Reviewerly/exHarmony huggingface dataset:
Development Scheme of C Language Teaching Practice Platform under Cloud Environment

As the mainstream desktop and application virtualization technology, CloudStack provides a new solution for information-based teaching. Therefore, combining with the development practice of C language teaching practice platform in a university, this paper proposes the basic framework of desktop cloud teaching platform based on virtualization, and studies the overall scheme design, deployment and optimization. The system adopts B/S mode and establishes the entity relationship model of the platform. We also discuss the system configuration technology based on role access control, which enables the teacher teaching platform to flexibly configure the services provided according to the changes of the tenant's application requirements. At the same time, it constructs the function module of job processing based on cloud service, introduces the related technology of workflow, and realizes the automation of job processing, integration process. The test and analysis prove that the teaching scheme has the advantages of more reasonable allocation of teaching resources, more adequate application of software and hardware resources, lower cost of development and maintenance, and it can effectively improve the teaching efficiency of the school, which shows better practical significance.
A Shape-Based Character Segmentation Using Artificial Neural Network for Mizo Script

This paper presents a new character segmentation algorithm by using shape-based-oriented feature vectors for offline printed Mizo script document. The approach in this technique involves area estimation for each isolated blobs which forms a different type of shape, finding the required amount of morphological dilation based on each estimated area, training the artificial neural network (ANN) to map between the area of the shape with the required amount of morphological dilation. This experiment is performed on a font size ranging from 8 to 72, on a dataset containing 70 printed documents each having approximate 200 characters, forming a total of 14,000 characters for single font size. In total, an approximate of 14,000 × 16 = 224,000 characters is considered. The experimental results show that using feed forward back propagation (FFBP) algorithm for both segmentation and classification achieved the best result with a 97% accuracy rate of recognition.
Analysis and Review of Cloud Based Encryption Methods

Data security plays a crucial role in cloud computing. Cloud storages are secured and managed centrally by cloud service providers. Data stored in cloud is secured by encrypting them with the help of various encryption techniques and models. Encryption is very important from a data confidentiality point of view and should match the system and security requirements. Complex encryption methods increase the computational time of the cloud system whenever any data query is being processed. The encryption method has to produce a cipher text which cannot be easily predicted or decrypted without the encryption key. This paper discusses various encryption methods being developed and provides an analysis and comparative study on them.
Geometrical Mapping of an Initially Unknown Region by a Mobile Robot

In this paper, we address a problem of mapping an unknown region of interest by a mobile robot. Unlike the conventional exploration and mapping techniques where the occupancy map of a spatially discretized environment is obtained, in the proposed Geometric Mapping (G-Mapping) strategy, the map is obtained in the form of geometric models of the obstacles, in a continuous space. For simplicity, we consider convex polygonal obstacles within a convex region. The proposed exploration strategy is implemented using MATLAB. The simulation results are presented to illustrate and demonstrate the G-Mapping strategy.
A Fusion Method for Low-Illumination Color Image Enhancement on CIELUV Space

In this paper, a fusion method for low-illumination color image enhancement on CIELUV space is presented. First, the input color image on RGB space is converted into the one on CIELUV space. Then, three color image enhancement methods are employed to enhance the low-illumination image individually including chroma-based method, homomorphic filtering method, and power-law transformation (PLT) method. Next, a Gaussian weighting fusion method is applied to combine three individual enhanced images to obtain the final enhanced image. Finally, low-illumination images are used to evaluate the enhanced quality of the proposed method and make comparison with conventional method.
A Comparative Analysis of Live Migration Techniques for Load Management in Cloud

Live migration is a popular technique used in cloud computing to optimally utilize hardware resources dynamically in the system and reduce unnecessary power consumption in cloud data centers. This paper discusses several prominent live migration techniques applied in cloud computing that aim to evenly distribute load in the system or to consolidate inactive servers. Each algorithm attempts to reduce the overhead of migration by either minimizing the number of migrations or decreasing the application downtime or scheduling the migration while taking available link bandwidth into consideration. A brief summary of each, followed by a comparative account in terms of the major features of the algorithms and advantages and shortcomings of each algorithm is tabulated. The paper is finally concluded by mentioning important points to be taken care of while migrating an application from one physical host to the other in order o achieve better performance.
A Security Model for Enhancement of Social Engineering Process with Implementation of Multifactor Authentication

Social engineering has developed as a major risk in online communities and it is a successful means to target data systems. Modern social engineering attacks are based on the services that knowledge workers use. In MNCs employees are not physically located at same offices, but works on distributed system which enables employees to interact in real-time environment. The lessening in physical interaction leads to increase in use of communication tools such as WhatsApp, Facebook, Twitter, Gmail etc. makes the system more vulnerable to social engineering attacks. Recent Pulwama attack in India showed the impact of social media and internet with lack of information authenticity that played a major role in escalation of the tension between both India and Pakistan. The lack of “Fear of Getting Caught” among the attackers is the main cause of unlawful activities over the online communities. This paper provides a security model that will help reducing social engineering attacks to a large extend.
Multi-Mask Based Stabilization of Turbulence Degraded Videos Containing Moving Objects

Stabilizing videos and detecting moving objects are important tasks in many computer vision applications, though it becomes challenging because of the presence of atmospheric turbulence that causes random pixel shifting and blurring of the videos. This paper proposes an improved method for correcting geometrical distortions of videos degraded by atmospheric turbulence while keeping moving objects unaltered. In this method, three different techniques are used to generate three different masks, which are then combined together to generate a more accurate mask. This mask is employed to properly detect the moving objects and finally fusing with the background a stabilized video output is obtained. The performance of this method is tested by applying it on different real-world datasets. A comparison with an existing method shows that the proposed method gives better detection of moving objects and improved stabilization of the degraded videos.
Comparative Analysis of Contrast Enhancement Techniques for MRI Images

Contrast Enhancement is one of the most essential stages of image processing. This method changes the distribution of image value over a wider range so that images can be perceived in a better way. In this paper we have studied and analyzed the best contrast enhancement technique which can be applied to MRI images. MRI images are very important for diagnosis and understanding the pathology of different organs. Proper contrast enhancement will lead to better understanding of these images. We have comparatively analyzed three different contrast enhancement techniques and interpreted the best technique for MRI images.
Query Modification Based on Relevance Feedback

Query modification based on the pseudo relevance feedback(PRF) is an effective way for improving the performance of retrieval system. It is promising approach that used to solve the problems of ambiguous language and synonyms depending on the feedback. In this paper each of expansion and reduction approaches are used for query modification. A hybrid of distribution and association methods are used to select significant terms and expand the query. In this paper, Kullback-Leibler Divergence (KLD) and CHI squared were used as distribution methods that depend on the terms distribution at the top relevant documents and distribute them in the entire corpus. In addition, proximity term frequency method was used to show the semantic relationship between each candidate term with query terms. Different sets of candidate terms are produced from these methods will be combined by Borda aggregation method to get the strength of each individual method. The second direction is removing insignificant terms from the original query and terms' pool to prevent them from being used for expansion query.
Weather Categorization Using Foreground Subtraction and Deep Transfer Learning

We propose a novel foreground subtraction algorithm to extract the Sky pixels and use transfer learning on these processed images using pre-trained ResNet-50 and DenseNet-161 models to classify them. For this study, the Image2Weather's 2 category weather dataset having 1500 images, is used. By using transfer learning and training, only the last layer by freezing rest of the layers, we have found that the proposed ResNet-50 and DenseNet-161 architectures achieved the classification accuracies of 94.3% and 92.68%, respectively, on the test images and outperformed the state-of-art methods. These results are extremely good as there is a significant improvement of 6–8% in accuracy over the existing models due to foreground subtraction. The proposed method can be implemented in real-time applications to help farmers plan their cropping cycle and for efficient weather forecast activities.
A Statistical QoE-QoS Model of Video Streaming Services

With the advance of network and multimedia technologies, the traffic of network video services has dramatically increased. In order to support the QoS (Quality of Service) and QoE (Quality of Experience) of network video services, it is important to model network layer QoS parameters and application layer QoE appropriately. This paper presents a statistical model between network layer QoS parameters and MOS (Mean Opinion Score) values, by analyzing the statistical characteristics of the video bit rate and jitter in given MOS ranges. Compared with existing approaches, this model can provide video service providers and network operators with more appropriate suggestions for setting network layer QoS parameters under given user's QoE level, and thus has good application prospect in related field.
Marker less Hand Gesture Tracking using Smart Phones

Most of today's small developer forms of mixed reality involves the use of markers to anchor virtual objects in the real world. However, the exponential increase of today's modern hardware in computing power, which can be further increased thanks to the advent of cloud computing, has led to new possibilities not realized before. The researchers' interest then for this study is to harness this increased computing power in knowing how the digital medium could be used to immerse one's self into a completely new environment such as simulated realities as another research into the field of synthesis of congruent technological paradigms. In this paper, the researchers demonstrate how modern tools can fuel a mobile phone such that it can perform a marker-less tracking (QR and AR Code free) and mimic hand gestures similar to what Microsoft Kinect© and Leap Motion© can do while only relying on the mobile phone's camera and presented as under the domain of Mixed Reality, which is a combination of Augmented and Virtual Reality. Also discussed are the improvements and benefits of this new method compared to other developments in this area.
Optimization of five stage cantilever beam design and three stage heat exchanger design using amended differential evolution algorithm

In design of mechanical components, designer considers objectives that are related with volume, area, quality, cost, etc. of the product depending on the requirements. In the present work, optimization of five stage cantilever beam design problem and three stage heat exchanger design problem is carried using Amended Differential Evolution Algorithm (ADEA). ADEA is modified DE that can handle constrained optimization problems very efficiently. In five stage cantilever beam design problem, the design variables are optimized to reduce the volume of beam and in three stage heat exchanger design problem, the design variables are optimized to reduce total heat transfer area. The results obtained using ADEA are compared with the results reported by other researchers who have used different algorithms, namely, dBA, HS, GA, BA to optimize the same problems. The comparison of results shows that ADEA gives better results for five stage cantilever beam design problem and competitive results for three stage heat exchanger design problem.
Silent cyber assessment framework

Abstract The (re)insurance industry is faced with a growing risk related to the development of information technology (IT). This growth is creating an increasingly digitally interconnected world with more and more dependence being placed on IT systems to manage processes. This is generating opportunities for new insurance products and coverages to directly address the risks that companies face. However, it is also changing the risk landscape of existing classes of business within non-life insurance where there is inherent risk of loss as a result of IT events that cannot be or have not been excluded in policy wordings or are changing the risk profile of traditional risks. This risk of losses to non-cyber classes of business resulting from cyber as a peril that has not been intentionally included (often by not clearly excluding it) is defined as non-affirmative cyber risk, and the level of understanding of this issue and the cyber peril exposure from non-cyber policies varies across the market. In contract wordings, the market has remained relatively “silent” across most lines of business about potential losses resulting from IT-related events, either by not addressing the potential issue or excluding via exclusions. Some classes of business recognise the exposure by use of write-backs. Depending on the line of business, the approach will vary as to how best to turn any “silent” exposure into a known quantity either by robust exclusionary language, pricing or exposure monitoring. This paper proposes a framework to help insurance companies address the issue of non-affirmative cyber risk across their portfolios. Whilst the framework is not intended to be an all-encompassing solution to the issue, it has been developed to help those tasked with addressing the issue to be able to perform a structured analysis of the issue. Each company’s analysis will need to tailor the basis of the framework to fit their structure and underwriting procedures. Ultimately, the framework should be used to help analysts engage with management on this issue so that the risk is understood, and any risk mitigation actions can be taken if required. In the appendix, we present a worked example to illustrate how companies could implement the framework. The example is entirely fictional, is focused on non-life specialty insurance, and is intended only to help demonstrate one possible way in which to apply the framework.
A note on the strong consistency of nonparametric estimation of Shannon entropy in length-biased sampling

In this article, we propose an estimator of Shannon entropy based on kernel estimators of density function in length-biased setting. The strong consistency of the proposed estimators is established. In addition, some simulations are conducted to evaluate the performance of the proposed estimator.
Ranking chat logs for crime investigation

Aiming at the needs of criminal investigators to quickly find chat logs related to crime case, a ranking method for chat texts has been proposed. First, query keywords were used to search chat texts, and the feedback texts were divided into relevant texts and irrelevant texts with manual annotation, then non-negative matrix factorization (NMF) was utilized to obtain the implicit semantic relationship of chat texts, finally chat logs could be ranked based on the scores calculated by similarity. Experiments show that the method proposed in this paper can quickly retrieve and get the messages of interest in a lot of chat logs, which can facilitate crime investigation.
Background Spatial Correlation Filter in Multi-Channel Object Tracking

Abstract Traditional correlation filters have high tracking efficiency, but because of the negative samples in the correlation filter that are obtained by moving the object patch itself, the real background information is not included in the sample composition. In this paper, the method for obtaining and detecting the background spatial information in multiple channel is given. A method that combines the background spatial information is integrated into the framework of correlation filter, and an experiment was carried out on existing publicly available datasets. The experimental results show that the tracking effect in some complicated scenes (e.g., illumination changes, fast motion, scale changes, and occlusion) is improved. This approach can effectively improve the success rate and accuracy of object tracking in complicated scenes without affecting the frame rate, obtaining a performance that is superior to that of other correlation filters.
Handwritten Character Segmentation in Tablet Based Application

Nowadays, modern tablets are widely used in the contribution of universal access to education, equality in the exercise of teaching and learning, aiming for more efficient management and administration. The recognition of handwritten characters on these tablets become a necessary consequence in this technology age. The main challenging of handwritten character recognition is splitting each character of unrestricted handwritten scripts and there is no complete solution yet according to our knowledge. In this system, the combination of sliding windows, Region of Interest (ROI) box and Convolutional Neural Network (CNN) are used to execute implicit segmentation of handwritten characters. For performing the experiments, our own dataset is constructed by collecting handwritten data from 24 members of our laboratory using three different tablet PC models.
Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling

Neuro-fuzzy systems based on a fuzzy model proposed by Takagi, Sugeno and Kang known as the TSK fuzzy model provide a powerful method for modelling uncertain and highly complex non-linear systems. The initial fuzzy rule base in TSK neuro-fuzzy systems is usually obtained using data driven approaches. This process induces redundancy into the system by adding redundant fuzzy rules and fuzzy sets. This increases complexity which adversely affects generalization capability and transparency of the fuzzy model being designed. In this article, the authors explore the potential of TSK fuzzy modelling in developing comparatively interpretable neuro-fuzzy systems with better generalization capability in terms of higher approximation accuracy. The approach is based on three phases, the first phase deals with automatic data driven rule base induction followed by rule base simplification phase. Rule base simplification uses similarity analysis to remove similar fuzzy sets and resulting redundant fuzzy rules from the rule base, thereby simplifying the neuro-fuzzy model. During the third phase, the parameters of membership functions are fine-tuned using a constrained hybrid learning technique. The learning process is constrained which prevents unchecked updates to the parameters so that a highly complex rule base does not emerge at the end of model optimization phase. An empirical investigation of this methodology is done by application of this approach to two well-known non-linear benchmark forecasting problems and a real-world stock price forecasting problem. The results indicate that rule base simplification using a similarity analysis effectively removes redundancy from the system which improves interpretability. The removal of redundancy also increased the generalization capability of the system measured in terms of increased forecasting accuracy. For all the three forecasting problems the proposed neuro-fuzzy system demonstrated better accuracy-interpretability tradeoff as compared to two well-known TSK neuro-fuzzy models for function approximation.
Elliptic curve cryptography arithmetic in terms of one variable polynomial division

This paper develops an approach for point addition and doubling on elliptic curves over finite fields using one variable polynomial arithmetic based on Euclidean division. This approach succeeds in computing these operations on realistic curves over large finite fields due to a striking observation about computing the gcd of two polynomials one which represents the elliptic curve as its roots and the other representing the lines which intersect or are tangent to the curve. This paper provides a verification of correctness of this approach for different finite fields. The resulting algorithm is tested on realistic elliptic curves and is shown to be practically scalable to perform the computations.
A Message Passing XSet for Path Finding Robotic Devices

Swarm intelligence systems where robotic devices encoded with primitive actions that are executed at individual levels in order to cause swarm level emergent behaviour are appealing to the fields of nanotechnology and bioinformatics. Interaction between robotic devices allow improved swarm level properties with features more than the sum of the contributions of the individual robotic devices that form the swarm. However, it is challenging to pinpoint particular primitive actions which drive robotic devices towards deliberately engineered emergent behaviour. We propose an XSet model inspired by the behaviours of message passing agents. An XSet model supports direct device to device interactions in which implicit communication spaces arise. In this context, an XSet puts together primitive actions, parameters, and meta information which stipulates when primitive actions are useful. We assess path finding abilities of message passing robotic devices and compared them to the relative performances of random wandering and stigmergic counterparts. Better message passing performances are observed when time in simulation is sufficiently long, and when the population of robotic devices in the swarm is high. Beside presenting a new swarm control model, the proposed XSet brings us closer to the identification of generalized swarm control rules.