Reviewerly/exHarmony huggingface dataset:
Research on CNN + LSTM user intention classification based on multi‐granularity features of texts

Mobile communication companies have a large number of online customer interaction records. However, it is difficult for those companies to identify customer appeals and call preferences accurately, which makes it hard to apply massive amounts of data to product services. Firstly, all the texts in the original text library are processed at character-level and word-level granularities, then the character sequence library and the word sequence library are constructed, respectively. The text vector training tool is used to train the sequence library to obtain the text vector model. Secondly, a multi-granularity user intention classification model is constructed, which combines convolutional neural network (CNN) and long short-term memory and includes the text feature of character-level and word-level granularity. The results of single model classification at different granularities are fused according to the corresponding weights to achieve efficient user intention classification. Finally, the model is cross-tested using the k -fold method on the data set provided by China Mobile Communications Corporation and reached 87.6% accuracy in the classification task covering 66 categories of user intentions. Experiments show that the multi-granularity feature fusion model has higher accuracy than single-granularity feature models.
Semiotic Tools in Technology for Promoting Hi-Tech Products in Chinese and Russian Advertising Technical Discourse

Digital society is characterized by hi-tech goods expansion. The purpose of this paper is to analyse the effectiveness of the use of symbolic tools in the technology of promoting hi-tech products in advertising technical discourse (B2C). Content analysis of a total sample of 240 technical advertising texts revealed the functions of semiotic tools - signs of Chinese and Russian national-cultural symbols of color and number - in the strategy of promoting hi-tech products.
A SAT-Based Approach for Index Calculus on Binary Elliptic Curves

Logical cryptanalysis, first introduced by Massacci in 2000, is a viable alternative to common algebraic cryptanalysis techniques over boolean fields. With xor operations being at the core of many cryptographic problems, recent research in this area has focused on handling xor clauses efficiently. In this paper, we investigate solving the point decomposition step of the index calculus method for prime-degree extension fields $$\mathbb {F}_{2^n}$$ , using sat solving methods. We experimented with different sat solvers and decided on using WDSat, a solver dedicated to this specific problem. We extend this solver by adding a novel symmetry breaking technique and optimizing the time complexity of the point decomposition step by a factor of m! for the $$(m+1)$$ th summation polynomial. While asymptotically solving the point decomposition problem with this method has exponential worst time complexity in the dimension l of the vector space defining the factor base, experimental running times show that the presented sat solving technique is significantly faster than current algebraic methods based on Gröbner basis computation. For the values l and n considered in the experiments, the WDSat solver coupled with our symmetry breaking technique is up to 300 times faster than Magma’s F4 implementation, and this factor grows with l and n.
Usability Analysis with Webuse Model in Information System Design in Monitoring Child Growth and Development

Abstract Information system today becomes a core need for any organizations as it can help in data management for resulting in the structured data. Also, the use of information system can assist the organization in minimizing the errors in making a policy. One of organizations implementing such system is POSYANDU or Integrated Service Post that is predominantly in charge of monitoring the child growth and development. In this research, a process of testing the information system was conducted using the WEBUSE (Web Usability Evaluation) model aimed to observe the level of usability of the system. Based on the results of the test, it has been found that the average values of the system were 0.69 indicating that the system was at the Good level of usability. Thus, the system can work in accordance to the needs of the users.
Using of PQWs for solving NFID in the complex plane

Abstract We approximate the solution of the nonlinear Fredholm integro-differential equation (NFID) in the complex plane by periodic quasi-wavelets (PQWs). This kind of wavelets possesses orthonormality properties, the numbers of terms in the decomposition and reconstruction formulas are strictly limited, and the localization is not emphasized. To the best of our knowledge, there are no numerical methods to obtain the solution of the NFID by PQWs. Here, we attempt to obtain the numerical solution of the NFID based on B-spline functions. Finally, the simulation results are shown for three examples.
Unobtrusive camera-based monitoring of behavioural disturbances in patients with dementia living in long term care facilities

The purpose of this project is to develop a platform for behavioural disturbances recognition in patients with dementia (PwD). Using a combination of pose recognition software and object segmentation, the system will identify hazardous behaviours and potentially dangerous objects. The system was able to recognize a number of human actions including: falling, fighting, throwing, and tantrums. Successfully identifiable objects are far more extensive, but notably include dangerous objects such as knives and scissors.
ScaleQC: a scalable lossy to lossless solution for NGS data compression

Abstract Motivation Per-base quality values in Next Generation Sequencing data take a significant portion of storage even after compression. Lossy compression technologies could further reduce the space used by quality values. However, in many applications, lossless compression is still desired. Hence, sequencing data in multiple file formats have to be prepared for different applications. Results We developed a scalable lossy to lossless compression solution for quality values named ScaleQC (Scalable Quality value Compression). ScaleQC is able to provide the so-called bit-stream level scalability that the losslessly compressed bit-stream by ScaleQC can be further truncated to lower data rates without incurring an expensive transcoding operation. Despite its scalability, ScaleQC still achieves comparable compression performance at both lossless and lossy data rates compared to the existing lossless or lossy compressors. Availability and implementation ScaleQC has been integrated with SAMtools as a special quality value encoding mode for CRAM. Its source codes can be obtained from our integrated SAMtools (https://github.com/xmuyulab/samtools) with dependency on integrated HTSlib (https://github.com/xmuyulab/htslib). Supplementary information Supplementary data are available at Bioinformatics online.
Protecting Organization Reputations during a Crisis: Emerging Social Media in Risk and Crisis Communication

Background/Objectives: This study focuses on how social media is used by Kuwaiti organizations as a tool in risk and crisis communication. Methods/Statistical Analysis: It examines how social media affects people’s opinions about the performance of companies, including whether or not people are really influenced by rumors and negative comments about these companies. In particular, the purpose of the study was to investigate how people were affected by available news and information on social media about organization performance. Findings: This study investigates whether people believe and trust information available on social media about organizations, and to what degree they are influenced by news or information regarding these organizations. Finally, we try to explore whether negative comments and rumors on social media platforms have an impact on people’s personal opinions, and to what extent. The participants for this study were a sample of Kuwaiti citizens living in the state of Kuwait. A random sample of 382 men and woman from different ages who use social media was obtained through an online survey (Survey Monkey). In this study, the researcher used an online questionnaire, with a total of 382 Kuwaiti citizens initially approached for responses, which were then analyzed for research. The results revealed that the majority of the respondents (82%) are at least sometimes affected by news and information available on social media, while 87% believe that social media is a successful tool in maintaining the reputation of a company. 65% of respondents indicated that social media plays a significant role in the development of companies, and 58% are influenced by abusive comments showing up on social media in relation to the companies’ reputation. Application/Improvements: The application of this study indicated that participants preferred to use Instagram as a social media tool, suggesting that this platform has specific features that do not exist on other platforms, such as high-quality photos being available and users being able to return to them whenever they need. Keywords: Business to Consumer and Cronbach’s Alpha, Management Crisis, Social Media
The DELAY Framework: Designing for Extended LAtencY

This paper introduces the Designing for Extended Latency (DELAY) Framework meant to inspire new systems that support social interaction in high-latency settings such as interplanetary communication, intermittent internet access, and time-zone incompatibilities. The framework includes six dimensions: Goal, Communication Genre, Sequencing, Cardinality, Mutability, and Responsiveness. We describe the iterative design process used to create the Framework, as well as three novel prototypes designed to increase social connectedness and social presence in high-latency situations: 1) the InSync app that allows partners to perform activities simultaneously even though they only see proof of their synchronicity later; 2) the After the Beep system that lets users leave IoT messages that are triggered by the recipients; and 3) the Surrogate platform where players play group battle games against "surrogate" artificial intelligence avatars that mimic unavailable individuals. Data from two design workshops validates the usefulness of the framework for generating new solutions to high-latency scenarios.
Traffic sign classification network using inception module

With the rapid development of the automobile industry, the demand for autonomous driving becomes more and more urgent, and the traffic sign recognition technology in autonomous driving is an indispensable technology. This paper proposes a GoogLeNet based convolutional neural network for traffic signs. This convolutional neural network improves each of the underlying Inception Modules and adds the Batch Normalization layer, effectively avoiding over-fitting of the network. We use a sparse structure that conforms to the Hebbain principle to reduce the parameters and improve the generalization ability of the network, which can extract the features of the image more accurately. Meanwhile, the network also reduces the parameters of the full connection layer by 20 times through the continuous two-layer pooling layer, which greatly speeds up the network training. Finally, the network is trained using the GTSRB data set and the classification accuracy rate can reach 98%. At the same time, we also verified the validity of the network on the MNIST dataset and the pneumonia dataset. The classification accuracy rate can reach 100% on the above two datasets. Experimental results on the above data sets show the validity of the convolutional neural network.
Application of YOLOv3 in road traffic detection

In order to calculate the traffic volume of different models at traffic intersections, the problem of target classification of different models of car, bus and truck can not meet the real-time problem. A real-time detection method for traffic flow of different models at traffic intersections is proposed. Through the analysis and experiment of the YOLOv3 (you look only once) convolutional neural network model, the vehicle detection mAP (mean accuracy) value of different models is 87.06%, and the detection speed is 38 frames/s. The experimental results show that the method can effectively detect vehicles with different types of traffic intersections and realize real-time statistics of traffic intersection traffic.
Demo of the EyeSAC System for Visual Synchronization, Cleaning, and Annotation of Eye Movement Data

Eye movement data analysis plays an important role in examining human cognitive processes and perceptions. Such analysis at times needs data recording from additional sources too during experiments. In this paper, we study a pair programming based collaboration using two eye trackers, stimulus recording, and an external camera recording. To analyze the collected data, we introduce the EyeSAC system that synchronizes the data from different sources and that removes the noisy and missing gazes from eye tracking data with the help of visual feedback from the external recording. The synchronized and cleaned data is further annotated using our system and then exported for further analysis.
Eye-tracking study of the celebrity effect on microblogging browsing: an example from Sina microblog

ABSTRACTIn this study, 89 college students were selected as research participants and the eye-tracking method was used to explore their browsing patterns and cognitive processing mechanisms for microblogging pages of celebrities and non-celebrities. Results showed that participants were more interested in the microblogging pages of celebrities than non-celebrities. Different types of participants had different cognitive processing strategies for microblogging pages: frequent bloggers used top-down processing methods to process the microblogging pages and infrequent bloggers used bottom-up methods for more sophisticated processing. Participants also had different degrees of cognitive processing for different regions of the microblogging pages. All participants gave priority to the processing of microblog contents, but the processing of celebrity microblog pages showed the dominant effect of profile picture, and the priority of blogger information was revealed in the processing of non-celebrity microblogs.
Evaluation of the accuracy of winnowing, rabin karp and knuth morris pratt algorithms in plagiarism detection applications

Abstract The unethical behaviour of acts of plagiarism has been a disgrace in the educational realm. Using the internet, people can easily find articles or documents that are relevant to their current work, and simply duplicate the sentences or paragraphs without paraphrasing or giving correct citations. Such action falls into the area of plagiarism. In order to minimize the problem, especially in the educational field, it is necessary to develop plagiarism detection applications. The algorithm employed in the application plays an important role in obtaining accurate plagiarism detection results. To the best of our knowledge, three algorithms are commonly used in plagiarism detection applications, namely Winnowing, Rabin Karp and Knuth Morris Pratt, which are all employed in our application. To specify the accuracy of each algorithm, the percentages of the plagiarism detection results are compared to the results from examination by a human expert. From our results, we found that the order of the accuracy from highest to lowest corresponded to the Winnowing algorithm, Rabin Karp algorithm and Knuth Morris Pratt algorithm, with value differences of 1.19%, 53.91% and 83.91% respectively.
Artificial Intelligence and Imagination

There is a long history of software programs that implement creativity, imagery, and imagination. They are used for generation of entertainment, creation of art, and simulation of the world. Some of these programs are created to shed light on the nature of imagination, and some are explicitly designed to model imagination in humans. These AIs also display a range of domain areas, from the creation of paintings, music, and plot structures, to the generation of entire fictional worlds. AI aids our understanding of imagination in several ways: It provides ideas of how it could work, it tests the feasibility of theories, and brings to light assumptions and new problems that are difficult to generate from theories that are only described in words.
Metaheuristic algorithm approach to solve non-linear equations system with complex roots

Abstract Non-linear equations system is a collection of some non-linear equations that will find the best solution. Finding the solution of non-linear equations system usually by using analytical method, but there are some complex cases that cannot be solved analytically, so new methods are needed to solve them. One method that can be used to solve non-linear equations system is by using metaheuristic algorithm. This research aims to solve non-linear equations system with complex roots by using metaheuristic algorithm. The metaheuristic algorithm used in this research is Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Cuckoo Search (CS). The input of this research is non-linear equations system that will be tested and parameters of the PSO, FA, and CS algorithm. Non-linear equations system which is the object of problem is polynomial function and transcendent function, which include logarithmic function, first degree trigonometric function and exponential function. The resulting output is an approximation of the complex roots and function value. Then the obtained solution of non-linear equations system compared with the result of accuracy by finding the value of the function f(x) which is closer to zero. The result of this research obtained by comparing the value of the function produced by each algorithm showed that Particle Swarm Optimization (PSO) algorithm is better at solving non-linear equations system with complex roots because the value of the resulting function is close to zero.
Probabilistic optimization algorithms for real-coded problems and its application in Latin hypercube problem

This paper proposes a novel optimization algorithm for read-coded problems called the Probabilistic Optimization Algorithm (POA). In the proposed algorithm, rather than a binary or integer, a probabilistic representation is used for the individuals. Each individual in the proposed algorithm is a probability density function and is capable of representing the entire search space simultaneously. In the search process, each solution performs as a local search and climbs the local optima, and at the same time, the interaction among the probabilistic individuals in the population offers a global search. The parameters of the proposed algorithm are studied in this paper and their effect on the search process is presented. A structured population is proposed for the algorithm and the effect of different structures is analyzed. The algorithm is used to solve Latin Hyper-cube problem and experimental studies suggest promising results. Different benchmark functions are also used to test the algorithm and results are presented. The analyses suggest that the improvement is more significant for large scale problems.
Triag, a framework based on triangles of RDF triples

The success of RDF-based enterprise Knowledge Graphs partly depends on the efficiency to serve SPARQL queries over large datasets. This usually requires the optimization of a large number of joins between a query's triple patterns. A common solution to this problem is to index triples in several orders and to provide adapted query processing optimizations. In this paper, we extend this approach by proposing a framework that tackles a frequently encountered basic graph pattern: triangles. We present appropriate data structures to store these triangles, provide distributed algorithms to discover and materialize them (including inferred triangles), and detail query optimization techniques. Experimental results conducted over an Apache Spark implementation on two real-world RDF datasets emphasize the performance boost obtained with our approach.
Variational Denoising Autoencoders and Least-Squares Policy Iteration for Statistical Dialogue Managers

The use of Reinforcement Learning (RL) approaches for dialogue policy optimization has been the new trend for dialogue management systems.Several methods have been proposed, which are trained on dialogue data to provide optimal system response.However, most of these approaches exhibit performance degradation in the presence of noise, poor scalability to other domains, as well as performance instabilities.To overcome these problems, we propose a novel approach based on the incremental, sample-efficient Least-Squares Policy Iteration (LSPI) algorithm, which is trained on compact, fixed-size dialogue state encodings, obtained from deep Variational Denoising Autoencoders (VDAE).The proposed scheme exhibits stable and noise-robust performance, which significantly outperforms the current state-of-theart, even in mismatched noise environments.
Mutual Information Gain and Linear/Nonlinear Redundancy for Agent Learning, Sequence Analysis, and Modeling

In many applications, intelligent agents need to identify any structure or apparent randomness in an environment and respond appropriately. We use the relative entropy to separate and quantify the presence of both linear and nonlinear redundancy in a sequence and we introduce the new quantities of total mutual information gain and incremental mutual information gain. We illustrate how these new quantities can be used to analyze and characterize the structures and apparent randomness for purely autoregressive sequences and for speech signals with long and short term linear redundancies. The mutual information gain is shown to be an important new tool for capturing and quantifying learning for sequence modeling and analysis.
Obstacles and Challenges to Library Service in Uganda

Library Services in Uganda, Africa face many challenges. Uganda faces extreme poverty, with uneven distribution of resources. It is not only the country’s citizens that face financial challenges, but Ugandan government, publishing companies, and libraries. Through understanding the challenges in library service in Uganda, librarians worldwide may be inspired. After all, library service in Uganda thrives despite a challenging environment with obstacles at nearly every level. This paper will explore the challenges and obstacles around library services to Uganda with a focus on the solutions.
Design of Multicriteria Decision Making Tools for IT Project Selection: A Case from Software House

Abstract Information Technology as an enabler helps organization to improve business by gaining more competitive advantage, it plays a significant role for organization to compete. Many organizations sense the pressure to leverage their investment towards information system. Selecting the optimal portfolio of IT projects is becoming increasingly important as the dependency on IT for organizational performance increases. The purpose of this research is to design tools for IT project selection tools in the software house. Combination of Delphi, AHP, and Factor rating method is used to gain the prioritize of the projects. Agile method is used to develop the application. The application is built in web platform to help the organization decision. Future research can be done to consider more variables for choosing the right project for the organization.