Reviewerly/exHarmony huggingface dataset:
An Enhanced Steganographic Algorithm for Secure Communication

Kaur, Rajwinder Singh, ButtaIn this work, we have proposed a randomized hybrid method by using coupled chaotic map and least significant bit replacement. Where the chaotic map generates the random locations to provide security to the proposed method, LSB maintains the visual and statistical parameters of the image. The performance of the proposed algorithm is evaluated using statistical parameters such as energy, correlation, homogeneity, peak signal-to-noise ratio (PSNR), mean square error (MSE) and entropy. Experimental results show that the designed approach is highly vigorous and withstand against external invasions.
Variational Sentence Augmentation for Masked Language Modeling

We introduce a variational sentence augmentation method that consists of Variational Autoencoder [1] and Gated Recurrent Unit [2]. The proposed method for data augmentation benefits from its latent space representation, which encodes semantic and syntactic properties of the language. After learning the representation of the language, the model generates sentences from its latent space with the sequential structure of Gated Recurrent Unit. By augmenting existing unstructured corpus, the model improves Masked Language Modeling on pre-training. As a result, it improves fine-tuning as well. In pre-training, our method increases the prediction rate of masked tokens. In fine-tuning, we show that variational sentence augmentation can help semantic tasks and syntactic tasks. We make our experiments and evaluations on a limited dataset containing Turkish sentences, which also stands for a contribution to low resource languages.
Design of Vocal Music Mobile Learning Platform Based on Digital Information Technology

Mobile learning is a new learning model based on digital learning and effectively combined with mobile computing technology. The mobile learning platform is a system platform that can solve the needs of mobile learning. The learning content can be repeatedly participated, the learning materials are easy to manage, the data statistics are more intelligent and clear, and the next stage of learning can be arranged in a targeted manner. In the mobile information age, shape the cultural self-confidence of college students needs to give full play to the role of the mobile learning platform. jQuery Mobile is suitable for all popular smart phones and tablets. This article builds the mobile learning platform based on the jQuery Mobile framework, and proposes paths for vocal performance course teaching to shape college students' cultural confidence.
A Meta-Analysis of Privacy

Facial recognition systems use advanced computing to capture facial information and compare the same with proprietary databases for validation. The emergence of data capturing intermediaries and open access image repositories have compounded the need for a holistic perspective for handling the privacy and security challenges associated with FRS. The study presents the results of a bibliometric analysis conducted on the topic of privacy, ethical and security aspects of FRS. This study presents the level of academic discussion on the topic using bibliometric performance analysis. The results of the bibliographic coupling analysis to identify the research hotspots are also presented. The results also include the systematic literature review of 148 publications that are distributed across seven themes. Both the bibliometric and systematic analysis showed that privacy and security in FRS requires a holistic perspective that cuts across privacy, ethical, security, legal, policy and technological aspects.
Approaches on Medical Image Watermarking Using Big Cordial Data in Network of Network

With the shift in time towards several calculation techniques during research of large-scale cordial as well as communication wonders, which is not limited to Internet Studies but may have been influenced by it to some degree. Medical images are important tool for diagnostic procedure and Medical image watermarking (MIW) has come up as a promising solution in this scenario. This “computational turn” is often referred to as a shift toward “big data” or, more precisely, “big data” analytics. This combines to push enhancement of updated techniques using big cordial data in network of networks approaches, as well as instruments because Internet Studies links many fundamental disciplines, it has been a proponent of huge information. All social and cultural fields related to different techniques that utilize “big data” computer science are the study of computers. Similarly, the main study objectives in network of networks are the various browsers, different android applications and gadgets, are all important sources of information. Whatever the, opinions, thoughts, of different clients, also the actions are captured in “big data.” It is suggested by proponents of such methods it’s becoming easier to “analyze society using the Internet,” while others raise serious concerns. As the logic of “big data” affects research objectives, some observations are favored and which are disregarded. In Internet Studies, the ancient invention as well as use of large amount of information different study techniques, as well as critical examinations of such methods, with a particular emphasis on study in Twitter application that acts as cordial.
An Experimental Analysis of Graph-Distance Algorithms for Comparing API Usages

Modern software development heavily relies on the reuse of functionalities through Application Programming Interfaces (APIs). However, client developers can have issues identifying the correct usage of a certain API, causing misuses accompanied by software crashes or usability bugs. Therefore, researchers have aimed at identifying API misuses automatically by comparing client code usages to correct API usages. Some techniques rely on certain API-specific graph-based data structures to improve the abstract representation of API usages. Such techniques need to compare graphs, for instance, by computing distance metrics based on the minimal graph edit distance or the largest common subgraphs, whose computations are known to be NP-hard problems. Fortunately, there exist many abstractions for simplifying graph distance computation. However, their applicability for comparing graph representations of API usages has not been analyzed. In this paper, we provide a comparison of different distance algorithms of API-usage graphs regarding correctness and runtime. Particularly, correctness relates to the algorithms’ ability to identify similar correct API usages, but also to discriminate similar correct and false usages as well as non-similar usages. For this purpose, we systematically identified a set of eight graph-based distance algorithms and applied them on two datasets of real-world API usages and misuses. Interestingly, our results suggest that existing distance algorithms are not reliable for comparing API usage graphs. To improve on this situation, we identified and discuss the algorithms’ issues, based on which we formulate hypotheses to initiate research on overcoming them.
Automatic Segmentation of the Golgi Apparatus in Volumetric Data with Approximate Labels

The Golgi apparatus (GA) is a cellular organelle involved in the processing and sorting of proteins in eukaryotic cells. Due to its numerous functions, structural complexity, and organizational dynamics, the role of the GA in normal and pathological processes is still under intensive research. In this work, we present an approach to automatic segmentation of the GA in electron microscopy volumetric data, consisting of i) a neural network trained on approximately labelled data, ii) active contours for refining the segmentation, and iii) filtering of the segmented regions. Evaluation on 3D volumes of a urinary bladder epithelial cell shows that the proposed algorithm is able to segment the GA with 89% sensitivity and 99% specificity. Using approximate labels reduced the time needed for manual annotation of the ground truth by a factor of five.
Research on Master-Slave Database Management and Scheduling Algorithm of Distance Education Based on Streaming Media Technology

At present, the application of streaming media (SM) technology and network technology in distance education is a hot issue. Because distance education, especially distance multimedia teaching, involves the transmission of audio and video on the network, and the current network situation is not ideal for the transmission of audio and video streams, therefore, solving the problem of master-slave database management and scheduling algorithm in distance education is the key research content of this paper. Multi-level feedback queues are used to store different types of data, and the idea of dynamic buffer partition is proposed. In different playing stages, the node buffer is divided into different parts. In the initial playing stage of the node, the data to be played is cached in the emergency area to reduce the buffering time. The data scheduling path loss model controlled by SM network communication cost matrix is constructed, and the algorithm is improved. Experimental results show that the model can effectively reduce the scheduling path loss of SM data, optimize SM data transmission, and finally achieve the minimum transmission delay and avoid scheduling delay and distortion.
Development of a Method for Building a Trusted Environment by Using Hidden Software Agent Steganography

Abstract For more than a decade, the scientific community has been developing various models of insider actions in the trusted-zone information system and methods for identifying such. All those years, they have been facing the challenge of finding quality data samples for analysis and testing, and it is the availability of reliable data on the attack that is crucial for any research or detection of attempts to steal or compromise legal software. Unfortunately, collection of such samples remains nearly impossible despite some attempts to do so. Any data on incidents is either incomplete or inaccurate, or is not available at all. Software developers also face lack of resources, including time. Developers do not have enough time, funding, material resources, or qualifications to make a sturdy security system, which is why they need to use third-party automations that dock a security module to the compiled program. The strength of this approach is that such a system can be embedded in any software, whereas the weakness is that the method is "one-size-fits-all". Standard protections span across multiple programs, which results in a higher chance of being hacked.
Making a Case for Studying the Effects of Using GitHub on HBCU Students' Computational Thinking Skills

While preparing students for careers beyond graduation, HBCU faculty often have to balance the priorities of bridging the gap between the level of student preparation and the expected level of preparedness as influenced by industry and discipline standards. Prioritization of these standards does not always align with the experience of students at Historically Black College and Universities (HBCU) and Minority Serving Institutions (MSI). The Association of Computing Machinery (ACM) 2013 Computer Science Curricula guidelines highlight version control as one of many core tools in the Social Issues and Professional Practice (SP) Knowledge Area students should experience in their upper-level software engineering courses. Research has demonstrated that at this level, students receive exposure to the tool but are not able to gain sufficient mastery. Other researchers have advocated for version control being introduced earlier in the computer science curricula. There is limited research that indicates that this particular tool improves the learning outcomes for students or significantly complements the other ACM Knowledge Areas. This paper explores the challenges experienced by an HBCU faculty member while integrating GitHub into an advanced programming class. It is important to note, there is no research that directly addresses the use of version control in predominately African American classrooms.
Fault Text Classification of Rotating Machine Based BERT

Accurate classification of equipment fault is one of the effective ways to improve the efficiency of fault analysis and maintenance. How to establish an effective equipment fault classification model has become a hot research topic. Therefore, a fault text classification method for rotating machine based on BERT (Bidirectional encoder representation from transformers) is proposed, which uses fine-tune on the BERT pre-training language model to make it suitable for downstream tasks and takes bidirectional transformers for feature extraction to get understanding of text semantics. The experimental results show that the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F1$</tex> of the model on the test set is up to 97.4%, which is 18.4% higher than the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F1$</tex> of the THUCTC (THU Chinese Text Classification) classification tool on average; the model based on BERT not only has a higher accuracy rate, but also has a certain reference significance for similar downstream tasks of natural language processing.
Design and Implementation of High-Performance Software Load Balancer for Cloud Service

With the growth of cloud computing platform scale and the surge of cloud tenant traffic, it will undoubtedly bring huge challenges to the load balancing of cloud service clusters. In order to achieve the load balancing of high concurrent tenant traffic balance to the appropriate service node, the paper proposes a DPDK-based software load balancer to deal with and optimize the traditional software load balancer's low performance and low reliability limitations. We also designed a health check mechanism for cloud computing, which can continuously and stably check the health status of service nodes under continuous overloaded business traffic. The experimental results show that the software load balancer designed in the paper has high performance, high availability, high scalability, and can meet most of the cloud computing service requirements.
Exploratory Spatial Analysis

In this chapter the tools for spatial exploratory analysis are provided. These include data postings, swathplots and experimental variograms.
An inference model of driving environment relationship based on rules and cases

In today’s most researches on autonomous driving, similar to industrial robot systems relying on sensor information to avoid obstacles, there is no prior knowledge to support logical reasoning analysis of their environment, which will make autonomous vehicles not receive enough information on traffic scenes and participate in traffic. Insufficient description of the contextual relationship between participants may lead to unreasonable decision-making in obstacle avoidance behavior and cause traffic accidents. Therefore, it is necessary to explore a safe driving behavior decision-making method from the perspective of knowledge reasoning for existing autonomous vehicles. This paper proposes an ontology-based self-driving car scene modeling and driving environment relationship inference method in an urban environment. By establishing a structured ontology model that considers the semantic relevance of scene elements, the driving scene is modeled. The knowledge-based reasoning decision model imitates the behavioral decision-making process of a human driver by the mapping relationship of "scene feature-driving action". This type of model stores driving knowledge. The driving knowledge here is mainly manifested from rules, cases to driving actions. The mapping relationship is used to establish a knowledge base containing driving knowledge and experience to provide behavioral decision-making suggestions for the future decision-making system of the vehicle.
Investigating the Effect of Sensor Data Visualization Variances in Virtual Reality

This paper investigates the effect of real-time sensor data variances on humans performing straightforward assembly tasks in a Virtual Reality-based (VR-based) training system. A VR-based training system has been developed to transfer color and depth images, and constructs colored point clouds data to represent objects in real-time. Various parameters that affect sensor data acquisition and visualization of remotely operated robots in the real-world are varied. Afterward, the associated task performance is observed. Experimental results from 12 participants performed a total of 95 VR-guided puzzle assembly tasks demonstrated that a combination of low resolution and uncolored points has the most significant effect on participants’ performance. Participants mentioned that they needed to rely upon tactile feedback when the perceptual feedback was minimal. The most insignificant parameter determined was the resolution of the data representations, which, when varied within the experimental bounds, only resulted in a 5% average change in completion time. Participants also indicated in surveys that they felt their performance had improved and frustration was reduced when provided with color information of the scene.
The Innovation of College Physical Education Teaching Model Based on Information Technology

With the advent of the era of science and technology, more and more colleges and universities have begun to try to construct a new teaching model. Among them, building a computer platform for auxiliary education is a relatively common and effective means. In the traditional teaching model, teachers occupies the dominant position for knowledge transfer. However, this static and simplistic education method is not conducive to the effective interactive development of teachers and students and to improve the level of students' participation in IT courses; In addition, it cannot meet the requirements of contemporary social talent training. Therefore, universities based on computer technology The reform of physical education(PE) is imperative. This article firstly discusses the construction of a new PE model that is suitable for the requirements of today's scientific and technological development and conforms to the characteristics of the current era; secondly, it analyzes the important role of computer technology in modern education.
College Project Preservation and Emulation Using Containerization Over Private Cloud

As part of the curriculum, students learn different subjects and in order to showcase their learning, mini projects in the respective subjects every year and a major project in the final year of their study has to be developed. Students work on personal machines which have preset environments and a tools base to facilitate seamless running of the code and project demonstration. The problem here is when these projects are submitted by students, they are received in softcopy form and are stored along with a readme file. All the projects which are submitted have different runtime environments and specific conditions which need to be replicated for successful execution of the project. So whenever there is a need to refer to or demonstrate the projects in the future to various committees or junior students, setting up of environment and tools base must be done for several projects keeping in mind the various versions of softwares to be used and resolving conflicts arising due to dependencies issue. All of this simply multiplies the effort required by the teachers/evaluators and significantly decreases the efficiency of the overall process hence limiting the access of the project to just one terminal that has been setup. More often, projects are preserved in the form of CDs and/or physical files which are subject to wear and tear leading to an arduous process of recovery. Ours is a cloud-based web application supported by DevOps practices, which helps users in creating images for their project and storing them in a persistent state. Also, it will help users to emulate projects by running containers of their image. We have built a cloud based private network which acts as the backbone of our system. The projects are saved as Docker images i.e. the snapshot of their project along with the runtime environment. These projects can then be emulated in a container at any time and can be accessed from anywhere using all the benefits cloud computing offers.
Application and Innovation of Traditional Financial Big Data Based on AI Algorithm

Under the background of big data era, online banking has achieved rapid development and has undergone qualitative changes. Online banking is a new financial form. The application of big data has promoted the development of online banking to a great extent, provided strong technical support for it, and improved the business model of online banking. The application of AI (artificial intelligence) in insurance, credit reporting, asset allocation, big data risk control and other financial fields has brought new changes to the financial industry. As a brand-new technology model, the combination of big data and AI with economy has realized the deep excavation of various resources, which can ensure the quality and efficiency of economic development and reduce the comprehensive cost, thus effectively enhancing the overall competitiveness of China's economy and gaining more initiative in the fierce international competition. This paper investigates the current situation of big data application in online banking, analyzes its potential value and main challenges, and discusses the traditional financial big data application and innovation path based on AI algorithm.
Block-Based Compression for Reducing Indexing Cost of DNN Accelerators

Sparse compression is often used in DNN (deep neural network) accelerators to reduce the data traffic of convolutional computation. As the quantization technique greatly reduces the data bit-width, the indexing cost may account for a large proportion of data traffic after sparse compression. In this paper, we point out the similarity of feature maps in different channels. Based on this observation, we propose a block-based compression approach to reduce the indexing cost. Benchmark data show that the proposed approach can reduce 21.1% indexing cost.
Quantum algorithms for Monte Carlo integration using pseudo-random numbers

Quantum algorithms for Monte Carlo integration (QMCI), which are based on quantum amplitude estimation (QAE), provide quadratic speed-up compared with classical counterparts, and are therefore widely investigated, along with applications to industries such as finance. One typical feature of problems in finance is high dimensionality, or, in other words, the necessity to create numerous random numbers (RNs) to calculate the integrand. In such a situation, the original implementation of QMCI, in which states encoding the probability distributions of the RNs are generated on different quantum registers, requires a large number of qubits. In this poster, we propose an implementation of QMCI, in which pseudo-random numbers (PRNs) are sequentially generated on one register, and therefore the qubit number is tremendously reduced. Moreover, we show that, when the integrand has the form such that contributions from different RNs are separable into different terms, we can also achieve time complexity reduction with respect to the number of dimensions by combining the nested QAE and use of PRNs.