Reviewerly/exHarmony huggingface dataset:
Text Tiling Text Segmentation Based on Hierarchical Dirichlet Process Model

Abstract Text segmentation plays an extremely important role in many areas such as abstract extraction and information retrieval. Topic model is one of important methods in text segmentation. However, the current methods based on topic model generally rely on the manual setting of the number of topics. In order to improve the efficiency of text segmentation, this paper proposes a TextTiling text segmentation method based on Hierarchical Dirichlet process (HDP) model. Firstly, the method uses the HDP model to obtain a vector representation of the text in the topic space, which can also automatically generate the number of topics. Applying the theme vector to the TextTiling segmentation algorithm can implement text segmentation. The results show that the method can effectively improve the performance of text segmentation, and be free from manually set topics.
Detection of Inappropriate Anonymous Comments Using NLP and Sentiment Analysis

The world became interactive and socially active now-a-days because of the increase in different types of content sharing applications. These content sharing applications are social media platforms which provide various features so that users can effectively interact and share their thoughts and ideology. One such platform is a discussion forum which promises the anonymous posting of user’s views and complaints. The spammers target the forums as the craze of the forums increase. Though these platforms act as medium of knowledge sharing, all of the users don’t use these platforms for a positive cause. They are also being used to abuse or bully targeted people taking advantage of their anonymous feature. Spamming and cyber bullying has grown rapidly to a limit that social media is being termed harmful. By reading spam and vulgar comments, readers will be diverted. Main aim is to detect these bad comments which are vulgar, inappropriate or not related to the specific context. The research is not based on the static contents but it live streams the comments and the entire research is being done. The research is based on NLP, Sentiment calculation and topic detection.
Embedded Face Analysis for Smart Videosurveillance

In this paper, we describe our methodology for designing a smart Videosurveillance system for face analysis. The system aims at increasing the security by gathering demographic statistics in highly crowded areas such as train stations, airports and shopping malls. Based on Convolutional Neural Networks (CNNs), the system architecture relies on the reconfigurable hardware to accelerate part of the computation and reduce the power consumption compared to general-purpose processors and GPUs. To achieve easy programmability, the platform makes use of the OmpSs programming model, which provides parallelization and acceleration by using simple directives to be added to the sequential code. The rsource-intensive tasks are offloaded to the reconfigurable hardware in order to achieve the desired performance levels. Our evaluation shows that we can detect more than 600 faces per frame, while keeping the power consumption at about 8W. The tests were performed by using the AXIOM hardware/software platform.
Author Impression on Dependent Authors Using Wordtovector Method

This paper provides an introduction to types of authorship analysis which is important in many applications of NLU, QA, Plagiarism detection etc. Author profiling helps to identify the traits of an author in the given text/texts, which finally leads to predict whether those traits are present in other text that reflects the important characteristics of the original author. The original author normally observed to have some sort of his impact or impression on dependent writers or authors The main focus of this paper is identifying the weight of impact of original author on dependent author. Word to vector technique is been used in this work to identify impact of original author on dependent authors.
Facilitating Cluster Counting in Multi-dimensional Feature Space by Intermediate Information Grouping

Previously, we showed that dividing 2D datasets into grid boxes could give satisfactory estimation of cluster count by detecting local maxima in data density relative to nearby grid boxes. The algorithm was robust for datasets with clusters of different sizes and distributions deviating from Gaussian distribution to a certain degree. Given the difficulty of estimating cluster count in higher dimensional datasets by visualization, the goal was to improve the method for higher dimensions, as well as the speed of the implementation. The improved algorithm yielded satisfactory results by looking at data density in a hypercube grid. This points towards possible approaches for addressing the curse of dimensionality. Also, a six-fold boost in average run speed of the implementation could be achieved by adopting a generalized version of quadratic binary search.
Effects of writing instruments and constraints on signatures

Writing instruments, writing, substrates, and writing conditions can influence the quality of a signature. Features caused by these influences can lead to error if they are misinterpreted by the examiner. The FDE should consider these factors when conducting a signature examination.
Computer Evidence Analysis Technology Based on Weighted Frequent Pattern Growth Algorithm

The current analysis of computer forensics is still dependent on the investigation personnel, leading to the problem of heavy workload and low efficiency. At the same time, computer evidences have the characteristics of complex structure and large amount of data, which is prone to the problem of association rules redundancy. In order to improve the efficiency of computer evidence analysis, this paper uses association analysis technology to analyze the user’s behavior habits from the obtained computer system log, browser records, file operation traces and other information; combined with the idea of weighted features, the weight set was generated according to the difference of attribute importance, and the Frequent Pattern Growth (FP-Growth) algorithm is improved. The experimental results show that the improved FP-Growth algorithm can effectively remove the redundant rules in evidence analysis, and is more practical for user behavior analysis.
The Rate of Return on Investment in Africa

In this chapter, we provide an aggregate overview of the return on investment in Africa, studying how it changed over time and by sector of operation. We compare our results to previous estimates for the same geographical region and time period in order to test the reliability of our estimates. The results from these comparisons show that our figures match such previous estimates well. We also compare our results to previous estimates for other parts of the world in order to draw conclusions about the rates of return on investing in Africa during the period that we study. The results indicate that the return on investment in Africa was slightly elevated on an aggregate level compared to the return on investment elsewhere in the world. There were also important differences in the return on investment over time and between different regions in Africa.
An Eye Tracking Study: What Influences Our Visual Attention on Screens?

This study investigated the influence of language orientation on visual attention and recalled information using eye tracking technology. A total of 37 bilingual participants completed the study. All participants’ native language orientation was from right to left such as Arabic, Urdu, Farsi and Hebrew, but all resided in the US. Participants speaking Arabic as their native language were assigned to Group1 (N = 20), otherwise they were assigned to Group2 (N = 17). Group1 received the instructional material in Arabic and Group2 in English. The study consisted of three parts: Word screen, Image screen and Sequence screen. An eye tracking devise was used to measure first visual fixation indicating the region (right or left) where the user’s attention was on the screen for all three parts. Afterwards, the participants were asked to recall three words, three images and explain the sequence process of the images presented. One-way ANOVA was used to assess the relationship between the visual fixations with the recalled answered orientation for both groups. Significance was found in visualizing words, images and the sequence process. Results indicate initial visual fixation is influenced by language orientation when reading the instruction of questions as the case in Group1, or the influence of language orientation of the country of residence as for Group2. This can help designers, developers and instructors when designing to get the international users’ attention.
A Secure User Authenticated Scheme in Intelligent Manufacturing System

In recently years, intelligent manufacturing system is getting grown up. There are many manufacturing vendors that investigate to develop their intelligent manufacturing systems in their product line. Our government also promotes some cities of mid Taiwan to become intelligent cities in the future. However, we discover that there are no such stand cryptography modules or security modules in these systems and there are a lot of papers that indicated security problems in intelligent manufacturing system. When a legimate user attempts to login an intelligent manufacturing system, an attacker may pretend as a legal server to perform mutual authentication with this user. On one hand, the server also faced the same problem. That is called the men-in-the-middle problem. On the other hand, an attacker may lunch software to capture some users’ login passwords. If a user’s login password does not to be protected by security mechanisms of the intelligent manufacturing system, the attacker may login this system with captured passwords successfully and fetch secret data in advance. Besides, there are no such provable user authentication schemes for the intelligent manufacturing system in random oracle model. Due to above problems, we proposed a secure user authentication scheme for intelligent manufacturing system. Not only this scheme does not store each user’s password in its database table, but also each user can change his/her own password after logging in successfully. Finally, we also give formal security proof in the random oracle model of the full version of this paper.
Developing Ontology of Properties and Its Methods of Use

Developing ontology of properties and units of measurement includes fundamental properties links and units of measurement, as well as links that show the formation of concepts. General accepted metrology definitions used as the basis of ontology construction. Semiotic ontology nature taken into account: ontology includes the signs system – appellations, designations, linguistic constructions that represent information in a communicative form, as well as concepts, measures, relations that allow modeling the reality. Examples of ontology applying in the consistent representation of objects parameters analysis are shown and their using in automatic properties and values extractions from texts.
Aircraft Detection in Remote Sensing Images Based On Deep Convolutional Neural Network

Aircraft detection in remote sensing images is always the research hotspot but a challenging task for the variations of aircraft type, pose, size and complex background. In the paper, we propose a region-based convolutional neural network to detect aircrafts. To enhance the learning ability of the network, a mult-resolution aircraft remote sensing dataset is collected from Google Earth. Then, the detection model is trained end to end by fine-tuning on the obtained dataset and realizes automatic aircraft recognition and positioning. Experiments show that the proposed method outperforms state-of-the-art method on the same dataset and the requirement for real-time can be satisfied simultaneously.
The Relationship Between Usability and Biometric Authentication in Mobile Phones

It is estimated that over 1 billion people are active mobile phone users in 2018. When using a mobile phone, there are a variety of ways to authenticate and “secure” the device, and biometric authentication is becoming an increasingly common way to do this, however, biometric authentication is not always as usable as it could be. Both usability and security are important, yet many people believe that there is a trade-off between the two. The focus of this paper was to better understand usability, computer security, and within computer security more specifically biometric authentication, and how all three can work together to create systems that are both usable and secure. A survey and interviews were conducted based on previous research to understand perceptions from the general population, usability experts, and security/biometrics experts. The results do indicate that there is indeed a perceived trade-off between usability and computer security.
Static Gesture Recognition Method Based on 3D Human Hand Joints

Depth cameras support working in a dark environment, and provide depth information from objects to cameras, hence have advantages over color cameras. So in this paper we adopt depth cameras to collect accurate gesture information for 3D modeling, in order to obtain accurate gesture recognition. On the depth map, we present methods of hand joint segmentation with random forest pixel classification and of gesture recognition with template matching, which provides accurate judgment for static gestures. Rotation may occur while the acquisition of hand data, so we conduct rotation correction by using SVD decomposition. Experimental results illustrate that this method provides more accurate joint segmentation, which is robust to hand rotation and achieves a recognition rate of 94.8% on ASL dataset.
Research on Lightweight and Fast Image Style Transform Algorithm

Abstract Image style transform technology refers to the use of a convolutional neural network to extract the style of a famous painting, thereby converting the input image into a corresponding style image. The current methods are mainly divided into two types, namely, the style transform method proposed by Gatys and the fast image style transform method (FNST) proposed by Li Feifei. The common problem between the two methods is that the hardware requirements are high, which is not conducive to a wide range of popularization and application. In order to use image style transform on more terminals and even mobile phones, research h on lightweight and fast image style transform is of great significance. This paper draws on the deep-wise split convolution in MobileNets to prun the convolutional layer in the loss network. After the pruning, the parameter content is only 11% of the original model; the residual component in Image Transform Net is trimmed. The parameter content was reduced by 96% compared to the original model. However, the amount of parameters in the process of weight reduction is greatly reduced, which may lead to the degradation of the stylized effect. It is proved by experiments that the style degradation of the method is not obvious, that is to say that the performance loss caused by the reduction of the parameter quantity is acceptable.
Multi-recursive Wavelet Neural Network for Proximity Capacitive Gesture Recognition Analysis and Implementation

This paper presents a multi-recursive wavelet neural network (MRWNN) with proximity capacitive gesture recognition. Recently, the capacitive sensor technologies have been developed for proximity methods that sensing electronic varies around sensor detection point, but the user gesture signals are time-variant. The MRWNN have multi layers recursive weight to record last signal variation, and we utilize microcontroller with MRWNN to identify algorithms and implement proximity capacitive gesture recognition. Moreover, we show MRWNN weight convergence analysis of the MRWNN signal identifier. In the experimental results, we show MRWNN can recognize patterns of different gesture signal accurately and reliably. In addition, we use wearable device combined with BLE (Bluetooth Low Energy) feedback output response immediately.
Automated design of random dynamic graph models for enterprise computer network applications

Dynamic graphs are an essential tool for representing a wide variety of concepts that change over time. In the case of static graph representations, random graph models are often useful for analyzing and predicting the characteristics of a given network. Even though random dynamic graph models are a trending research topic, the field is still relatively unexplored. The selection of available models is limited and manually developing a model for a new application can be difficult and time-consuming. This work leverages hyper-heuristic techniques to automate the design of novel random dynamic graph models. A genetic programming approach is used to evolve custom heuristics that emulate the behavior of real-world dynamic networks.
Closed Timelike Loops

In the insane vortex of the black hole interior, then, time’s arrow is twisted like a pretzel. If intelligence in the distant future ever learns to ride those currents—perhaps by the deliberate construction of a naked singularity, or a Tipler time gate (see below)—time travel may become commonplace. The ultimate absurdity of science fiction will be a matter of mundane fact.
Research on primary school Chinese character recommendation algorithm based on collaborative filtering

Collaborative filtering is to calculate the similarity between users by taking all the preferences of a user as a reference value. After finding adjacent users, the information mining of relevant recommendations can be realized through similar behavior history. This paper proposes a Chinese character recommendation algorithm based on user collaborative filtering technology by digging deeply into the recorded information of students' Chinese character learning.
An Implementation on Agriculture Recommendation System Using Text Feature Extraction

Improvement of organizations on the web is extending ordinary as needs be benefits related data end up being too tremendous to even think about even consider preparing by traditional data taking care of systems. In the midst of the period of broad volume of data, there happens a potential for making quicker advances in different savvy teaches and updating the favorable position and accomplishment of different undertakings through better assessment of the general volumes of information that are persuading the chance to be accessible. Early strategies like substance based recommendation lost its significance and the model setting pleasing separating approach gets its suitability in all spaces. To play out a predominant quality information recommendation on agribusiness, information based shared detaching techniques are being utilized in the midst of the present days. The work prompts the decay of tremendous instructive rundown into littler illuminating record in which the majority of the tendencies take after each other. In this paper a blend revamp recommender structure subject to client information is proposed which diminishes the general include time in generous data suggestion. Unmistakable obstructions in standard recommender structures like information sparsity, cool begin issue have been overwhelmed in this proposed framework.
EvoIsland

We present EvoIsland, an interactive evolutionary augmented reality interface for parametric 3D OpenSCAD models. Our mobile prototype enables content creators to explore the full range of design possibilities encoded in a model's source code through the combination and separation of hexagonal evolutionary tiles embedded with genetic data. As these tiles are grouped into islands, localized clusters of design populations emerge for creators to explore. Interactions that take place within our EvoIsland prototype provide content creators with a novel approach for shaping evolutionary populations in an immersive environment.
Online Chinese Handwriting Recognition with Time Sequence Information

Handwriting recognition has been a heated topic over years. Due to the development of deep learning, a lot of research has been done to apply convolutional neural network (CNN) model to this task, which have achieved outstanding accuracy. Instead of focusing merely on CNN models, this article takes the features of Chinese handwritten character into consideration and manages to extract the information of the stroke order of the online handwriting recognition. To achieve this, two methods are proposed: (1) Design a two-branch model combining CNN and recurrent neural network (RNN);(2) Give a new channel division strategy. Also, the task of advanced prediction of the character which little research has been worked on is the key point. With the information of stroke order and some data augmentation strategy, methods proposed have achieved satisfying results.
A Method for Determining Access Node of Microwave Relay Network Based on Genetic Algorithm

Abstract In order to improve the deployment efficiency of the microwave relay network, a method for determining access node of microwave relay network based on genetic algorithm is proposed in this paper. System analysis of network structure characteristics, node attributes and deployment process, the access node deployment issues is transformed into continuous area location issues. After discretization the area, the set of candidate access nodes is obtained considering boundary points and boundary chords. Comparing the coverage set of each access node, the candidate set is de-redundancy. Then taking coverage ratio maximum and the deployment cost minimum as the optimization goal, the optimal set of access nodes is obtained from candidate set by using genetic algorithm. A case with 50 user terminals is simulated by our method. Results show that after de-redundancy and genetic algorithm calculation, the number of access nodes is optimized from 240 to 17, and only 9 user terminals are repeated covered by different access node.
An Approach to Deep Learning Service Provision with Elastic Remote Interfaces

Deep learning has been widely applied for computer vision, natural language processing, and information retrieval etc. Using a deep learning framework can reduce learning curve of beginners facilitating them to get involved with deep learning algorithms. Current deep learning frameworks can mainly be divided into traditional local deployment and cloud-based platforms. However, the two forms cannot be considered at the same time in terms of debugging and remote access. This paper focuses on the logical isolation between deep learning algorithm design and actual business execution, and it proposes an elastic framework that can resolve the contradiction between internal improvement and external access, which can improve the efficiency of both algorithm design researchers and business requirements department engineers.