Reviewerly/exHarmony huggingface dataset:
Sensitive Information Security in Network as a Service Model in Cloud-IPSec

The integration of material for use of the Internet, which is utilized by IT technology, is a cloud. The well-defined cloud is one of the best modern technology companies for product performance and changes depending on demand. Now, all the infrastructure of day-to-day business infrastructures, there is a lot of data necessary for the safe transfer of data through the Internet. This may include a company's confidential information about product designs, product expiration dates, patent owner information, human resources, job evaluation, etc. Currently, all organizations are waiting on the web. Conversely, the information necessity be moderated, although it can capture the information. Consequently, all customers must use the cloud. In Hacker's observation which in some cases is the immediate move that the sensitive information will be held within the virtual private cloud. The global security record during 2018, containing the data mobility in the cloud at about 86.67%, a global analyst estimates that this liming can be achieved at 100%. Therefore, this research paper focuses on IP security, which is a typical set of rules for obtaining Internet Protocol (IP) communication by verifying and encrypting the transfer of the information stream from the network routing as OSPF and EIGRP (Enhanced Interior Gateway Routing Protocol) protocol to implements, the effects of using the IP security tunnel on the Network as a Service in the edge router. AES - Encryption algorithm, SHA1 hash algorithm and Pre-Shared keys are used in the proposed structure. The looking at of the domino effect also shows to facilitate the ESP protocol is vaguely less effective than the header authentication protocol, which is obviously due to the ESP protocol that supports data encryption, where Cloud is implemented with GNS3, tested in the Wireshark to protect against attacks.
Parallax-Tolerant Image Stitching with Optimal Homography

Abstract Artificial ghosting is a serious problem in image stitching with wide-baseline, which makes it difficult to achieve seamless stitching. A robust parallax-tolerant image stitching method combined with optimal homography matrix selection and seam-cutting processing is proposed. Optimal homography matrix is obtained by analysing feature point distribution and minimizing the registration error. And then, the aligned area is enhanced by the optimal homography matrix, which can facilitate the seam searching. To realize seamless stitching, the luminance difference and the geometrical structure are used in seam-cutting processing. At last, the experimental results show that the artificial ghosting is solved quickly and effectively in the images stitching with large parallax.
Determining the Effectiveness of Data Remanence Prevention in the AWS Cloud

Previous efforts to detect cross-instance cloud remanence have consisted of searching current instance unallocated space for fragments easily attributable to a prior user or instance, and results were necessarily dependent on the specific instances tested and the search terms employed by the investigator. In contrast, this work developed, tested, and applied a general method to detect potential cross-instance cloud remanence that does not depend on specific instances or search terms. This method collects unallocated space from multiple cloud virtual machine instances based on the same cloud provider template. Empty sectors and sectors which also appear in the allocated space of that instance are removed from the candidate remanence list, and the remaining sectors are compared to sectors from instances based on other templates from that same provider; a matching sector indicate potential cross-instance remanence. Matching sectors are further evaluated by considering contiguous sectors and mapping back to the source file from the other instance template, providing additional evidence that the recovered fragments may in fact be content from another instance. This work first found that unallocated space from multiple cloud instances based on the same template is not empty, random, nor identical - in itself an indicator of possible cross-instance remanence. This work also found sectors in unallocated space of multiple instances that matched contiguous portions of files from instances created from other templates, providing a focused area for determining whether cross-instance data remanence exists. This work contributes a general method to indicate potential cross-instance cloud data remanence which is not dependent on a specific provider or infrastructure, instance details, or the presence of specific user-attributable remnant fragments. A tool to implement the method was developed, validated, and then run on Amazon's AWS cloud service.
Structure and gradient‐based industrial interpolation using computational intelligence

Summary Industry and medical imaging are technologies and processes of managing subjective representations of the industry product, interior of a body for analyzing. The deinterlacing is the procedure of transforming interlaced signal into progressive one. In this paper, I propose a new single field deinterlacing method which has three sub‐methods: bilinear method, small filter‐based method, and large filter‐based method. Pixels in a given image are grouped into three regions by calculated local mean and variance values: stable region, neutral region, and complex region. To implement deinterlacing process, I use weight average filter by considering two factors (the likeness factor and the distance factor) and determine region characteristics. Simulation results inform that the presented method yields satisfactory results in objective metrics (PSNR, SSIM, implementation time) and subjective quality.
Research on Optimization System of Radiotherapy Equipment from the Perspective of Human-Computer Interaction

Based on the knowledge of human-computer interaction, this paper deeply analyzes the human-computer interaction problems existing in China's existing tumor radiotherapy equipment, and notices the lack of "patient-machine" interaction in the human-computer interaction system of radiotherapy. In response to this human-computer interaction problem, the interactive experience of tumor patients was analyzed by collecting questionnaire data. According to the "input-output" process, the interactive relationship between the patient and the machine in the radiotherapy space is decomposed into visual interaction, auditory interaction, tactile interaction and comfort to form a human-computer interaction optimization system based on the data analysis. It also provides an optimized direction for the existing human-computer interaction between radiotherapy equipment and patients.
Image Enhancement Using Filters on Alzheimer’s Disease

The Alzheimer’s disease (AD) is a neurological disorder. It is a slow ongoing brain disease that starts well before clinical symptoms emerge. The subsequent decline in cognitive functions worsens with time and eventually can lead to death. In this study, we worked on T2 sequence Magnetic Resonance Images (MRI) of the Brain AD affected images. The brain may be affected by the AD in different regions. Proposed work concentrates on the hippocampus region affected by the AD using MRI imaging modality. In order to analysis the medical images with no noise the images are enhanced by using different filtering techniques. The work carried out in this paper discuss about different filtering techniques like Gaussian, Median, Wiener and order statistical filter. The performance of these algorithm were examined on PSNR and RMSE values. After the computation the median filter is consider to be the good filter as per the results for the MRI images of AD.
Generation of W states via Lyapunov control by combination of quantum Zeno dynamics

We propose a scheme to create a 4-atom W state by Lyapunov control with the help of quantum Zeno effect. The effective Hamiltonian to describe quantum Zeno dynamics can be obtained in an invariant Zeno subspace and the 4-atom W state can be achieved through modulating the system parameters. Numerical simulation shows that the present scheme is robust against both cavity decay and the atom spontaneous emission. Furthermore, we present a proposal for W state conversion by the same principle, which provides theoretically a novel way for multi-qubits entanglement preparation.
A Simple Web Utility for Automatic Speech Quantification in Dyadic Reading Interactions

Adult-child reading interactions can produce efficient childhood learning outcomes when formal methods of adult-initiated prompting and dialog are employed. In order to quantify instances of such prompting, child development researchers often manually code speech recordings or transcripts taken from adult-child readings, which can be a laborious process. For researchers, it would therefore be valuable to have the capacity to automatically capture quantitative measures of communication from adult-child reading interactions. This paper introduces SoundCount, a new open-source utility for extracting descriptive features from recordings of speech during communication between an adult and child reading an eBook together. In a simple web-based framework, SoundCount consolidates functionalities for speech analysis and quantification in the context of dyadic reading interactions, specifically providing measures related to word count and speaker differentiation. Our preliminary results demonstrate the functional feasibility of SoundCount, and our technical discussion will enable readers to use SoundCount in their own research. Given the technical viability of SoundCount, future work will include the implementation of additional measures for the system, new features such as automated audio segmentation based on speaker, and a test of the efficacy of real-time feedback systems based on speech measures.
Redefinition of Benefits of Inconvenience

A new design methodology for a human-machine system, which is called Benefits of Inconvenience (BI), has attracted lots of attention, especially from the people who worry about the harmful effects of too convenient products and systems. Nevertheless, the definitions of “inconvenience” and “benefits” of inconvenience differ not only among the researchers and the general public but also among the researchers. Therefore, the present manuscript clarifies the reasons why the misunderstandings of the above-mentioned definitions occur, and it redefines the BI by separation of effort and benefits into two types; objective ones and subjective ones. Moreover, the present manuscript analyzes some BI examples from the viewpoint of the new BI definition.
Refinement and Trust Modeling of Spatio-Temporal Big Data

The conventional studies of spatio-temporal data models and their big data applications cannot reliably reflect the large volume, heterogeneity and dynamics of spatio-temporal big data. In this paper, the structure and function expression of spatio-temporal metadata is analyzed. With fused and normalized spatio-temporal reference and data structure, the constraint rules of spatio-temporal big data refinement are proposed. Using the domain specific modeling (DSM) and the data granulation algorithms, an object-oriented modeling language, the thrust modeling of spatio-temporal big data, and the aggregated status correlation of unified model data are established. This work utilizes the trust modeling theory and the spatio-temporal data processing methods and defines a case study that converts spatio-temporal data into dynamic complex big data. This research paves the way for the trust modeling and validation of spatio-temporal big data.
Problems and Prospects of Anti-Theft and Mobile Phone Tracking: A case in Nigeria

The study addresses some security challenges and opportunities brought by mobile phone tracking technologies, especially in the business of selling and buying used handsets. Cases, for instant, arise on the issue of stolen or missing phones. Some of the cases may involve the police, who make digital investigations to apprehend the culprits. However, as a result of some complainants' inability to provide details that will aid the police in making successful investigation, an innocent person or persons may be mistakenly apprehended as the culprit of a phone-theft related crime. The paper reviews literature on Anti-Theft and mobile tracking technologies, with the aim of providing guidance and awareness for phone owners and the potential buyers/sellers of used mobile phones who want to protect their handsets against any other GSM related issues.
GPU-Based Lossless Compression of Aurora Spectral Data using Online DPCM

It is well known that aurorae have very high research value, but the data volume of aurora spectral data is very large, which brings great challenges to storage and transmission. To alleviate this problem, compression of aurora spectral data is indispensable. This paper presents a parallel Compute Unified Device Architecture (CUDA) implementation of the prediction-based online Differential Pulse Code Modulation (DPCM) method for the lossless compression of the aurora spectral data. Two improvements are proposed to improve the compression performance of the online DPCM method. One is on the computing of the prediction coefficients, and the other is on the encoding of the residual. In the CUDA implementation, we proposed a decomposition method for the matrix multiplication to avoid redundant data accesses and calculations. In addition, the CUDA implementation is optimized with a multi-stream technique and multi-graphics processing unit (GPU) technique, respectively. Finally, the average compression time of an aurora spectral image reaches about 0.06 s, which is much less than the 15 s aurora spectral data acquisition time interval and can save a lot of time for transmission and other subsequent tasks.
Secure Deduplication on Public Cloud Storage

Public cloud storage is an important resource of modern computing. One important aspect is the potential to improve economic efficiency. It can be drastically increased by leveraging deduplication techniques. The authors of this paper aim to minimize the risk of data confidentiality violation while maximizing storage utilization. This secure and efficient deduplication concept combines the benefits of context-sensitive partitioning, convergent encryption and remote update protocols. Those technologies are enhanced by a partition-based sensitivity detection algorithm and the handling of sensitive partitions. Thus, incremental updating of stored files is possible. The scheme features an information concealment algorithm to prevent information retrieval attacks. In addition, our solution prevents gaining of illegitimate access to sensitive data due protection by the proof-of-data-ownership scheme Asymmetric Convergent Encryption (ACE). This holistic concept overcomes the weaknesses of deduplicating storage systems. The results of this paper can be leveraged to enhance not only public cloud storage but also other remote storage systems.
Computer Vision Based Systems for Human Pupillary Behavior Evaluation: A Systematic Review of the Literature

Analyzing human pupillary behavior is a noninvasive and alternative method for assessing neurological activity. Changes in this behavior are correlated with various health conditions, such as Parkinson's, Alzheimer's, autism and diabetes. Examining pupil behavior is a simple, low-cost method that can be used as a complementary diagnosis in comparison with other neurological evaluation methods. This approach is made by recording the pupillary behavior against light stimuli and measuring the pupil diameter through the video. The relation of pupillometry with digital image processing creates a dependency for computer vision based systems. Therefore, this paper presents a systematic review of the literature (SRL) conducted in order to analyze the progress of pupillometry systems based on computer vision. The main goal was to establish the state of art and identify possible gaps.
Post-Quantum Signature Schemes for Efficient Hardware Implementation

Post-quantum cryptosystems have attracted a great interest, from researchers, latest. This work introduces a new form of the hidden discrete logarithm problem and two new post-quantum signature schemes. The finite non-commutative associative algebras containing a large set of global single-sided units are used as the algebraic support of the proposed crypto schemes. The illustrated FPGA implementation results, show the efficiency of the proposed cryptographic schemes, in hardware approaches. Detailed comparisons, with other security hardware implementations, are also presented.
A Study on Narrative Timing Sequence of Animation in Mobile Interfaces

This paper focuses on the narrative features of interactive animation of mobile phone interface, and discusses the design structure and influencing factors of time sequence dimensions in mobile interactive animation. The narrative feature of interactive animation can improve the user’s understanding of information and optimize the logic and information transmission efficiency of animation, which plays an important role in improving user experience. Therefore, this paper analyzes the interactive animation of existing mobile phone systems on the market, and summarizes the narrative features of interactive animation, including the characteristics of “micro-event” narrative, strong functional narrative and abstract graphic metaphor narrative. This paper discusses the design structure and influencing factors of time sequence dimensions in mobile interactive animation. The time sequence structure of three basic interactive animation narratives of “linear structure”, “parallel structure” and “link structure” is proposed, which provides a new design thinking angle and reference for interactive animation design.
An Adaptive Approach for Demand-Response and Latency Control in Distributed Web Services

Real-time electricity prices and volatile workload patterns have a major impact on the operational costs of distributed data centers. A smart grid enables large consumers to regulate their usage patterns with the knowledge of real-time prices at different locations, and their energy consumption through the demand response (DR) program. However, the price inclined workload assignment may lead to an increase in peak demand, and decrease the benefits of workload offloading. Moreover, energy-efficient service management is challenged by unpredictable network conditions that can cause service performance degradation. Using an actor-critic approach, we developed a system that distributes a service's load among distributed sites to take advantage of the spatial variations in energy pricing. Our experimental results on the CloudLab testbed have proved with 45% reduction in running costs without impacting the response time using the Wikipedia request traces considering peak-based pricing.
Graph-Based Supervised Feature Selection Using Correlation Exponential

In this article, a graph-theoretic method for supervised feature selection using matrix exponential of pairwise correlation value has been illustrated. In machine learning, high-dimensional data sets have a enormous number of redundant and irrelevant features. The sum of mean and standard deviation of exponential matrix has been set as the threshold for selecting relevant features. Principles of vertex cover and independent set have then been used to remove redundant features. In the next step, mutual information value has been used to select relevant features that were initially rejected. The results show that this method has performed better than the benchmark algorithms when experimented on multiple standard data sets.
Multi-scale and Hierarchical Embedding for Polarity Shift Sensitive Sentiment Classification

Appropriate paragraph embedding is critical for sentiment classification. However, the embedding for paragraph with polarity shift is very challenging and insufficiently explored. In this paper, a MUlti-Scale and Hierarchical embedding method, MUSH, is proposed to learn a more accurate paragraph embedding for polarity shift sensitive sentiment classification. MUSH adopts CNN with multi-size filters to reveal multi-scale sentiment atoms and utilizes hierarchical multi-line CNN-RNN structures to simultaneously capture polarity shift in both sentence level and paragraph level. Extensive experiments on four large real-world data sets demonstrate that the MUSH-enabled sentiment classification significantly enhances the accuracy compared with three state-of-the-art and four baseline competitors.
A Real-time Character-level Subtitle Detector based on Faster R-CNN

Abstract Faced with a deluge of video data, automatic subtitle text detection and recognition using computer vision technology can be extremely useful. This paper proposes a real-time character-level subtitle detector, which detects subtitle text with high accuracy and efficiency in a single forward pass. It also provides an efficient method to build dataset aimed at video subtitle text detection problem. This paper’s method can achieve both 99.5% precision and recall for text detection, and the whole detection and recognition process can achieve 45 fps.
UsabEU: Online Platform for Translation, Validation and Native Use of Usability Questionnaires with Multilingual User Groups

The goal of UsabEU is to provide a starting point for usability evaluations with questionnaires in native or mother tongue. The platform supports online collaborative translation of usability questionnaires and their validation. Additionally, it serves as a repository for all validated questionnaires and a tool to perform statistically sound usability evaluations. The current proof-of-concept platform supports the translation and validation of the System Usability Scale questionnaire and statistical assistance for sample size estimation and data summarization.
SketTune: Real-time input assistance for novices to compose music for self-expression

When novice composers create a combination of melodies and accompaniments in the early stages of composition, they require a high easiness and good flexibility. We define music sketches as a “combination of an accompaniment and a melody. ” Novices need assistance in making music sketches, however, automation approach increases easiness but decreases flexibility. In this paper, we have developed a system that can input natural melodies in real-time based on the accompaniments so that novice composers can easily make music sketches. Moreover, the system provides rhythm assistance for novice composers who cannot provide inputs in real-time. Finally, we evaluated the proposed system using a comparison experiment having four systems that differed based on the note-flexibility, which is the flexibility of the pitches and the rhythms. As a result, we proved that the proposed system is the best for novice composers because of its easiness and note-flexibility.