Reviewerly/exHarmony huggingface dataset:
Jointly Learning Author and Annotated Character N-gram Embeddings: A Case Study in Literary Text

An author's way of presenting a story through his/her writing style has a great impact on whether the story will be liked by readers or not.In this paper, we learn representations for authors of literary texts together with representations for character n-grams annotated with their functional roles.We train a neural character ngram based language model using an external corpus of literary texts and transfer learned representations for use in downstream tasks.We show that augmenting the knowledge from external works of authors produces results competitive with other style-based methods for book likability prediction, genre classification, and authorship attribution.
Establishing Waiting Time Thresholds in Interactive Web Mapping Applications for Network QoE Management

Customer expectations will continue to drive communication service developers to optimise their use of network resources based on user satisfaction. Thus, network platforms need to be remodelled from Quality of Service (QoS) centric to Quality of Experience (QoE) aware platforms. The perceived QoE for interactive web applications such as Google maps or Openstreetmaps is dominated by waiting time, i.e. the perceived time to render the page and map. Studies have explored waiting time estimation for Web QoE applications (e.g. email, downloads, web pages). Perceived waiting time for web mapping applications have been less comprehensively explored. The relationship between perceived waiting time and network QoS is a key QoE management factor to enable QoE aware networks. In this paper, we review the principle of network QoE management and the perception of waiting times. We present experimental design and methodology that facilitate the identification of waiting time thresholds for web applications, using web maps as a use case. We outline our results along with a statistical analysis and discussion interpreting the results and their applications. Finally, we discuss follow-up experiments and how they could be developed and applied in the network QoE management.
Randomness Extraction via a Quantum Generalization of the Conditional Collision Entropy

Randomness extraction against side information is the art of distilling from a given source a key which is almost uniform conditioned on the side information. This paper provides randomness extraction against quantum side information whose extractable key length is given by a quantum generalization of the collision entropy, which is smoothed and conditioned differently from how this is done in existing schemes. Based on the fact that the collision entropy is not subadditive, its optimization with respect to additional side information is introduced, and is shown to be asymptotically optimal. The lower bound derived there for general states is expressed as the difference between two unconditional entropies and its evaluation reduces to an eigenvalue problem of two states, which are the entire state and the marginal state of side information.
Resource Sharing via Capability-Based Multiparty Session Types

Multiparty Session Types (MPST) are a type formalism used to model communication protocols among components in distributed systems, by specifying type and direction of data transmitted. It is standard for multiparty session type systems to use access control based on linear or affine types. While useful in offering strong guarantees of communication safety and session fidelity, linearity and affinity run into the well-known problem of inflexible programming, excluding scenarios that make use of shared channels or need to store channels in shared data structures. In this paper, we develop capability-based resource sharing for multiparty session types. In this setting, channels are split into two entities, the channel itself and the capability of using it. This gives rise to a more flexible session type system, which allows channel references to be shared and stored in persistent data structures. We illustrate our type system through a producer-consumer case study. Finally, we prove that the resulting language satisfies type safety.
Global Beautification of 2D and 3D Layouts With Interactive Ambiguity Resolution

Specifying precise relationships among graphic elements is often a time-consuming process with traditional alignment tools. Automatic beautification of roughly designed layouts can provide a more efficient solution but often lead to undesired results due to ambiguity problems. To facilitate ambiguity resolution in layout beautification, we present a novel user interface for visualizing and editing inferred relationships through an automatic global layout beautification process. First, our interface provides a preview of the beautified layout with inferred constraints without directly modifying an input layout. In this way, the user can easily keep refining beautification results by interactively repositioning and/or resizing elements in the input layout. Second, we present a gestural interface for editing automatically inferred constraints by directly interacting with the visualized constraints via simple gestures. Our technique is applicable to both 2D and 3D global layout beautification, supported by efficient system implementation that provides instant user feedback. Our user study validates that our tool is capable of creating, editing, and refining layouts of graphic elements, and is significantly faster than the standard snap-dragging or command-based alignment tools for both 2D and 3D layout tasks.
Quantum Computers for Optimization the Performance

Computers decrease human work and concentrate on enhancing the performance to advance the technology. Various methods have been developed to enhance the performance of computers. Performance of computer is based on computer architecture, while computer architecture differs in various devices, such as microcomputers, minicomputers, mainframes, laptops, tablets, and mobile phones. While each device has its own architecture, the majority of these systems are built on Boolean algebra. In this study, a few basic concepts used in quantum computing are discussed. It is known that quantum computers do not possess any transistor and chip while being roughly 100 times faster than a common classic silicon computer. Scientists believe that quantum computers are the next generation of the classic computers.
The Futility of Bias-Free Learning and Search

Building on the view of machine learning as search, we demonstrate the necessity of bias in learning, quantifying the role of bias (measured relative to a collection of possible datasets, or more generally, information resources) in increasing the probability of success. For a given degree of bias towards a fixed target, we show that the proportion of favorable information resources is strictly bounded from above. Furthermore, we demonstrate that bias is a conserved quantity, such that no algorithm can be favorably biased towards many distinct targets simultaneously. Thus bias encodes trade-offs. The probability of success for a task can also be measured geometrically, as the angle of agreement between what holds for the actual task and what is assumed by the algorithm, represented in its bias. Lastly, finding a favorably biasing distribution over a fixed set of information resources is provably difficult, unless the set of resources itself is already favorable with respect to the given task and algorithm.
The development of <i>ppdb (admission of new students)</i> application to develop the quality of new students’ recapitulation administration in vocational high school bumi cikajang

Every year the Vocational of High School BMI Cikajang as the education executant accepted new students. Students who registered there were registered manually by filling out the forms and attaching some requirements. Related to the results of interview with the committee regarding the process of registration found the problems, those were the disappearance of data or the registrant forms due to mixed with other files and the lack of management administrative of new students recap by the committee. The purpose of study was to find out PPDB application can assist the committee in conducting the data recapitulation of new students and making efficient of the performance of committee. Waterfall implementation was employed as a design of study. To test the functional aspect, this study used black box testing, meanwhile to test usability aspect it used CSUQ. The test was conducted to whole prospective new students and the committee. The result of questionnaire was 88.2% with very decent criteria, it can be concluded that PPDB application can help the committee in recapitulation the data of new students; in addition, it can make efficient the performance of committee in conducting registration collectively.
Implementation of Content-Based Anonymization Edge Router on NetFPGA

In recent years, a large number of Internet of Things (IoT) devices have appeared. Accordingly, various services using data from such devices have been proposed. However, the collected raw data include private information, and thus, privacy problems arise. Data anonymization is a method for removing privacy-sensitive information from raw data. Data anonymization for IoT data services should satisfy the following requirements. First, the raw data should be anonymized between a device and the cloud server. Second, the anonymization methods and the destinations of the collected data should be flexibly configured, as they depend on data types and agreements with data suppliers. Third, network transparency is necessary for ease of installation. However, conventional data anonymization systems do not satisfy these requirements. We propose anonymization hardware that functions as a network router on network edges. It directly anonymizes data in network packets. Moreover, it decides the destination IP address of the packets and anonymizes data based on their content. For high-throughput and low-power processing of the packets, the proposed hardware was implemented by using a field-programmable gate array. The throughput of the proposed hardware achieved 10 Gbps wire speed, and the power consumption was lower than that of software implementation.
Statistical and geostatistical spatial and temporal variability of physico-chemical parameters, nutrients, and contaminants in the Tenango Dam, Puebla, Mexico

Human activities such as farming have contributed to the contamination of water bodies. Studying the spatial and temporal variations of parameters that influence water quality is essential for determining whether the water is suitable for human consumption and the ecosystem. This work presents the results from a study of the Tenango Dam, Mexico, which was aimed at determining the spatial and temporal variability of water quality parameters. To this end, five field visits were conducted during 2015. A total of 34 georeferenced water collection stations were installed in order to analyze physico-chemical parameters, nutrients, and metals, namely: temperature, pH, dissolved oxygen, and hardness; nitrates (NO3−), nitrites (NO2−), and phosphorus (P); and cadmium (Cd), chromium (Cr), copper (Cu), and lead (Pb). Statistical analyses were performed in order to determine seasonal variations in these parameters and geostatistical methods (ordinary [OK] and universal kriging [UK]) were applied to define their spatial variability. The functioning of these prediction methods were evaluated with leave-one-out cross validation. The results indicate that the physico-chemical parameters do not present a problem for the dam's water system. Nevertheless, NO3−, NO2−, and metal levels indicate poorer water quality, making it unsuitable for human consumption. The geostatistical analysis shows that seasonality and the anthropogenic activities occurring in the area contributed to increasing or decreasing the values of the variables studied, as well as the geostatistical models' ability to capture their spatial variability patterns.
Objective Quality Assessment for Light Field Based on Refocus Characteristic

The light filed (LF) is emerging as a new form of 3D content due to its super dense-view and refocus properties. The compression algorithms of LF have been developed maturely; however, few metrics are published to measure the performance of coding algorithm. At present, the metrics widely used to evaluate the quality of LF is limited to average the objective scores on the whole sub-aperture images (SAI), but this time-consuming process cannot represent the overall quality of LF well. The refocus images are mapped from the original LF, and the distortion measure of the refocus images reflects the overall quality of LF. Therefore, we unprecedentedly utilize the refocus character of LF to build a new image quality assessment framework, named RIQA. The results show that RIQA can improve most of objective metrics than SAI method. Furthermore, the RIQA framework can save the running time extremely.
News Diversity and Recommendation Systems: Setting the Interdisciplinary Scene

Concerns about selective exposure and filter bubbles in the digital news environment trigger questions regarding how news recommender systems can become more citizen-oriented and facilitate – rather than limit – normative aims of journalism. Accordingly, this chapter presents building blocks for the construction of such a news algorithm as they are being developed by the Ghent University interdisciplinary research project #NewsDNA, of which the primary aim is to actually build, evaluate and test a diversity-enhancing news recommender. As such, the deployment of artificial intelligence could support the media in providing people with information and stimulating public debate, rather than undermine their role in that respect. To do so, it combines insights from computer sciences (news recommender systems), law (right to receive information), communication sciences (conceptualisations of news diversity), and computational linguistics (automated content extraction from text). To gather feedback from scholars of different backgrounds, this research has been presented and discussed during the 2019 IFIP summer school workshop on 'co-designing a personalised news diversity algorithmic model based on news consumers' agency and fine-grained content modelling'. This contribution also reflects the results of that dialogue.
Explainable AI

The American College of Radiology (ACR) has guidelines on appropriate ordering of Magnetic Resonance Imaging (MRI) brain scans. MRI requests are currently manually reviewed by radiologists to ensure compliance to these guidelines. In this paper, we implemented a stacked recurrent neural network (RNN) utilizing a bidirectional long short-term memory (Bi-LSTM) sequence with BioWordVec, a biomedical word embedding vector that uses word representations from a lexicon developed from medical publications, to develop an automated classification system for request audit. To overcome the problems of interpretation by black-box models, the RNN is integrated with a model agnostic explainer LIME (Local Interpretable Model-Agnostic Explanations) to provide explainable support for clinicians in the healthcare environment. The performance of the RNN is compared with a Random Forest (RF) algorithm that utilizes the bag-of-words concept. The RNN was trained and validated on 2470 rows of different patient free-text orders and tested on a separate 2711 orders, producing an accuracy of 82.51% and a ROC value of 0.89 which was either comparable to or surpassing RF both in performance and usability. The use of deep learning with explainable LIME in this study provided a good use case towards an augmented decision-making framework in healthcare.
An Evolutionary Approach to address Interoperability Issues in Multi-Device Signature Verification

In the present paper, we propose an evolutionary approach to address interoperability issues in multi-device signature verification, based on transformation mappings automatically tuned by a genetic algorithm. These mappings are meant to decrease dissimilarities between signatures acquired through different devices and with different modalities (stylus/finger). The effectiveness of the proposed method was evaluated on the e-BioSign data set. Our proposal achieved an average relative improvement of 26% of EER, for the case of skilled forgeries, compared to baseline results.
A SIMD-aware pruning technique for convolutional neural networks with multi-sparsity levels

Designs of electronic systems often require considering multiple design concerns. In this paper, we propose a novel multi-phase pruning technique for convolutional neural networks (CNNs) that is capable of efficient exploration of multiple design objectives and constraints. To truly take advantage of the sparsity obtained by pruning, we present two different levels of pruning granularity, fine- and coarse-grain, and show how they are combined in the design space exploration. In particular, we propose to take the SIMD architecture into account in the fine-grain pruning. By iteratively pruning to a single CNN, multiple candidates can be obtained from the trade-off between the given design concerns. Experiments with existing CNNs verify that the proposed technique enables more efficient design space exploration over the accuracy-speed trade-off.
Preference-based interactive multi-document summarisation

Abstract Interactive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound. Preference-based interactive learning has been successfully applied, but the existing methods require several thousand interaction rounds even in simulations with perfect user feedback. In this paper, we study preference-based interactive summarisation. To reduce the number of interaction rounds, we propose the Active Preference-based ReInforcement Learning (APRIL) framework. APRIL uses active learning to query the user, preference learning to learn a summary ranking function from the preferences, and neural Reinforcement learning to efficiently search for the (near-)optimal summary. Our results show that users can easily provide reliable preferences over summaries and that APRIL outperforms the state-of-the-art preference-based interactive method in both simulation and real-user experiments.
Scene Text Recognition Based on Deep Learning: A Brief Survey

Scene text recognition is a universal text recognition technology, which has become a research hotspot in computer field in recent years. Compared with the traditional document text recognition, the scene text recognition is more complex in aspect of font, distribution, background and so on. Which makes the traditional OCR technology no longer adapt to the new challenge. With the development of technology, deep learning has achieved good results in the field of image recognition. Therefore, this paper mainly summarizes the representative achievements in scene text recognition field based on deep learning method.
Ateb-Gabor Filtering Method in Fingerprint Recognition

A new filtration method of Ateb-Gabor for biometric security systems has been developed. It is substantiated that the developed method possesses all the properties of the classic Gabor filter. This greatly extends the functionality of the Gabor filter. Atebfunctions are a generalization of elementary trigonometry, therefore, they have greater functional capacity. The properties of Ateb-Gabor filtering are derived. The application of Ateb-Gabor filtering to images by introducing a two-dimensional Ateb-Gabor filter is considered. Examples of frequency characteristics with different parameters of the Ateb-Gabor filter have been developed. Examples of frequency characteristics with different parameters of the Ateb-Gabor filter have been developed. A data set for filtration was developed in the biometric security system. For this purpose, the bank of the frequency characteristics of the Ateb-Gabor filter has been formed. Experimental samples of filtration have been presented. The expediency of using the new filter is substantiated basing on comparing the filtered Gabor filter and the Ateb-Gabor filter by the metric PSNR - peak signal-to-noise ratio.
Traditional and Swarm Intelligent Based Text Feature Selection for Document Classification

In the age of information, the role of text feature selection has become a hot challenge for building automatic document classification models in various application areas. Meanwhile, various powerful schemes dealing with text feature selection are being developed continuously nowadays, but there still exists a research gap for "feature optimization problem": looking for the optimal features by taking account not only local optimism but also global optimism by avoiding the bias problem for unbalance and high dimension text feature. In this paper, we observed swarm-based searching policy which includes Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) to overcome the local optimization problem of high dimensional feature selection for BBC News document classification. Then, we compared the results using various evaluation measurements for performance and computation cost. The experimental results show that the selected feature using swarm intelligence search is more robust for classification results.
Authorship Analysis of Online Predatory Conversations using Character Level Convolution Neural Networks

Authorship Attribution (AA) of written content presents several advantages within the digital forensics domain. While AA has been traditionally applied to long documents, recent works have shown improved performance of neural AA models on short texts such as tweets and online conversations Concurrently, the rise of social media as well as a plethora of chat messaging platforms have made it easier for teenagers to be vulnerable to online predators. In this work, we present an authorship attribution model that trains on a corpus of online conversations involving predators, and perform subsequent analysis of the message representations. Our results show comparable performance relative to prior work for Authorship Attribution and highlight differences between predatory and non-predatory message styles.
Pre and Postprocessing for JPEG to Handle Large Monochrome Images

Image compression is one of the most important fields of image processing. Because of the rapid development of image acquisition which will increase the image size, and in turn requires bigger storage space. JPEG has been considered as the most famous and applicable algorithm for image compression; however, it has shortfalls for some image types. Hence, new techniques are required to improve the quality of reconstructed images as well as to increase the compression ratio. The work in this paper introduces a scheme to enhance the JPEG algorithm. The proposed scheme is a new method which shrinks and stretches images using a smooth filter. In order to remove the blurring artifact which would be developed from shrinking and stretching the image, a hyperbolic function (tanh) is used to enhance the quality of the reconstructed image. Furthermore, the new approach achieves higher compression ratio for the same image quality, and/or better image quality for the same compression ratio than ordinary JPEG with respect to large size and more complex content images. However, it is an application for optimization to enhance the quality (PSNR and SSIM), of the reconstructed image and to reduce the size of the compressed image, especially for large size images.