Reviewerly/exHarmony huggingface dataset:
Expanding Training Set for Graph-Based Semi-supervised Classification

Graph Convolutional Networks (GCNs) have made significant improvements in semi-supervised learning for graph structured data and have been successfully used in node classification tasks in network data mining. So far, there have been many methods that can improve GCNs, but only a few works improved it by expanding the training set. Some existing methods try to expand the label sets by using a random walk that only considers the structural relationships or selecting the most confident predictions for each class by comparing the softmax scores. However, the spatial relationships in low-dimensional feature space between nodes is ignored. In this paper, we propose a method to expand the training set by considering the spatial relationships in low-dimensional feature space between nodes. Firstly, we use existing classification methods to predict the pseudo-label information of nodes, and use such information to compute the category center of nodes which has the same pseudo label. Then, we select the k nearest nodes of the category center to expand the training set. At last, we use the expanded training set to reclassify the nodes. In order to further verify our proposed method, we randomly select the same number of nodes to expand the training set, and use the expanded training set to reclassify nodes. Comprehensive experiments conducted on several public data sets demonstrate effectiveness of the proposed method over the state-of-art methods.
Ancora sulla paratassi dello scritto letterario

The paper presents a corpus-based research (GRITTEXT) on modern Italian literary texts. According to a writer-oriented framework, it is aimed to analyse the writer's thought movements while he is composing instead of considering the communicative effects of the text on the reader. The period is the linguistic unit of reference chosen for analysis and is identified by strong punctuation marks (dot, question mark, exclamation point, sequence of dots). It is not necessarily composed by a sentence, since it can correspond to a set of clauses and any kind of phrases. The research made emerge the pervasiveness of parataxis in literary texts. Two or more independent syntactic constituents can be connected within a period only through weak punctuation marks (comma, semicolon, colon). Each constituent corresponds to a semantic scene characterized by an autonomous point of view depending on the protagonist, the narrator or the same writer. The paratactic period is a style device to express within a same representation the narrative joints arising to the writer in the course of writing but above all the different perspectives of each scene that are also temporally ordered. The paratactic period corresponds to a unitary multi-focus, multi-modal, multi time representation whose truth value cannot be verified unlike a proposition
Web Service Composition by Optimizing Composition-Segment Candidates

Web service composition has been increasingly challenging in recent years due to the escalating number of services and the diversity of task objectives. Despite many researches have already addressed the optimization of multiple Quality of Service (QoS) attributes, most of the currently available methods have to build a large web service dependency graph, which may incur excessive memory consumption and extreme inefficiency. To address these issues, we present a novel web service composition method by optimizing composition-segment candidates. Firstly, we formalize the web service composition problem as a Mixed-Integer Linear Programming (MILP) model and introduce some effective techniques for complex cases, and then a standard solver can be applied to this model. Afterwards, a candidate optimization method is proposed to solve the MILP model efficiently, which runs sharply fast without building a web service dependency graph. Experimental results on both Web Service Challenge 2009's datasets and substantial datasets randomly generated show that the proposed method outperforms the state-of-art while achieving a much ideal tradeoff among all the objectives with better performance.
Time in Multi-agent Systems

This is a research proposal, aiming to improve tooling and features of agent-oriented programming languages, in particular Jason, to handle virtual time and real-time deadlines. The main idea is to apply known techniques and patterns from asynchronous programming in classical programming languages, and extend Jason as necessary.
The Empirical Study of Passwords Analysis in Access Point with Specific-Rules and Graphic Process Units

Access Point is challenging as WiFi security is often abused. Most access points are open to the public and might be targeted by hackers. One of the most popular hacking strategies to take control of the passwords that are easy to do with GPU. This empirical research proposes three major parts to examine the security of the access point and shows that the access point located in the public is vulnerable. The proposed methods are the method of catching the encrypted password packets, the GPUs for the passwords analysis, the specific rules of passwords analysis, and indicates the insecurity of the AP network. The wireless work is insecure in the real scene from the empirical data we collected. Part of the AP passwords can be cracked effectively from our cloud server which is equipped with GPUs and the rules set for certain areas, and do the password patterns analysis anywhere once the encrypted password packets are caught randomly. Thus, the vulnerability and weakness of wireless networks are seen. It shows that the insecurity gap caused by the socio-human elements which are the so-called lazy passwords. It reveals the true evidence of the insecurity of wireless networks and gives suggestions on wireless network protection.
A Framework for Cervical Cell EDF Image Segmentation Using Similarity Measure

The segmentation of cervical cells is the core part of the automatic analysis system of cervical smear. In this paper, we propose a method for segmentation of EDF images of overlapping cervical cells based on similarity measure. Based on the consistency of focus and position information between cytoplasm and target nucleus, the similarity measure of cytoplasm is constructed by using the characteristic of EDF image which is composed of several images with different focal points. Based on similarity measure, the cytoplasmic contour was tracked to achieve rough segmentation. Finally, regularized level set evolution (DRLSE) was used to refine the obtained contour at the pixel level. The approach was evaluated based on the public dataset-ISBI2015 which is superior to existing methods in image segmentation of EDF for overlapping cells.
Ontology-Based System for Automatic SQL Exercises Generation

In computer science as in many fields, practice activities are essential to facilitate skill acquisition. Nevertheless, developing and correcting questions are time consuming tasks. That is the case, in particular, for traditional and online SQL courses which include many SQL questions to be created and corrected. SQL learners need to practice more writing SQL statements in order to better understand and correct their syntactic and semantic errors. To overcome the difficulty of creating questions from scratch, research proposes to automatically generate such questions. In this work, we propose a system that teachers can use to automatically produce SQL questions sorted by difficulty level. Instead of creating SQL questions, teachers only propose a database schema. Learners can also use the system to answer SQL questions and get appropriate feedback to improve their skills.
Image-Based Recognition of Braille Using Neural Networks on Mobile Devices

Braille documents are part of the collaboration with blind people. To overcome the problem of learning Braille as a sighted person, a technical solution for reading Braille would be beneficial. Thus, a mobile and easy-to-use system is needed for every day situations. Since it should be a mobile system, the environment cannot be controlled, which requires modern computer vision algorithms. Therefore, we present a mobile Optical Braille Recognition system using state-of-the-art deep learning implemented as an app and server application.
Development of a Learning-Support System for Science Using Collaboration and Body Movement for Hearing-Impaired Children: Learning Support for Plant Germination and Growth Conditions

In this study, we aimed to develop a full-body gaming-style collaboration and learning support system for a proxy topic of plant germination and growth conditions while focusing on improving the learning experiences of hearing-impaired children. The system is based upon a quiz game activity in which participants learn about plant germination and growth conditions via collaboration and question-answering with their friends. Through the use of a collaborative gaming environment, hearing-impaired learners can repeatedly simulate experiments and observations on germination and growth to accelerate and enhance learning. The system is built with standard information and communications technologies and is evaluate the system to assess its effectiveness. The evaluation experiment revealed the system supported collaborative game play with enjoyment, empathy for others, and interaction for hearing-impaired children. We conclude that the system is useful in supporting hearing-impaired children in learning science through collaboration. Furthermore, we believe that system, which uses body movement and collaboration, might be applicable to other learning areas.
Communication-Efficient and Privacy-Preserving Protocol for Computing Over-Threshold Set-Union

In a variety of applications, the data items of multiple participants are collected and analyzed, and meanwhile the participants’ privacy needs to be protected. This paper studies an over-threshold data aggregation problem, i.e., over-threshold set-union. In our model, we assume there are n participants, an untrusted data aggregator and a proxy, and each participant has a sensitive data item. The over-threshold set-union is normally defined as follows: given a threshold t, the aggregator only learns the data items which appear at least t times in the union of data items of all participants without learning anything else. Existing solutions either suffer from high communication cost or leak the multiplicity information of data items. In order to handle this defect, we present an efficient protocol in the honest-but-curious model by leveraging threshold secret sharing and dual pairing vector spaces. We prove that the proposed protocol not only has $$O(n\log ^2 n)$$ communication complexity which nearly matches the lower bound $$\varOmega (n/\log n)$$ but also protects the data privacy.
Cybersecurity: a predictive analytical model for software vulnerability discovery process

A software vulnerability is defined as a flaw that exists in computer resources or control that can be exploited by one or more threats. Vulnerabilities are discovered throughout the entire life cycle of the software. In this paper, we examine existing vulnerability models on the subject area and propose a new time-based differential equation model. Our proposed model is based on the assumption that vulnerability saturation is a local phenomenon, that possesses an increasing cyclic behaviour within the software vulnerability life cycle. Daily vulnerability data is extracted from the National Vulnerability Database (NVD) to obtain a cumulative quarterly vulnerability data set for three Operating Systems: Mac OS X, Windows 7, and Linux Kernel. When we apply the proposed model to this data, it is discovered that our model performs significantly better than existing models, in terms of fitting and prediction capabilities.
Semantic Matching over Matrix-Style Tables in Richly Formatted Documents

Table is an efficient way to represent a huge number of facts in a compact manner. As practitioners in the vertical domain share lots of common prior knowledge, they tend to represent facts more concisely using matrix-style tables. However, such tables are originally intended for human reading, but not machine-readable due to their complex structures including row header, column header, metadata, external context, and even hierarchies in headers. In order to improve the efficiency of practitioners in mining and utilizing these matrix-style tables, in this study we introduce a challenging task to discover fact-overlapping relations between matrix-style tables. This relation focuses on fine-grained local semantics instead of overall relatedness in conventional tasks. We propose an attention-based model for this task. Experiments reveal that our model is more capable of discovering the local relatedness, and outperforms four baseline methods. We also conduct an ablation study and case study to investigate our model in detail.
QoS Time Series Modeling and Forecasting for Web Services: A Comprehensive Survey of Subsequent Applications and Experimental Configurations

Time-aware (time series-based) Web service QoS modeling and forecasting have been investigated and addressed for over a decade and a large number of studies and approaches have been produced. However, these existing efforts lack a comprehensive and detailed review that profoundly and systematically organizes, analyzes, and discusses this body of work. Thus, to fill this gap, the authors offered the paper QoS Time Series Modeling and Forecasting for Web Services: A Comprehensive Survey, in which four essential research concerns of this area, namely, problems, approaches, performance measures, and QoS datasets, have been recognized and reviewed in detail. However, aside from these essential research concerns, we also identified two optional research concerns from the current studies, namely, the subsequent applications and experimental configurations. Due to space restrictions, these two optional research concerns were only briefly mentioned in the above survey article, and thus, in this supplementary paper, the authors thoroughly present and review these two optional research concerns. The primary purpose of performing QoS time series modeling and forecasting is to obtain accurate future QoS estimations for subsequent usage (application), such as QoS-aware service composition and proactive service replacement for SLA/QoS management. Therefore, in the section addressing the first optional research concern, the application of each surveyed study is identified first, and then these current applications are introduced in detail. However, to comprehensively and rigorously observe and evaluate the performance of a proposed or employed approach under different conditions, a set of configuration settings must be varied to run experimentation. Thus, in the second part of this paper, we first define and discuss the identified experimental configuration parameters in this research area and then list the parameters and settings that have been considered in each surveyed study.
A Novel Cyber-attack Detection Approach based on Kernel Extreme Learning Machine using FR-Conjugate Gradient

Billions of devices have been developed with communication technology especially cyber technology, which provides great convenience for our lives. However, they also have produced some huge information security risks. The detection algorithm of traditional intrusion detection system (IDS) usually has a poor generalization and inefficient performance, that is to say it is not able to identify new attack types and deal with training data set with huge samples. In this article, a novel detection algorithm of IDS based on kernel extreme learning machine (KELM) using FR-conjugate gradient (FRCG-KELM) is proposed to overcome these problems. What's more, a standard NSL-KDD data set is used to evaluate its performance. Through comparing with KELM, the serial experiments verify that the proposed method can achieve a better performance including the higher detection accuracy, higher detection rate, higher generalization capability, lower false detection rate and greater computation ability.
Guided Networks for Few-Shot Image Segmentation and Fully Connected CRFs

The goal of the few-shot learning method is to learn quickly from a low-data regime. Structured output tasks like segmentation are challenging for few-shot learning, due to their being high-dimensional and statistically dependent. For this problem, we propose improved guided networks and combine them with a fully connected conditional random field (CRF). The guided network extracts task representations from annotated support images through feature fusion to do fast, accurate inference on new unannotated query images. By bringing together few-shot learning methods and fully connected CRFs, our method can do accurate object segmentation by overcoming poor localization properties of deep convolutional neural networks and can quickly updating tasks, without further optimization, when faced with new data. Our guided network is at the forefront of accuracy for the terms of annotation volume and time.
Contour-based High-speed Image Registration for Train Fault Diagnosis in Complex Environment

As an important part of image processing, the performance of train image registration imposes a direct impact on the efficiency of image-processing-based fault diagnosis of train systems. Considering the huge size of the train image (e.g., 180, 000 × 2, 048 pixels), it is usually difficult for the traditional image registration methods to meet the high requirements on rapidity and accuracy in complex diagnosis environment. In this paper, a novel contour-based train image registration method is proposed aiming at expediting the diagnosis process and improving the diagnosis accuracy simultaneously. First, the distorted image is preprocessed to eliminate the adverse effects of external interference on the image registration. Second, the contour features of key devices of train are extracted from the preprocessed images. Third, the image is registered by virtue of those features to eliminate the deviation between the distorted and template images. The experimental results show that, compared with the traditional feature point extraction registration method, the registration performance has been improved significantly in the sense that the time consumption is reduced from 517s to 97s, and the registration accuracy is increased from 96.77% to 100%.
Context Based Quantum Language Model with Application to Question Answering

Equipped with quantum probability theory, quantum language models (QLMs) aimed at a principled approach to modeling term dependency have drawn increasing attention. However, even though they are theoretically more general and have effective performance, current QLMs do not take context information into account. The most important element, namely density matrix, is constructed as a summation of word projectors, whose representation is independent of context information. To address this problem, we propose a Context based Quantum Language Model (C-QLM). Between word embedding and sentence density matrix, a bidirectional long short term memory network is adopted to learn the hidden context information. Then a set of vectors is utilized to extract density matrices’s features for question and answer sentences which can be used to calculate the matching score. Experiment results on TREC-QA and WIKI-QA datasets demonstrate the effectiveness of our proposed model.
Adaptivity as a Service (AaaS): Personalised Assistive Robotics for Ambient Assisted Living

The need for personalised Ambient Assisted Living (AAL) solutions is widely recognised. However, many existing solutions lack flexibility in terms of long-term user-adaption, a natural effect of working within the constraints of lab-based evaluation. As such, few approaches directly address a potentially desirable heterogeneous future where AAL is accessible to all: people should be able to create solutions that work for them through bricolages of assorted off-the-shelf (OTS) devices, including robots. For AAL to succeed at scale, these devices must share experiences of user interactions within and outwith the home. Adaptivity as a Service (AaaS) sandboxes adaptivity into its own research domain and posits that adaptation should be managed by a highly specialised adaptivity service that ‘fits in’ with different AAL solutions.
Reasoning With Interval-Valued Probabilities

Adequate representative statistical training data needed for machine learning algorithms are often unavailable and, when available, they are often mired in incomplete/missing data. Imputation of such data must be guided by the relationships among different variables and/or by data `missingness' mechanisms. Interval-valued (IV) probabilities are better suited in situations where such information is unavailable. We take the viewpoint that IV probabilities (IVPs) emerge from a single underlying probability distribution about which one has only partial information. PrBounds, the IVPs that this vantage point engenders, offer a fresh perspective of the IV counterpart notions of conditioning and independence and enable reasoning to be carried out in much the same manner as one would with probabilities. When the attribute values are unknown/missing or are known to lie within a set of values, PrBounds can be efficiently learnt by a frequency counting method. The probabilities associated with an arbitrary imputation strategy, including the underlying `true' probabilities, are guaranteed to lie within the PrBounds learnt in this manner. We present an experiment to illustrate the proposed framework.
Multi-target Track Extraction Method Based on Gaussian Mixture Probability Hypothesis Density Filter

Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter can estimate the number and state of targets simultaneously, which is an effective multi-target tracking method. However, the identification of each target is needed to construct target track, and the filter cannot directly provide such information. In this paper, an improved multi-target tracking algorithm is proposed, which assigns a unique label value to each Gaussian component, and gives association between the states of each target over time. At the same time, to solve the problem of performance degradation under high clutter concentration, the update weight of Gaussian component is re-corrected in this paper, and finally the only observation corresponding to each target is extracted to ensure the continuity of track. Simulation results show the effectiveness of the proposed method.
EXTENSIVE READING DALAM UPAYA MENINGKATKAN MINAT BACA SANTRI AL-ADHIM DALAM MENGHADAPI SBMPTN

ABSTRACTExtensive Reading is one of the alternatives for educators to inspire the students in completing their interest in reading especially when answering questions that have long and complex English Reading texts. Extensive Reding can also help the students to be more enthusiastic about reading and elaborate on the writer's ideas into an understanding that is easier to understand. This is the right solution for the students who perceive long English texts as difficult things to do especially for students who face University Entrance Examination (SBMPTN). Although, school education is considered capable and trusted by the government to provide education, but in real the school deemed insufficient to meet the standards set by goverenment, therefore non-school educational institutions such as the Pesantren Kilat (SATLAN) as Learning Guidance Institution need to be accelerate to assist students who are less able and want went to college to upgrade his knowladge.Keyword: Extensive Reading, Reading Interest, Pesantren Kilat (SATLAN).