Reviewerly/exHarmony huggingface dataset:
Research on Payment Attractiveness of Knowledge Contributors in Paid Q&amp;A Based on Hidden Markov Model

Paid Q&A is in great popularity because it provides askers with the right to choose satisfying knowledge contributors to consult independently, and it has faster responding speed and higher answer quality than traditional nonpaid Q&A. Based on social capital theory, this paper summarizes influencing factors of payment attractiveness, explores the relationship among payment decision, payment attractiveness and influencing factors such as interactive behaviors in the community by constructing a hidden Markov model. Function of each part of the model is set to reveal the dynamic process of knowledge contributors' payment attractiveness and specific effect of each influencing factor. Data crawled from Zhihu.com are used for model training and parameter estimation. The results show that the state of payment attractiveness can be divided into three (high, medium and low) to best optimize the model. Influencing factors of number of likes, followers, public answers and reviews will play a positive effect on the transition of payment attractiveness state except for a few special cases. Number of honor labels will play a positive effect on the transition of medium and high attractiveness state to payment decision while price plays a negative effect. The results of the study are expected to provide a reference for community operators to efficiently manage knowledge contributors and promote paid Q&A service to develop better.
A new model for coreference resolution based on knowledge representation and multi-criteria ranking

Coreference resolution is critical for improving the performance of all text-based systems including information extraction, document summarization, machine translation, and question-answering. Most of coreference resolution solutions rely on using knowledge resources like lexical knowledge, syntactic knowledge, world knowledge and semantic knowledge. This paper presents a new knowledge-based coreference resolution model using neural network architecture. It uses XLNet embeddings as input and does not rely on any syntactic or dependency parsers. For more efficient span representation and mention detection, we used entity-level information. Mentions were extracted from the text with an unhand engineered mention detector, and the features were extracted from a deep neural network. We also propose a nonlinear multi-criteria ranking model to rank the candidate antecedents. This model simultaneously determines the total score of alternatives and the weight of the features in order to speed up the process of ranking alternatives. Compared to the state-of-the-art models, the simulation results showed significant improvements on the English CoNLL-2012 shared task (+6.4 F1). Moreover, we achieved 96.1% F1 score on the n2c2 medical dataset.
A Study on Microarchitectural Covert Channel Vulnerabilities in Infrastructure-as-a-Service

Microarchitectural cross-VM covert channels are software-launched attacks which exploit multi-tenant environments’ shared hardware. They enable transmitting information from a compromised system when the information flow policy does not allow to do so. These attacks represent a threat to the confidentiality and integrity of data processed and stored on cloud platforms. Although potentially severe, covert channels tend to be overlooked due to an allegedly strong adversary model. The literature focuses on mechanisms for encoding information through timing variations, without addressing practical considerations. Furthermore, the field lacks a realistic evaluation framework. Covert channels are usually compared to each other using the channel capacity. While a valuable performance metric, the capacity is inadequate to assess the severity of an attack. In this paper, we conduct a comprehensive study on the severity of microarchitectural covert channels in public clouds. State-of-the-art attacks are evaluated against the Common Vulnerability Scoring System in its most recent version (CVSS v3.1). The study shows that a medium severity score of 5.0 is achieved. In comparison, the SSLv3 POODLE (CVE-2014-3566) and OpenSSL Heartbleed (CVE-2014-0160) vulnerabilities achieved respective scores of 3.1 and 7.5. As such, the paper successfully demonstrates that covert channels are not theoretical threats, and that they require the immediate attention of the community. Furthermore, we devise a new and independent scoring system, the Covert Channel Scoring System (CCSS). The scoring of related works under the CCSS shows that cache-based covert channels, although more and more popular, are the least practical ones to deploy. We encourage authors of future cross-VM covert channel attacks to include a CCSS metric in their study, in order to account for deployment constraints and provide a fair point of comparison for the adversary model.
On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data

A regression problem with dependent data is considered. Regularity assumptions on the dependency of the data are introduced, and it is shown that under suitable structural assumptions on the regression function a deep recurrent neural network estimate is able to circumvent the curse of dimensionality.
UAV aerial image mosaic algorithm based on FAST-Tomasi feature and Delaunay triangulation

In view of the current inability to meet the requirements of fast speed and strong stability in feature extraction, and the slow speed in feature point elimination, this paper proposes an UAV remote sensing image mosaic algorithm based on FAST-Tomasi features and Delaunay triangulation. Firstly, FAST feature detection algorithm is used to accelerate Shi-Tomasi algorithm to obtain fast and accurate feature points, the feature points are described by BRIEF operator, and the phase is conducted by hamming distance. Then, Delaunay triangulation network is constructed according to the initial matching points, and the similarity between the triangles in the two images to be stitched is calculated. The triangle pair with large similarity is the matching triangle pair, so the corresponding accurate matching points are obtained. Then, the improved SPHP algorithm is used for registration. Finally, the weighted algorithm is used to fuse the image. Experimental results indicate that the algorithm retains more correct matching points, the accuracy of eliminating false matching points is higher, and the matching speed is significantly improved.
An Auto Question Answering System for Tree Hole Rescue

This paper introduces an automatic question answering system which aimed to provide online how-to instructions for volunteers of Tree Hole Rescue–a Chinese online suicide rescue organization. When a volunteer needs to make sure how to deal with a rescue task professionally, he/she could ask this system via its WeChat public account other than reading a rescue instruction menu book. Firstly, a Tree Hole Rescue question-answer knowledge graph was constructed to manage Tree Hole Rescue question-answer knowledge and its relationship. Then, based on this semantic technology, a question in Chinese natural language was parsed into a machine-readable logical language through the question preprocessing and entity mapping process. And, a candidate question set was generated through a hierarchical information retrieval strategy. Finally, an exact or close answer and recommended similar questions were sent to the asker after calculating words sequence similarity via an algorithm which combined word form and semantic features. If the system could not match an answer for a question, the question would be added to unsolved question list and the system would alert administrator to deal with it. System testing shew that the Q&A system has a high accuracy rate in response of Tree Hole Rescue questions. Meanwhile, this system provides a series of methods to improve the update capability of the Q&A library and the scalability of the system.
Location Sensitive Image Retrieval and Tagging

People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging.
Innovative Approaches for Avant-Garde Artifacts

In the last decade, the society already regenerated their cognition by collective consciousness that leads humanity to moral attitudes and in a responsible manner upon global concerns. Likewise, as in various industries, the designers also have set up a sensitive approach through sustainability and empowerment. The new generation designers are the current change-makers that are shaping the future. For enhancing design innovation, current technology augments the creativity along with diversity and eligibility of applied techniques, and it enables the designer to work interdisciplinary. Any context or inspiration that stands in the edges of creativity is not out of the realm of possibility, only if it covers up social, environmental, or cultural concerns in harmony. To achieve distinctive results, the empirical approaches of design should be encouraged for solution-oriented and revolutionary projects. Therefore, this chapter discusses in-depth why and how designers should align with the integrity of art, engineering, science, technology, and sociology for holistic design-thinking.
Multi-directional Rule Set Learning

A rule set is a type of classifier that, given attributes \(X\), predicts a target \(Y\). Its main advantage over other types of classifiers is its simplicity and interpretability. A practical challenge is that the end user of a rule set does not always know in advance which target will need to be predicted. One way to deal with this is to learn a multi-directional rule set, which can predict any attribute from all others. An individual rule in such a multi-directional rule set can have multiple targets in its head, and thus be used to predict any one of these. Compared to the naive approach of learning one rule set for each possible target and merging them, a multi-directional rule set containing multi-target rules is potentially smaller and more interpretable. Training a multi-directional rule set involves two key steps: generating candidate rules and selecting rules. However, the best way to tackle these steps remains an open question. In this paper, we investigate the effect of using Random Forests as candidate rule generators and propose two new approaches for selecting rules with multi-target heads: MIDS, a generalization of the recent single-target IDS approach, and RR, a new simple algorithm focusing only on predictive performance. Our experiments indicate that (1) using multi-target rules leads to smaller rule sets with a similar predictive performance, (2) using Forest-derived rules instead of association rules leads to rule sets of similar quality, and (3) RR outperforms MIDS, underlining the usefulness of simple selection objectives.
Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data

Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data structures are obtained when high-throughput transcriptome sequencing data are collected for n genes across p conditions at r occasions. Matrix variate distributions offer a natural way to model three-way data and mixtures of matrix variate distributions can be used to cluster three-way data. Clustering of gene expression data is carried out as means of discovering gene co-expression networks.In this work, a mixture of matrix variate Poisson-log normal distributions is proposed for clustering read counts from RNA sequencing. By considering the matrix variate structure, full information on the conditions and occasions of the RNA sequencing dataset is simultaneously considered, and the number of covariance parameters to be estimated is reduced. We propose three different frameworks for parameter estimation: a Markov chain Monte Carlo-based approach, a variational Gaussian approximation-based approach, and a hybrid approach. Various information criteria are used for model selection. The models are applied to both real and simulated data, and we demonstrate that the proposed approaches can recover the underlying cluster structure in both cases. In simulation studies where the true model parameters are known, our proposed approach shows good parameter recovery.The GitHub R package for this work is available at https://github.com/anjalisilva/mixMVPLN and is released under the open source MIT license.
Some ways to solve problems arising in the process of using information and communication technologies in teaching physical and mathematical disciplines at the pedagogical university

The article deals with the use of information and communication technologies (ICT) in the educational process of the Pedagogical Universities, in particular, in teaching physics and mathematics including distance learning. The questions that the students ask are studied. The purpose and tasks of the study of this issue, conducted by the authors, and its scientific novelty are formulated. Based on the analysis of modern scientific sources, and on personal experience of the authors, the positive and negative aspects of the use of ICT in the training of future teachers are analyzed. The authors have put forward proposals on improving the effectiveness of the use of ICT in the educational process of the pedagogical university, and they also indicate how the teacher can contribute to this process. The article lists the actions in which the active position of the teacher is very important. It describes the results and prospects of further research and work in this direction, in particular, the active participation in the formation and optimization of the electronic educational environment of the University.
A Case Study of Improving a Very Small Entity with an Agile Software Development Based on the Basic Profile of the ISO/IEC 29110

Nowadays the agile methodologies are gaining attention from the software development market in several ways because they are oriented to satisfy the needs of the businesses. Agile methodologies such as Scrum, Lean, and XP are among the most popular. Some organizations have implemented them in their processes, using the sprints of Scrum to distribute, and monitor the workload of the project. Many organizations have problems adapting these concepts because a common thinking is that agile is fast, avoiding work products, processes, tasks or even roles, causing more troubles than benefits among the team. This paper shows an overview of a guide, which was created based on agile methodologies and the Basic profile of the ISO/IEC 29110, as well as a case study performed in an organization to validate the benefits of using this guide. Besides, the results obtained were compared with the results of improvements in agile methodologies proposed by other authors.
A Combined Linguistic-Movement Model based Bengali Text Entry Method for Physically Handicapped People

Placement of alphabets in any keyboard layout requires extensive analysis on several key features of that specific language. Vocabulary size, the correlation between alphabets and punctuation marks, user group based on cultural diversity and many other issues often make the layout optimization method extremely challenging. And when it comes to generalizing the solution for a user group who need to access buttons via feet due to physical disability, additional parameters like assuring minimum movement and analyzing letter frequency chart while placing keys needs to be considered with utmost precision. In this paper, we propose a keyboard layout for Bengali language-speaking physically handicapped group with a vision to assist them in achieving maximum typing speed with less typing error occurrence.
Security Enhancement of Smart Home System using Signature Recognition on Raspberry Pi

Signatures play a crucial role in human life as part of their identity. Nowadays, there is a growing interest in the smart home system using the Internet of Things (IoT). Furthermore, signature recognition and verification can play essential roles in finance, banking, home system, insurance, and others. This paper’s main objective is to design and implement a signature recognition system on a single board computer, i.e., Raspberry Pi 3 equipped with an LCD touchscreen. First, the acquired signature image was cropped and resized. Next, a binary image was extracted as features to train the artificial neural network (ANN). The trained ANN was used to classify the input signature to determine whether the signature is genuine or forged. Results showed that the recognition rate of 99.77% was achieved using a confidence level threshold of 85% during testing.
A Low footprint Automatic Speech Recognition System For Resource Constrained Edge Devices

Deep Learning (DL) has been instrumental in pushing artificial intelligence (AI)/ machine learning (ML) algorithms to edge of the network. It allows building AI/ML algorithms for computer vision, speech processing, and other timeseries analytics tasks with limited domain knowledge. As there is no mechanism to control the representations learned from a large dataset, it becomes hard to predict whether a very small DL model can learn the proper dependencies needed for a particular problem at hand.
CHAT SPI: Knowledge Extraction Proposal Using DialogFlow for Software Process Improvement in Small and Medium Enterprises

The development of the software products and services within organizations generates a large amount of information that is not registered and is generally known as tacit knowledge. This knowledge is the most valuable information for an organization since knowledge helps to improve and avoid repetition of errors. This search is even more accentuated in the organizations called SMEs: (Small and Medium Enterprises), since they do not have a culture of processes and as a result lack processes. Therefore, the large growth of this information in the SMEs generates the need to create a platform which automates the knowledge extraction. To achieved this, this article presents a proposal to extract knowledge using DialogFlow for Software Process Improvement in SMEs. The proposal presents a modification in how DialogFlow works and the establishment of a knowledge repository based on ISO/IEC 29110. This modification allowed to develop a CHAT SPI.
A unified analysis of stochastic gradient‐free Frank–Wolfe methods

Abstract In this paper, we consider a class of constrained stochastic programs in which the gradient of the random function in the objective and the projection onto the feasible set are assumed to be prohibitive to compute. We propose a class of mini‐batch gradient‐free Frank–Wolfe methods. Under mild conditions, we conduct a unified convergence analysis for both convex and nonconvex problems.
Matching Images On The Face Of A Buddha Statue Using The Scale Invariant Feature Transform (SIFT) Method

The condition of the statue in Indonesia at this time many are missing, especially the face of the statue. 504 Buddha statues in the Borobudur temple, there are 248 statues which are now in a headless condition. The condition of the lost statue face is due to being stolen, still buried in the area around the temple, or has changed hands to another party. To find the statue's head according to the face, a matching image is needed, one of which uses the sift method. Buddha face statue matching can be done based on the characteristics of hair, eyebrow shape, lips, head shape or attributes found on the head of the statue. The image matching method can describe an image with a specific keypoint using Scale Invariant Feature Transform (SIFT), where each keypoint has a gradient orientation (GO) and gradient magnitude (GM) which are processed into features in the image registration process. The purpose of this study is to match the face images of Buddha statues with different angles. This study aims to match the face image of the original statue with the image of the statue chosen from different angles. The results of this study are that the same image with different angles obtains 90% accuracy.
Embedding Compression with Right Triangle Similarity Transformations

Word embedding technology has promoted the development of many NLP tasks. However, these embeddings often require a lot of storage, memory, and computation, resulting in low efficiency in NLP tasks. To address the problem, this paper proposes a new method for compressing word embeddings. We sample a set of orthogonal vector pairs from word embedding matrix in advance. Then these vector pairs are fed into a neural network sharing weights (i.e., Siamese network), and low-dimensional forms of the vector pairs are obtained. We get two vector triplets by adding the subtraction results of the vector pairs, respectively, which can be regarded as two triangles. The neural network is trained by minimizing the mean square error of the three internal angles between the two triangles. Finally, we extract its shared body as a compressor. The essence of this method is the right triangle similarity transformation (RTST), which is a combination of manifold learning and neural networks. It is distinguishable from other methods. The orthogonality in right triangles is beneficial to the compressed space construction. RTST also maintains the relative order of each edge (vector norm) in triangles. Experimental results on semantic similarity tasks reveal that the vector size is 64% of the original, while the performance is improved by 1.8%. When the compression rate reaches 2.7%, the performance drop is only 1.2%. Detailed analysis and ablation study further validate the rationality and the robustness of the RTST method.
Development of Indonesian Language Speech Recognition Algorithm Model in Knowledge Database

Research on humanoid robots in Indonesia is developing very rapidly, marked by research on humanoid robots having the ability to learn independently using Indonesian and vocabulary to receive and provide responses from humans based on Natural Language Processing. The general objective of research focuses on speech recognition systems to be able to interact with people naturally. Humanoid robots must be able to consider the question style and infer answers through automated, unreliable conversations in noisy environments. In development, there are still shortcomings that is if the answer is not in the knowledge base, the robot will look for the context that is most similar to the knowledge base, instead of giving an answer, the robot gives an answer `not found'. Sometimes, the wrong answer will be displayed because the cosine value between the question and the wrong answer is influenced by the same context, that the truth is not the actual answer. Based on these problems, this study was conducted with the aim of producing structured data and information knowledge according to the topic of the question, producing knowledge storage designs, optimizing models based on design and knowing the performance of knowledge representation storage models. Based on these objectives, this research will design algorithms and modeling representation of Indonesian language knowledge that will be applied to humanoid robots. The expected output is an algorithm design and storage of knowledge representation on humanoid robots.