Reviewerly/exHarmony huggingface dataset:
Web text data mining method based on Bayesian network with fuzzy algorithms

With the advent of Web 3.0 era, the number and complexity of Web pages in Bayesian networks have shown an explosive growth trend. Accompanying this is the geometric growth of information contained in Web pages. Web text data in Bayesian networks usually hide rich knowledge and rules of user value. However, due to the semi-structured, real-time and discrete characteristics of Web text data, it is difficult for users to obtain the knowledge they need directly from such complex data sets. The emergence of fuzzy mathematics provides a good research idea and method for solving such problems. It can use the idea of fuzzy mathematics to analyze the practical problems in text data. Therefore, how to effectively mine the Web text data information and knowledge that users really care about from Bayesian network, and present it in a way that users can understand, it is a very popular research topic at present. In this paper, we select the text of Bayesian network: microblog data for experiments. User data model of microblog is established by using relevant knowledge of fuzzy theory. The concept of fuzzy measure is introduced to calculate the non-additive measure value under the interaction relationship between the detection indicators. Determine the membership function relationship between the detection user and the text data, calculate the integral values of Choquet integral, Sugeno integral and Wang integral of the membership function under the non-additive measure, the final valuable web text data is judged by integral value. On the basis of the above research contents, the research results of Web text mining technology and fuzzy arithmetic mathematics are combined, design and implement information acquisition and analysis for Bayesian network community. The recall rate obtained by the experimental method in this paper is as low as 4%, and tends to be more stable.
A CNN-Based Tool to Index Emotion on Anime Character Stickers

Anime character stickers consist of a detailed illustration of characters that represents emotions. Generally, message apps let users save received stickers, but in some cases, the task of manual searching for a specific sticker may be frustrating when a large amount of them are stored. In this work, we propose a CNN-based tool for emotion indexing of message stickers. We built a dataset with 12.668 labeled stickers with 3 classes (Sad, Happy and Angry). In experiments, our model achieves 84.01% of global f1-score. Additionally, we describe our proposed tool for emotion indexing of message stickers that has three main functionalities: (1) sticker recommendation function that classifies stickers and recommends the class desired; (2) filter function that takes out the stickers it considers mislabeled; (3) ranking function that orders the labeled stickers.
Why Spiking Neural Networks Are Efficient: A Theorem

Current artificial neural networks are very successful in many machine learning applications, but in some cases they still lag behind human abilities. To improve their performance, a natural idea is to simulate features of biological neurons which are not yet implemented in machine learning. One of such features is the fact that in biological neural networks, signals are represented by a train of spikes. Researchers have tried adding this spikiness to machine learning and indeed got very good results, especially when processing time series (and, more generally, spatio-temporal data). In this paper, we provide a possible theoretical explanation for this empirical success.
HD-MAABE: Hierarchical Distributed Multi-Authority Attribute Based Encryption for Enabling Open Access to Shared Organizational Data

Attribute Based Encryption (ABE) has emerged as an access control technique for sharing data in cloud environment. In systems using ABE, the data owners specify the recipients for their data on the basis of attributes carried by them. The users obtain the attributes from appropriate authorities. In the context of open access systems, data pertaining to an organization is used within the organization by the internal members and at the same time, it can be shared with the users outside the organization. Sometimes, such shared data could be of critical importance. For example, medical data of a patient owned by a hospital could be important for a doctor to provide him suitable treatment. In this case, any authorized doctor should be able to treat the patient. Current ABE based schemes exhibit some limitations in addressing attribute management and policy formation issues for open access systems. In this paper, a Hierarchical Distributed Multi-Authority Attribute Based Encryption (HD-MAABE) scheme is proposed, where the organization and standard attribute authorities issue the attributes. Internal users of the organization get their attributes issued from the organization and attribute authorities arranged in a hierarchy are responsible for issuing valid attributes to the users external to the organization. Arrangement of attribute authorities in a hierarchy makes the system easily scalable, as attribute authorities can be added through a simple attribute delegation mechanism. This also makes it possible to issue attributes in a distributed manner.
Building Language-Agnostic Grounded Language Learning Systems

Learning the meaning of grounded language - language that references a robot's physical environment and perceptual data - is an important and increasingly widely studied problem in robotics and human-robot interaction. However, with a few exceptions, research in robotics has focused on learning groundings for a single natural language pertaining to rich perceptual data. We present experiments on taking an existing natural language grounding system designed for English and applying it to a novel multilingual corpus of descriptions of objects paired with RGB-D perceptual data. We demonstrate that this specific approach transfers well to different languages, but also present possible design constraints to consider for grounded language learning systems intended for robots that will function in a variety of linguistic settings.
Research on Kinect-based Gesture Recognition

In the study of gesture recognition, it is often affected by changes in illumination, complex background, and skin color interference. In response to these problems, this paper designed a Kinect-based gesture recognition method. Based on the Kinect somatosensory device, this paper collected the bone information and binary image of the human body, and combines the position of the bone points to obtain the hand image. Then, the feature of the hand image was extracted using the HOG algorithm, and the gesture image was classified using the SVM algorithm. In order to implement this method, we collected 10 digital gestures through Kinect and evaluated the method through experiments. The experimental results show that based on the Kinect somatosensory device, the HOG algorithm and the SVM algorithm can effectively and efficiently recognize gestures, which significantly improves the accuracy of gesture recognition, and the average recognition rate is as high as 98.3%. This is of great significance for the research and promotion of gesture recognition technology.
Deep Hierarchical Single Image Super-Resolution by Exploiting Controlled Diverse Context Features

This paper presents a hierarchical convolutional neural network (CNN) for single image super-resolution (SISR), which exploits the controlled multi-context features. We focus on the method to extract more enriched features than the case of using fixed size kernels. For this, we attempt to bring out the best of the given parameter capacity through the design of some sophisticated networks in a hierarchical manner. First, we exploit the multi-kernel dilated convolution for extracting multi-size contexts from the image and combine them with the proposed trainable parameters. The multi-kernel network with some new pre-and post-processing blocks forms our basic building block. Then the basic building blocks are densely connected with a new feature fusion schemes, which makes the upper level building block. Then, by connecting the upper level blocks, we can use various features which can enrich the representation of the images. In the experiments, it is shown that the proposed method achieves significant PSNR gain compared to recent lightweight models with comparable numbers of parameters.
A Method to Audit the Assignment of Registered Voters to Districts and Precincts

Electoral boundaries are an integral part of election administration. District boundaries delineate which legislative election voters are eligible to participate in, and precinct boundaries identify, in many localities, where voters cast in-person ballots on Election Day. Election officials are tasked with resolving a tremendously large number of intersections of registered voters with overlapping electoral boundaries. Any large-scale data project is susceptible to errors, and this task is no exception. In two recent close elections, these errors were consequential to the outcome. To address this problem, we describe a method to audit the assignment of registered voters to districts. We apply the methodology to Florida’s voter registration file to identify thousands of registered voters assigned to the wrong state House district, many of which local election officials have verified and rectified. We discuss how election officials can best use this technique to detect registered voters assigned to the wrong electoral boundary.
Towards Recommendation in Internet of Things: An Uncertainty Perspective

As a bridge between the physical and cyber world, the Internet of Things (IoT) senses and collects a large amount of user data through different types of devices connected to it. As a general information filtering technology, the recommender systems can help to associate information with each other in the IoT and to recommend personalized services for users. However, in practical applications, the collected data is uncertain due to noise, sensor errors, transmission errors, etc., which in turn affects system performance. In order to solve the data uncertainty problem in the IoT-based recommender systems, we propose a new recommender framework with item dithering. In this framework, the list of recommendations generated by the recommender algorithm is stored in a newly opened storage space for the entire session of the interaction between the user and the system. When the user interacts with the system, the list is pushed to the user after being shaken. Based on the proposed framework, we designed IDither, an item-based dithering and recommendation algorithm to shake out irrelevant items through predetermined indicators, thereby retaining the items required by the user and recommending them to the user. Experiment evaluations on real datasets show that IDither is an effective solution for handling uncertainty in the IoT-based recommender systems. We also found that IDither can be viewed as a list updating tool to increase diversity and novelty.
Physics mobile learning with scaffolding approach in simple harmonic motion to improve student learning independence

A study has been conducted that applies physics mobile learning with a scaffolding approach. The purpose of this study was to determine the level of student learning independence as the effect of the use of physics mobile learning with the scaffolding approach. This research uses pre-experimental with one group Pretest-Postest design. The research subjects used were students of MAN 3 Yogyakarta class X IPA 1 as a modeling class of 32 people and X IPA 4 as an implementation class as many as 30 people. Subjects were selected by purposive sampling technique. Data for learning independence obtained through questionnaires and observation. Quantitative and qualitative analysis techniques are used to evaluate research data. The results showed that (1) The use of physics mobile learning with the scaffolding approach can improve student learning independence. (2) Student learning independence is in high and very high categories for modeling class and implementation class.
Efficient Indexing of Multiple Metric Spaces with Spectra

The widespread of social networks and online channels has increased the capture of large amounts of complex data, such as images and videos, which demand efficient and flexible tools to perform information retrieval. Many existing approaches to retrieve complex data follow the "Query by Similarity" paradigm, using Metric Access Methods (MAMs) to index complex data and speed-up information retrieval. In this context, many descriptors represent complex data using representative features such as color, shape, or texture for images. MAMs were initially designed to index features from complex data using only one descriptor, leading users to build several indexes when more than one descriptor is required. Recent approaches that use different representations in a single index structure suffer from a higher number of distance calculations. In this work, we propose the Spectra MAM, which indexes complex data using several features at once. Spectra integrates several metric spaces and answers queries based on one or more descriptors at once. Moreover, Spectra relies on existing correlations among different spaces to choose the best descriptors to obtain a concise yet accurate indexing space. Thus, it reduces the number of distance calculations, speeding up the query execution, and improving the resulting quality.
Noise Reduction of Segmented Images by Spatio-Temporal Morphological Operations

To realize more effective and reasonable systems for robot navigation, this paper proposes a novel visual navigation scheme for robot navigation that strongly depends on the result of semantic segmentation. The visual navigation scheme showed good performance in some actual scenes. However, the current implementation does not work well if noise regions emerge on a movable area after semantic segmentation. To solve this problem, this paper proposes a novel scheme for noise reduction of segmented images based on three-dimensional morphological operations using spatio-temporal images. Experimental results showed that the number of noise regions could be reduced to zero while keeping the false negative value at 1. In addition, the proposed scheme could remove noise regions even though a smaller number of frames was used for filtering than in the case of the spatio-temporal mode filter previously proposed.
Seamless Personal Authentication using Biometrics

As recent devices are growing in potentiality and accessibility, they offer more necessities in terms of security. With the extensively documented inadequate use of PINs, Continuous Authentication System (CAS) is constructed to overcome the vital issue of secure authentication and usability through biometric-based techniques to seamlessly authenticate the user identity. This work presents a multimodal biometric approach which includes the user information from iris and face. Human face and iris involve rich textural data that is the decisive information for biometric verification. It is very unique and one amongst the foremost precise biometric modalities. However, spoofing methods can be used to imitate or occult user identities and increase the possibility of FRR or FAR. Mostly, the print attack is employed as the impersonate user identities. It is noticed that print attack, independently and in conjunction, can crucially vary the interpersonal and intrapersonal distributions and thus improve the possibility to deceive the CAS. The system is assessed to investigate the performance of providing security and usability. The final outcomes illustrated that the system is capable to supply a 92% lessening in the number of invasive authentication requests needed for high-security applications.
Comparison of Direct and Indirect Approaches for Numerical Solution of the Optimal Control Problem by Evolutionary Methods

The optimal control problem with phase constraints is considered. A new indirect approach of synthesized optimal control is proposed as an alternative to direct methods. A comparative study of direct and indirect approaches is carried out on the problem of optimal control for a small group of mobile robots in the complex environment with phase constraints by evolutionary algorithms. With a direct approach to the numerical solution of the optimal control problem, the control function is searched in the form of piece-wise functional approximation. The indirect approach of synthesized optimal control comes from the engineering practice. Instead of reducing the optimal control problem to the problem of finite-dimensional optimization, we firstly make the object stable relative to some point in the state space by solving an additional task of synthesis of stabilizing control and then we find the coordinates of stabilization points as the desired parameters of optimal control.
Deep Minimax Probability Machine

Deep neural networks enjoy a powerful representation and have proven effective in a number of applications. However, recent advances show that deep neural networks are vulnerable to adversarial attacks incurred by the so-called adversarial examples. Although the adversarial example is only slightly different from the input sample, the neural network classifies it as the wrong class. In order to alleviate this problem, we propose the Deep Minimax Probability Machine (DeepMPM), which applies MPM to deep neural networks in an end-to-end fashion. In a worst-case scenario, MPM tries to minimize an upper bound of misclassification probabilities, considering the global information (i.e., mean and covariance information of each class). DeepMPM can be more robust since it learns the worst-case bound on the probability of misclassification of future data.
Towards Taxonomical-Based Situational Model to Improve the Quality of Agile Distributed Teams

At present, software organizations are developing software products that employ global software development (GSD) teams. Organizations tend to adopt new methodologies for global software development, among which is the use of agile in the GSD industry, which yields both benefits and challenges. However, software development teams do not consider situational needs that delay software delivery, resulting in the late discovery of incompatible assumptions and architecture level rework. In this study, we conduct a systematic literature review (SLR) to identify the situational factors that need to be considered by software development teams before developing a software product. We further present taxonomical classification and comprehensively map the situational factors that impact design development and advancement in the proposed Situational Agile Distributed Development (SADD) model. We propose 18 directed hypotheses against each situational factor that supports our SADD model. In order to evaluate our directed hypotheses, statistical analysis method is used, and the level of confidence of each directed hypothesis is validated. The result of our study confirms that global software development teams are highly reliant on the SADD Model. Our study will largely contribute by devising a multilevel taxonomy of situational factors that elevate the performance of global software development teams. This taxonomical classification will allow to better map the relationships between multiple situational factors and elevate the process of creating a holistic model to handle situational needs in the context of Agile Distributed Software Development (ADSD).
Improving lower bounds on the second-order nonlinearity of three classes of Boolean functions

Boolean functions used in symmetric-key cryptosystems must have high second-order nonlinearity to withstand several known attacks and some potential attacks which may exist but are not yet efficient and might be improved in the future. The second-order nonlinearity of Boolean functions also plays an important role in coding theory, since the maximal second-order nonlinearity of all Boolean functions in n variables equals the covering radius of the Reed–Muller code with length 2n and order r. It is well-known that providing a tight lower bound on the second-order nonlinearity of a general Boolean function with high algebraic degree is a hard task, excepting a few special classes of Boolean functions. In this paper, we improve the lower bounds on the second-order nonlinearity of three classes of Boolean functions of the form fi(x)=Tr1n(xdi) in n variables for i=1,2 and 3, where Tr1n denotes the absolute trace mapping from F2n to F2 and di’s are of the form (1) d1=2m+1+3 and n=2m, (2) d2=2m+2m+12+1, n=2m with odd m, and (3) d3=22r+2r+1+1 and n=4r with even r.
A Light-weight Mobile Education App for 3D Modelling Course Teaching

The 3D modelling course teaching usually needs the usage of the professional software tools, such as SolidWorks, AutoCAD and Maya, which require heavy memory cost and needs to be installed on the PC system. The teaching place and respondents are thus usually limited by this condition. With the popularity of mobile devices in the students' learning behaviors, it is expected to have a 3D modeling education App on the easy-portable smartphone or smart pad, so that the teacher can carry out the 3D modeling teaching easily in the common labs without these professional software or even in the classroom without the PC system. Though most of the mobile devices have been equipped with the touch screen, which allows intuitive inputs for the user via finger gestures, there are still no standard gestures for 3D model manipulation, thus the development of a light-weight tool for 3D modeling and editing for the smart devices is a promising research topic for the 3D modeling course teaching. In this paper, a light-weight mobile App is developed for 3D modeling course teaching by designing a set of multi-touch gesture operations specifically for the smartphone or smart pad. The teachers can lead the students to design 3D models with this App easily and effectively.
Adaptive Frame Interpolation using an End-to-End Deep Net with High Quality Flow Estimation

Video frame interpolation algorithm aims at synthesizing intermediate frame(s) sequence between two consecutive frames, these intermediate frames are both temporally and spatially coherent with input frames and each other. Video frame interpolation is a classic problem in computer vision and has many applications, e.g., frame rate upscaling and slow-motion effect. Most existing approaches are single-frame interpolation and have shown impress performance. However, these approa-ches, which cannot be directly used to synthesize multiple frames at one time, sometimes are user-unfriendly. On the other hand, existing multiple-frame interpolation approaches sometimes lead to higher storage space or computational cost. Therefore, we propose an adaptive variable frame inter-polation method, which evaluates the amount of frame to be generated according to the estimated motion, to reduce the storage space and improve the generation efficiency. In addition, we add edge loss to the loss function, expecting better results.