic ontological view that contains only components corresponding to the user’s need. It is relied on a SPARQL patterns-based approach which takes the user’s need materialized by a set of search criteria as input and generates the ontological view that matches these criteria. The obtained tool is also applied to visualize the PersonLink ontology for non-ontology experts and a large-scale ontology DBpedia for ontology experts. Finally, we discuss the promising results derived from the evaluation of Ling-Graph.
Embedded Prototype Subspace Classification: A Subspace Learning Framework

Handwritten text recognition is a daunting task, due to complex characteristics of handwritten letters. Deep learning based methods have achieved significant advances in recognizing challenging handwritten texts because of its ability to learn and accurately classify intricate patterns. However, there are some limitations of deep learning, such as lack of well-defined mathematical model, black-box learning mechanism, etc., which pose challenges. This paper aims at going beyond the black-box learning and proposes a novel learning framework called as Embedded Prototype Subspace Classification, that is based on the well-known subspace method, to recognise handwritten letters in a fast and efficient manner. The effectiveness of the proposed framework is empirically evaluated on popular datasets using standard evaluation measures.
Visualization and Analysis of Global Agricultural E-Commerce Research Based on Knowledge Graph

In order to gain an insight into the current research of agricultural e-commerce, this paper makes a visual analysis through the relevant literature collected in the Web of Science database. The published literature mining from different years, disciplines, countries(or regions), and the literature mainly contain basic research, applied research, research hotspots and frontiers. Knowledge graph, the method we use in this paper, provides an intuitive and convenient way for researchers to better understand the research status of agricultural e-commerce. Also, the results could strongly provide relevant reference for domestic studies in the same field.
Using Ontologies to Express Prior Knowledge for Genetic Programming

Ontologies are useful for modeling domains and can be used to capture expert knowledge about a system. Genetic programming can be used to identify statistical relationships or models from data. Combining expert knowledge as well as statistical rules identified solely from data is necessary in application domains where data is scarce and a large body of expert knowledge exists. We therefore study if the performance of genetic programming can be improved by incorporating prior knowledge from an ontology. In particular, we include prior knowledge as additional features for genetic programming. The approach is tested with six benchmark data sets where we compare the required computational effort that is necessary to find an acceptable model with and without additional features. The results show that additional features gathered from an ontology improve the performance of tree-based GP. The probability to find acceptable solutions with a fixed computational budget is increased. For noisy data sets we observed the same effect as for the data sets without noise.
Automatic Monitoring &amp; Early Warning of Video Receiving System

In order to solve the communication security risk of video receiving system, improve the fault early warning, analysis and troubleshooting time, we use spectrum analyzer technology, serial communication technology, network monitoring technology, etc., from the reception of RF signals, analysis of analog signals, monitoring of decoding parameters, the feasibility of realizing the automatic monitoring & early warning method of video signal receiving system is studied.
A Coin-Free Oracle-Based Augmented Black Box Framework

After the work of Impagliazzo and Rudich (STOC, 1989), the black box framework has become one of the main research domain of cryptography. However black box techniques say nothing about non-black box techniques such as making use of zero-knowledge proofs. Brakerski et al. introduced a new black box framework named augmented black box framework, in which they gave a zero-knowledge proof oracle in addition to a base primitive oracle (TCC, 2011). They showed a construction of a non-interactive zero knowledge proof system based on a witness indistinguishable proof system oracle. They presented augmented black box construction of chosen ciphertext secure public key encryption scheme based on chosen plaintext secure public key encryption scheme and augmented black box separation between one-way function and key agreement. In this paper we simplify the work of Brakerski et al. by introducing a proof system oracle without witness indistinguishability, named coin-free proof system oracle, that aims to give the same construction and separation results of previous work. As a result, the augmented black box framework becomes easier to handle. Since our oracle is not witness indistinguishable, our result encompasses the result of previous work.
Patch-Level Regularizer for Convolutional Neural Network

Over-fitting is a common issue of training deep convolutional neural network especially when the dataset is limited. In this work, we propose a patch-level regularizer to force a convolutional neural network to learn many sub-models during training, and aggregate these models during testing. This approach proves to be robust and noise tolerant as our regularizer exposes small patches of an image to the network to learn all the features equally during training. The regularizer can be easily applied to the convolutional neural networks to further improve their classification performance. Experiment results on publicly available datasets demonstrate consistent improvement over existing regularizers.
Deep Jpeg Image Deblocking Using Residual Maxout Units

Image compression is a field in image processing that tries to remove redundant information in an image. Losing the information in lossy compression techniques such as JPEG results to artifacts in the decompressed image that necessitates the image restoration. In this work, a new image restoration scheme based on deep neural nets and maxout activation functions for the application of image deblocking is proposed. Experimental results are presented to demonstrate the superiority of the proposed method both in terms of subjective and objective metrics.
Participatory Evaluation of Human-Data Interaction Design Guidelines

The design of visual analytics tools for facilitating human-data interaction (HDI) plays a key role to help people identifying useful knowledge from large masses of data. Designing data visualization based on guidelines is relevant. However, it is necessary to further promote the engagement of people in evaluation activities in the design process. Stakeholders need to comprehend the guidelines to help with the evaluation results and design decisions. In this paper, we propose participatory evaluation practices based on HDI design guidelines. The practices aim to create the conditions to participants from any profile collaborate with the design guidelines evaluation. The practices were used on a design problem involving interactions with coordinated visualization. The context of application was a visual analytic tool supporting decisions related to the production strategy in oil reservoirs with the participation of key stakeholders. The results indicate that participants were able to understand the design guidelines and took advantage from them in the design decisions.
Multi-task Deep Reinforcement Learning with Evolutionary Algorithm and Policy Gradients Method in 3D Control Tasks

In deep reinforcement learning, it is difficult to converge when the exploration is insufficient or a reward is sparse. Besides, on specific tasks, the amount of exploration may be limited. Therefore, it is considered effective to learn on source tasks that were previously for promoting learning on the target tasks. Existing researches have proposed pretraining methods for learning parameters that enable fast learning on multiple tasks. However, these methods are still limited by several problems, such as sparse reward, deviation of samples, dependence on initial parameters. In this research, we propose a pretraining method to train a model that can work well on variety of target tasks and solve the above problems with an evolutionary algorithm and policy gradients method. In this method, agents explore multiple environments with a diverse set of neural networks to train a general model with evolutionary algorithm and policy gradients method. In the experiments, we assume multiple 3D control source tasks. After the model training with our method on the source tasks, we show how effective the model is for the 3D control tasks of the target tasks.
Single-Image Rain Removal Via Multi-Scale Cascading Image Generation

A novel single-image rain removal method is proposed based on multi-scale cascading image generation (MSCG). In particular, the proposed method consists of an encoder extracting multi-scale features from images and a decoder generating de-rained images with a cascading mechanism. The encoder ensembles the convolution neural networks using the kernels with different sizes, and integrates their outputs across different scales. The decoder implements a coarse-to-fine image generation framework, adding fine details incrementally to the final de-rained images according to the spatial contextual information on different scales. We test the proposed method on both synthetic and real-world datasets. Experimental results show that the proposed method is robust to the changes of scene, e.g., the viewpoint and the depth, the heaviness of rain, etc., which suppresses the blurring problem of de-rained image and outperforms state-of-the-art methods consistently.
Using a digital still camera for shearography: preliminary experiments and observations

We have used a simple digital still camera, instead of a conventional video-rate camera, to capture high-resolution images in a shearographic system. The high-resolution, compressed JPEG still images (of 18 megapixel resolution) are only generated by the camera on command from the controlling PC. We successfully generated shearograms from the still images, which are indicative of the applied stresses on the test object. The test object in this case is a center-loaded metal plate, fixed at the edges. The use of a digital still camera simplifies the electronic part of the shearographic system, and prevents the generation of unnecessary images. We propose to apply the system in several different applications.
PCB Drill Path Optimization by Improved Genetic Algorithm

The paper provides an improved genetic algorithm based on full combination breeding strategy (FCGA). FCBC increases the probability of producing excellent individuals by full combination breeding and improves the quality of initial population by greedy algorithm. In this paper, all holes of printed circuit boards (PCB) are grouped by adjacency list. The distance matrix of all groups is obtained by coordinate quadrant method. Then the improved genetic algorithm is used to search the shortest path of all groups. Finally, the improved genetic algorithm is used to search the shortest path of all holes in each group. Test results with five traveling salesman problem(TSP) examples show that FCGA significantly reduces the number of iterations in comparison to classical genetic algorithm(CGA). Similarly, the average running time are reduced by an average of 17.04%. The experimental results also show that the proposed algorithm applied to PCB drill path optimization can effectively solve the problem of large-scale printed circuit board drilling path optimization.
Discriminative Features for Incremental Learning Classifier

An important problem in artificial intelligence is to develop an efficient system that can adapt to new knowledge in an incremental manner without forgetting previously learned knowledge. Although Convolutional Neural Networks (CNNs) are good at learning strong classifier and discriminative features, CNNs can not perform well in incremental classifier learning due to the catastrophic forgetting problem in the retraining process. In this paper, we propose a novel yet extremely simple approach to enhance the discriminative property of features for incremental classifier learning. We build a network for the universal feature space in which a group of image classes have intra-class compactness and inter-class separability. And, we model each incremental class to have a maximum margin from the rest of the models in universal space. Experiments are conducted on CIFAR-100 dataset and IMage Database for Context Aware Advertisement (IMDB-CAA) we collected. The results demonstrate the superiority of our approach, improving performance on CIFAR-100 dataset over state-of-the-art incremental learning systems. Furthermore, experiments on few-short incremental learning setting show very promising performance although we use only 4% of training samples on CIFAR-100 dataset.
Real-Time Style Transfer with Strength Control

Style transfer is a problem of rendering a content image in the style of another style image. A natural and common practical task in applications of style transfer is to adjust the strength of stylization. Algorithm of Gatys et al. [4] provides this ability by changing the weighting factors of content and style losses but is computationally inefficient. Real-time style transfer introduced by Johnson et al. [9] enables fast stylization of any image by passing it through a pre-trained transformer network. Although fast, this architecture is not able to continuously adjust style strength. We propose an extension to real-time style transfer that allows direct control of style strength at inference, still requiring only a single transformer network. We conduct qualitative and quantitative experiments that demonstrate that the proposed method is capable of smooth stylization strength control and removes certain stylization artifacts appearing in the original real-time style transfer method. Comparisons with alternative real-time style transfer algorithms, capable of adjusting stylization strength, show that our method reproduces style with more details.
Identifying and predicting key features to support bug reporting

Abstract Bug reports are the primary means through which developers triage and fix bugs. To achieve this effectively, bug reports need to clearly describe those features that are important for the developers. However, previous studies have found that reporters do not always provide such features. Therefore, we first perform an exploratory study to identify the key features that reporters frequently miss in their initial bug report submissions. Then, we propose an approach that predicts whether reporters should provide certain key features to ensure a good bug report. A case study of the bug reports for Camel, Derby, Wicket, Firefox, and Thunderbird projects shows that Steps to Reproduce, Test Case, Code Example, Stack Trace, and Expected Behavior are the additional features that reporters most often omit from their initial bug report submissions. We also find that these features significantly affect the bug‐fixing process. On the basis of our findings, we build and evaluate classification models using four different text‐classification techniques to predict key features by leveraging historical bug‐fixing knowledge. The evaluation results show that our models can effectively predict the key features. Our comparative study of different text‐classification techniques shows that naïve Bayes multinomial (NBM) outperforms other techniques. Our findings can benefit reporters to improve the contents of bug reports.
Mining Contrast Sequential Patterns based on Subsequence Location Distribution from Biological Sequences

With the generation of a large amount of biological data, researches on methods that can automatically analyze these biological data has become a hot spot. Contrast sequential patterns play an important role in identifying the characteristics of different biological sequences. However, previous studies on mining contrast sequential pattern did not consider the effects of gene/amino acid location distribution on patterns in given biological sequences. In this paper, we introduce the subsequence location distribution into the conditions of the contrast sequence pattern mining, extending previous studies which only considered support of patterns. We also design a novel algorithm, SLD-tree, which compresses datasets into the tree to avoid repeated scanning of the dataset, and can effectively mines contrast sequential patterns based on subsequence location distribution. The empirical study using real-world biological sequence demonstrates the effectiveness of our method. Moreover, we carry out classification experiment, the results verify our method have higher classification accuracy.
Development of a Generator System of Parameterized Questionnaires to Evaluate the Usability of Web Pages

The effectiveness of using websites have a strong dependence on their ergonomic qualities, which affect directly the business. This is more significant when related to the e-commerce, especially considering the little known companies. To verify ergonomic features in a website, the evaluator may conduct inspections based on questionnaires that are answered by him/her or by final users to provide information about the interface. In this paper, our goal was the development of an application that automatically generates ergonomic questionnaires to evaluate the usability of web sites. The main idea is to select specific questionnaires and specific questions that must be parameterized according to the users choice. Among these parameters, are the interaction elements present in the pages that compose the website, such as scroll bars, buttons, hyperlinks, windows, text fields and others. The analysis of the questionnaire responses, provides data to build better usability interfaces, resulting in less time and effort to achieve their goals, and thus, delivering positive results. These questionnaires and their responses can be stored in a database for future reference and may support new usability assessments. The tool was used by 22 evaluators. These users are aged between 19 and 30 years, and have some connection with the University of Sao Paulo. Correlations were also made between some questions to see if there was any relationship between them .
Modeling and Control of a Personal Care Robot Considering Posture Adjustment

In this paper, we propose an upper body posture adjust mechanism for our personal care robot. First, we review the related work on the development and control of mobile humanoid robots. Confronting the fact that little research has been carried out considering coordinated control of the upper and lower body, we present a posture adjust system which generates upper body configurations with respect to the acceleration of the mobile platform. As a result, the robot can behave with higher stability and additional personality during navigation tasks. In addition, evaluation experiments in real household scenarios prove that the proposed approach works as expected.
Facial Expression Recognition Using Adaptive Robust Local Complete Pattern

An effective face descriptor is a significant element of a perfect facial expression recognition system. In this paper, we propose a new face descriptor, Adaptive Robust Local Complete Pattern (ARLCP). ARLCP effectively encodes significant information of emotion-related features by using the sign, magnitude and directional information of edge response that is more robust to noise and illumination variation. In this histogram-based approach, obtained feature image is divided into several regions, histogram of each region is computed independently and all histograms are concatenated to generate a final feature vector. We have experimented our method on several datasets using cross-validation schemes to evaluate the performance. From those experiments, it is evident that our method (ARLCP) provides better accuracy in facial expression recognition.