Reviewerly/exHarmony huggingface dataset:
Informatization of education in military academies

Information technology is penetrating and transforming the field of education. It also provides realistic conditions and unprecedented opportunities for educational reform and innovation in military academies. Based on the analysis of the logical connotation of education informationization in military academies and in view of the practical predicament in the development, this paper describes the realization path of education informationization in military academies from six dimensions of "remolding education idea, perfecting laws and regulations, perfecting teaching environment, transforming teaching process, innovating evaluation methods and constructing education ecology", with a view to providing theoretical research on higher education informationization. And provide reference for practical operation.
Where Should I Submit My Work for Publication? An Asymmetrical Classification Model to Optimize Choice

Choosing a journal to publish a work is a task that involves many variables. Usually, the authors’ experience allows them to classify journals into categories, according to their suitability and the characteristics of the article. However, there are certain aspects in the choice that are probabilistic in nature, whose modelling may provide some help. Suppose an author has to choose a journal from a preference list to publish an article. The researcher is interested in publishing the paper in a journal with a rank number less than or equal to k. For this purpose, a simple classification model is presented in order to choose the best journal from the list, from which some fundamental consequences can be deduced and simple rules derived. For example, if the list contains 100 journals and is ordered using 2-year impact factor, the rule “send to the journal at the k − 10 position” is adequate.
Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing

Image segmentation is a fundamental step in many of image processing applications. In most cases the image’s pixels are clustered only based on the pixels’ intensity or color information and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering the importance of including spatial information of pixels which improves the quality of image segmentation, and using the information of the neighboring pixels, causes enhancing of the accuracy of segmentation. In this paper the idea of combining the K-means algorithm and the Improved Imperialist Competitive algorithm is proposed. Also before applying the hybrid algorithm, a new image is created and then the hybrid algorithm is employed. Finally, a simple post-processing is applied on the clustered image. Comparing the results of the proposed method on different images, with other methods, shows that in most cases, the accuracy of the NLICA algorithm is better than the other methods.
Selecting Secret Sharing Instantiations for Distributed Storage

Distributed storage systems using secret sharing enable information-theoretic confidentiality, making them especially suitable for the outsourced storage of sensitive data. In particular, proactive secret sharing enhances the confidentiality protection of such systems by periodically renewing data shares. This adds a time constraint for an attacker trying to reconstruct the initial data by collecting enough shares. Proactive secret sharing can only be carried out effectively if the participating servers (grouped in storage service providers) are reliable. The selection of participating servers is thus critical to security. In practice, data owners have little means to make an informed decision in this regard. Furthermore, optimal share allocation depends on data-owner-specific confidentiality, availability and cost requirements. Data owners also require guidance with respect to the selection of the underlying secret sharing scheme. In this paper, we introduce a novel approach to guide data owners in the instantiation of secret sharing for outsourced storage. The decision support covers both the allocation of shares to specific storage service providers, and the choice of the underlying secret sharing scheme. We realise our approach as a solver for a set of integer linear programming problems. We then dually evaluate our approach. First, we evaluate the feasability of constraint solving by implementing the linear programs in PuLP and inputting them to the GLPK linear problem solver. The evaluation involves sixty data centers from six major public cloud providers. Second, we compare the performance of hierarchical and non-hierarachical secret sharing schemes to determine if the performance loss due to the support of hierarchical structures is affordable. Ultimately, our approach aims at supporting non-expert data owners in making the most appropriate choices for the selection of a secret-sharing-based distributed storage system, based on their requirements.
Check2: A Framework for Fake Check-in Detection

Location-based social networks (LBSNs) have increased rapidly over the past several years due to the proliferation of mobile devices. As people continue to use their phones everywhere and all the time - even while they shop and dine, a lot of LBSN services such as Foursquare ( https://foursquare.com/ ), Facebook place ( https://www.facebook.com/places/ ) have been introduced. These LBSNs provide new types of services that are location based for social network users. LBSNs integrate the functionalities of both location services and social networks. Consequently, this empowers people to use location data with online social networks in several ways including: location recommendation, mobile-advertising, etc. Mainly, LBSN is widely used in sharing location between users. In addition, LBSNs make use of the notion of “check-in” to enable users to share their location with other users. To attract more users, LBSN service providers offer rewards to users who check-in at a certain place, however, this caused some users (i.e. attackers) to cheat regarding their true locations in order to get either monetary or virtual rewards. Nevertheless, fake check-ins can cause monetary losses to service providers. In this paper, we propose a novel framework for analyzing users’ check-ins and detecting fake check-ins.
Straight Line Detection Through Sub-pixel Hough Transform

In this study, we present the sub-pixel Hough Transform, which is used to detect line positions at sub-pixel level. A comparison with the conventional Hough Transform was performed. As a result, we obtained an increase in detection resolution.
Empirical Analysis of the Growth and Challenges of New Programming Languages

New programming languages (e.g., Swift, Go, Rust, etc.) are being introduced to provide a better opportunity to developers by matching the requirements of new platforms and application contexts. In the beginning, a programming language is likely to have constraints of resources that encourage the developers to seek help from experienced peers active in Question-answering (QA) sites such as Stack Overflow (SO). In this study, we would like to analyze the discussions on three popular new languages that are introduced after the inception of SO (2008). The relevant posts in SO present an interesting representation of the growth/evolution of that language and also expose the demands of the relevant development community. The major findings of the study are: (i) the time when adequate resources are expected to be available vary from language to language, (ii) the unanswered question ratio increases regardless of the age of the language and (iii) a new language is benefited from its predecessor language. The study outcome is likely to help the owner/sponsor of these languages to design better features and documentation and software developers or students to prepare themselves to work on these languages in an informed way.
Peripheral Sensing: Monitoring Quality of Experience for Video Services Based on Mobile Terminals

Quality of experience (QoE), which directly relates to both technical evolvement and profit promotion, is a vital concern for mobile video services. However, the wireless network operators have long been troubled by the problem of lacking effective QoE monitoring approaches since the traditional evaluation methods of communication quality are objective metrics oriented. Considering that mobile terminals are the network elements closest to users, it is promising to realize a real-time QoE estimation for video services by fully utilizing the sensing capabilities of mobile terminals. As the first step, we specifically develop a mobile video testing application. With support from China Unicom, one of the three major wireless network operators in China, over 80 000 data records are collected under the real-world conditions. The collected data consist of four types of subjective scores and 13 objective parameters concerning video attributes, network performance, device capability, playback events, and external factors. After preprocessing the data set through correlation analysis, we establish the two QoE estimation models based on the C4.5 method and the gradient boosting decision tree (GBDT) method, respectively. The experimental results demonstrate that the proposed models can achieve remarkable estimation performances and outperform the baseline models. Specifically, the overall estimation accuracy of the GBDT-based model is approximately 80% for a five-level scale and approaches 90% when a more practical 3-level scale is adopted. Finally, we comprehensively discuss the estimation performances based on characteristics of the data and validate the feasibility of estimating QoE based on mobile terminals-the “peripheral sensors” of the mobile networks.
A Triple-Bit Coding Scheme for Digital Image Watermarking

With the wide use of the internet and digital images, watermarking became very popular and brought the attention of many researchers. Image watermarking is the process of adding additional information (e.g. text) to the original image in order to provide copyright protection. Various ways of watermarking in both the transform domain and the spatial domain have been proposed. Watermarking algorithms aim to improve the watermark robustness, and to provide high imperceptibility and security. The impact of channel coding on the performance of watermarking is effective. It appears natural if one compares the watermarking problem with the transmission of a signal over a noisy channel. In this paper, a triple-bit error-correcting algorithm for digital image watermarking in the transform domain is proposed. The algorithm considers the stego-image as a channel and the compression attack as a noise signal. The proposed algorithm was evaluated against two well-known techniques, which are the Block coding and the Convolutional error-correcting code; it showed very good results in terms of watermark robustness comparing to the other two techniques. The proposed algorithm has been implemented using MATLAB.
Significance of Emotional Context While Performing Semantic Tasks

This research aims to study the features of the brain mechanisms of predicting words based on the previous context, including emotional context. Based on the results of an experiment conducted on 21 people (aged 18 to 32 years, 14 male and 7 female respondents), it can be concluded that the emotional context can accelerate the “decision-making” about the meaning of the stimulus and whether it is suitable for the context. Significant differences between stimuli in different conditions of presentation arose in the later components (about 400 ms after stimulus), which suggests that these differences really reflect differences in meaning of the stimuli, instead of their visual component.
Comparative Analysis of Data Transmission Quality of Seismic Station Based on Different Communication Modes

In this paper, we selected the VPN tunnels based on wireless 4G network card or fiber broadband, MSTP digital optical fiber and other communication methods, using the seismic station in Hunan Province to set up seismic observation instrument, at the same time, the same location compared to the above communication mode To the observation data, analysis of data transmission test results, selected the best mode of communication for the new seismic stations or replace the existing seismic stations to provide a reference for communication.
Increasing of Semantic Sustainability in the Interaction of Information Processes

Ordinary semantic network in use is based on the referencing of individuals in a constant mode. When modeling the interaction of information processes, there is a dynamic that requires a transient process. In this case, individuals can be dereferenced, which leads to a violation of the stability of the information system (IS). A dynamic model is proposed and, in particular, based on (after-became)-neighborhood of properties in the semantic network. There is a semantic Web, in which there is individual confluence, i.e. semantic vulnerabilities. A semantic secure solution is based on: (1) the semantic sustainability of interrelated information processes, (2) granulation of data-metadata objects, and (3) the generation of variable metadata objects. This is done against the background of the formation of “possible worlds” as a spectrum of evolving the events. There is a transformation of properties and an associated transition process, for which a means of ensuring sustainability is proposed.
A Lyapunov Stability Based Adaptive Learning Rate of Recursive Sinusoidal Function Neural Network for Identification of Elders Fall Signal

This paper presents an adaptive learning rate of recursive sinusoidal function neural network (ALR-RSFNN) with Lyapunov stability for identification elders fall signal. The older human signal analysis has been a research topic in health care fields that algorithms are implemented in wearable device real time to detect fall situation. However, the code size of the microcontroller in wearable device is limited, and the neural network learning rate choice is important which influencs neural network convergence performance. The recursive sinusoidal function neural network uses sine wave modulation input function to reduce train times in traditional Gaussian function vertex and width. Moreover, we utilize adaptive learning rate to guarantee network stability. In the experimental results, the ALR-RSFNN identify human fall signal accurately and reliably. In addition, we use wearable device combined BLE (Bluetooth low energy) to feedback output response real time.
Analysis of Word Embeddings Using Fuzzy Clustering

In data dominated systems and applications, a concept of representing words in a numerical format has gained a lot of attention. There are a few approaches used to generate such a representation. An interesting issue that should be considered is the ability of such representations—called embeddings—to imitate human-based semantic similarity between words. In this study, we perform a fuzzy-based analysis of vector representations of words, i.e., word embeddings. We use two popular fuzzy clustering algorithms on count-based word embeddings, known as GloVe, of different dimensionality. Words from WordSim-353, called the gold standard, are represented as vectors and clustered. The results indicate that fuzzy clustering algorithms are very sensitive to high-dimensional data, and parameter tuning can dramatically change their performance. We show that by adjusting the value of the fuzzifier parameter, fuzzy clustering can be successfully applied to vectors of high—up to one hundred—dimensions. Additionally, we illustrate that fuzzy clustering allows to provide interesting results regarding membership of words to different clusters.
An Improved Multi-classification Algorithm for Imbalanced Online Public Opinion Data

When the datasets about online public opinion are imbalanced, the classifier is prone to sacrifice the accuracy of minority class to achieve the overall best performance. In order to solve this problem, an online public opinion text multi-classification algorithm based on random forest and cost-sensitive is proposed in this essay. The algorithm uses Naïve Bayes to construct cost matrix, chooses Gini index with misclassification cost to select the decision tree node. After the comparative experiment, the classifier has improved performance by 3% overall and 8% on minority classes, which can solve the problem of data imbalance to some extent.
Image Steganography Using Random Image

Now-a-days every person depends on computer and Internet. Security is one of the major problem in public communication systems, the information may be accessed by third parties during the transmission of data through a network. Network security is not sufficient, and cybercrime is also increasing. It is possible to provide more security to data with the combination of cryptography and steganography. Cryptography is basically secret writing and steganography is the method hiding data in an object. Cryptography and steganography both methods can be used for preventing, and protecting data from different types of attacks. In the existing system data bits are encrypted and then embedded into cover image using, it results degradation of cover image. The degradation and security of hidden data are the drawbacks. To overcome these drawbacks, a new technique is proposed using random image. Initially text is converted to an image, and it is embedded into a random cover image. The proposed method may provide more security with the help of random image. It is not easy to perform the steg analysis on random image. Performance measures such as MSE, PSNR are showing good results when compared with the existing method.
Measuring the Complexity of Learning Content to Enable Automated Comparison, Recommendation, and Generation

Learning content is increasingly diverse in order to meet learner needs for individual personalization, progression, and variety. Learners may encounter material through different content, which invites a measurable comparison method in order to tell when delivered content is sufficient or similar. Content recommendation and generation similarly motivate a fine-grained measure that enhances the search for just the right content or identifies where new learning content is needed to support all learners. Complexity offers a fine-grained way of measuring content which works across instructional domains and media types, potentially adding to existing qualitative and quantitative content descriptions. Reductionist complexity measures focus on quantifiable accounting which practitioners and computers in support of practice can use together to communicate about the complexity of learning content. In addition, holistic complexity measures incorporate contextual influences on complexity that practitioners typically reason about when they understand, choose, and personalize learning content. A combined measure of complexity uses learning objectives as a focus point to let teachers and trainers manage the scope of reductionist elements and capture holistic context factors that are likely to affect the learning content. The combined measure has been demonstrated for automated content generation. This concrete example enables an upcoming study on the expert acceptance and usability of complexity for differentiating between hundreds of generated scenarios. As the combined complexity measure is refined and tested in additional domains, it has potential to help computers reason about learning content from many sources in a unified manner that experts can understand, control, and accept.
Multidimensional Analysis Model of Perforation Optimizing Design Platform

The PODP is designed to optimize the perforation scheme and is characterized for specific oilfield. Many factors such as drilling, geology, fluid, perforation, and string mechanics are involved in the design process, and the parameter attributes are numerous, multidisciplinary and cross related. So, the PODP multi-dimensional analysis model is constructed to shield the high-dimensionality and the cross-correlation of the attributes to provide support for the evaluation of the perforation schemas, acquisition of perforation optimizing design knowledge and more characteristic improvement of algorithms. The application examples show that the model can provide multi-angle, multi-objective and multi-granularity analysis of perforation optimizing design parameters and conclusions.
Overhead wires detection by FPGA real-time image processing

The paper presents design and hardware implementation of real-time image filtering for overhead wires detection divided on image processing and results presentation blocks. The image processing block was separated from the whole implementation, and its delay and hardware complexity was analysed. Also the maximum frequency of image processing of the proposed implementation was estimated.
Cicero VR - Public Speaking Training Tool and an Attempt to Create Positive Social VR Experience

Cicero VR game is a simulation gaming tool for public speaking skills training. The game system is measuring user efforts regarding the volume and speed of speech, gesticulation and eye contact with in-game models. There are two modes of play: practice (where you can train how to speak with use of benchmark presentation) and challenge (where you can upload your presentation that pitches new product or service). Presentation in practice mode features a scenario of a yearly report submission in front of a board about a new brand in the company. We have created a fictional car-sharing service, and the user has to present all valuable data standing in a conference room in one of the downtown skyscrapers. The virtual audience varies regarding demographic representation (race, gender and age). They will also react to users performance quality. If the talk is performed with right volume, gesticulation, and maintaining the eye contact – then board avatars will act as more engaged and vice versa. The following paper will describe outcomes of the testing phase of such gaming artifact with analysis of overall VR experience reception. The simulation game was tested by business university students in fall of 2018.
Research on adaptive fractal image compression algorithm based on flexible classification technology

Abstract This paper proposes a flexible classification technique for accelerating image matching and improving coding quality, using a fast adaptive fractal image compression method with four fork tree as image division and flexible classification technology, which is a kind of pre-segmentation of images. With using the self-similarity of each part of the image, the fractal encoding is carried out. In order to reduce the encoding time to dozens of seconds, it eliminate the block effect and realize the fast fractal coding of color image, so as to speed up the encoding speed and further improve the image comprehensive performance of compression ratio and signal-to-noise ratio/encoding time.
A Diagonal-Based Algorithm for the Constrained Longest Common Subsequence Problem

Given two sequences A and B of lengths m and n, respectively, and another constrained sequence C with length r, the constrained longest common subsequence (CLCS) problem is to find the longest common subsequence (LCS) of A and B with the constraint that C is contained as a subsequence in the answer. Based on the diagonal concept for finding the LCS length, proposed by Nakatsu et al., this paper proposes an algorithm for obtaining the CLCS length efficiently in O $$(rL(m-L))$$ time and O(mr) space, where L denotes the CLCS length. According to the experimental result, the proposed algorithm outperforms the previously CLCS algorithms.