Reviewerly/exHarmony huggingface dataset:
Analysis of Novel Hybrid Encryption Algorithm for Providing Security in Big Data

The word “SECURITY” play’s an important role in the area of the Database. The massive volume of the data is been collected every day which is been stored, transferred over the network from one distant location to the other to provide the availability of the data to the end user. To make the data secure in the network or into the drives where it is stored need some security measures like encryption of the data into the storage device and over the network. There are many encryption algorithms which are been used to secure the data. So that it must be made as confidential in the sense of end user, that its data is protected. Transferring and storing of the data in a plaintext format may create a risk of hacking by the attacker or maybe miss used by the attacker. In order to get prevented by this kind of attacks, proper encryption/authentication must be performed. Or proper mechanism must be imposed on the system so that the data which is been transferred or stored must be secured. Use of various encryption/decryption algorithms like AES, 3DES, RSA and many more are preventing it nowadays but we can create a new algorithm by merging the two algorithms of a different nature to protect our data from the attacker. In this Research, RSA 2048-bit algorithm and AES 256-bit will be implemented using ASP.Net or Java and cryptool 2.0. Securing the Big-Data application is intended to suit security administrations, for example, classification, trustworthiness, verification, and non-disavowal.
Improved Iterative Convergence Rate for Soft-Decision Bit-Level Reed-Solomon Decoders using Information Set Decoding

Iterative bit-level decoders for Reed-Solomon codes, such as the ABP and the PTA, have been shown in literature to yield a good error correction performance. However, this is often at the cost of a high computational complexity. This complexity can be attributed to the number of iterations required during the decoding process. This paper focuses on improving the iterative convergence rate of the bit-level decoders with aid of an additional stopping criteria based on information set decoding techniques. The additional stopping criteria is able to reduce the number of iterations by obtaining an information set and using it to decode the entire received vector. This decoding approach significantly reduces the number of iterations required during decoding of the received vector, while still maintaining the good error correction performance of iterative bit-level decoders.
Towards hyperentangled time-bin and polarization superdense teleportation in space

Quantum communication networks based on fiber optics are restricted in length since efficient quantum repeaters are not yet available. A free-space channel between a satellite in orbit and Earth can circumvent this problem. We have constructed a system to demonstrate the feasibility of quantum communication between space and earth using photons hyperentangled in their polarization and time-bin degrees of freedom. With this system, we have implemented superdense teleportation (SDT) with a fidelity of 0.94&plusmn;0.02. To increase the efficiency of SDT, we have developed an active, polarization-independent switch compatible with SDT. We characterized the performance of its switching efficiency. Finally, we have constructed a novel two-level interferometer for time-bin qubit creation and analysis in orbit, and bounded its stability.
Catastrophic Interference in Disguised Face Recognition

It is commonly known the natural tendency of artificial neural networks to completely and abruptly forget previously known information when learning new information. We explore this behaviour in the context of Face Verification on the recently proposed Disguised Faces in the Wild dataset (DFW). We empirically evaluate several commonly used DCNN architectures on Face Recognition and distill some insights about the effect of sequential learning on distinct identities from different datasets, showing that the catastrophic forgetness phenomenon is present even in feature embeddings fine-tuned on different tasks from the original domain.
Integrated Secure Architecture for Live Virtual Machine (VM) Migration in Cloud – A Survey

The core of Cloud computing includes virtualization of hardware resources such as storage, network and memory provided through virtual machines (VM). The live migration of these VMs is introduced to obtain multiple benefits which mainly include high availability, hardware maintenance, fault takeover and workload balancing. Besides various facilities of the VM migration, it is susceptible to severe security risks during migration process due to which the industry is hesitant to accept it. The research done so far is on the performance of migration process; whereas the security aspects in migration are not fully explored. We have carried out an extensive survey to investigate the vulnerabilities, threats and possible attacks on the live VM migration. Furthermore, we have identified security requirements for secure VM migration and presented a detailed analysis of existing solutions on the basis of these security requirements. Finally, limitations in the existing solutions are presented.
Reason-maintenance Belief Logic with Uncertain Information

In this article, we propose a logic for reasoning about belief based on fusion of uncertain information. The resultant reason-maintenance possibilistic belief logic can represent both implicit and explicit uncertain beliefs of an agent. While implicit beliefs stipulate what are believable, explicit beliefs can trace the process of belief formation by information fusion. To set up the formal framework, we start with developing a basic reason-maintenance belief logic, present its syntax and semantics, and investigate its axiomatization and properties. Then, we extend the basic logic to accommodate the possibilistic uncertainty of information and beliefs, provide a complete axiomatization of the extended logic, and show that it can address the reason-maintenance issue of partially inconsistent beliefs. We also demonstrate the applicability of our formalisms by using several examples in realistic scenarios.
A Tag-Based Group Recommendation Algorithm

Existing algorithms in group recommendation usually focus on studying the rating data instead of the social tag data. Finding the group based on tags is a novel problem for group recommendation. In this paper, we first employ locality-sensitive hashing to quickly find potential user groups of similar types. Next, we adopt higher-order singular value decomposition to explore the potential semantic relevance among the ternary relationship data of <user-item-tag> and fill the sparse data of users for items. Finally, we apply a fairness aggregation strategy based on proportionality and envy-freeness to generate the fairest group recommendation list that is simulated to address the problem of finding the maximum coverage. We use the MovieLens dataset to demonstrate that the fairness strategy proposed here has a better fairness. What's more, under the nDCG indicator, the algorithms based on the three strategies all generated a fairly good recommendation quality.
Track Control of Complex-valued Recurrent Neural Networks Based on the Hanaly’s Inequality

This paper discusses the track control problem of complex-valued neural networks (CVNNs) with time delay. By defining the suitable norm, we directly investigate globally asymptotically stable of CVNNs. With the Hanaly's inequality and analysis techniques, we derive a sufficient condition which can make the state of CVNNs globally exponentially track the given reference trace. At last, one illustrative example verifies our result.
Micro-Hypervisor Applications

This chapter showcases several micro-hypervisor-based applications (“uberapps”) that employ uberXMHF. These uberapps span a wide spectrum of security applications including application compartmentalization and sandboxing, attestation, approved code execution, key management, tracing, verifiable resource accounting, trusted-path, and on-demand I/O isolation. They showcase the efficacy and versatility of a micro-hypervisor-based system security architecture.
Clinical Risk Assessment and Prediction

Medicine is by definition a personalized endeavor. Hippocrates emphasized the importance of integrating information about the specific individual and their environment into clinical judgment (Kleisiaris et al., J Med Ethics Hist Med 7:6, 2014). Modern western medicine increasingly makes use of formal Personalized and Precision Medicine (PPM) modalities such as growth curves, risk biomarkers, disease staging, and statistical models. This chapter provides an overview of these tools, describes their contexts of applications, and summarizes their adoption to healthcare. A workflow for developing clinical risk models is also established.
The Application of Text Categorization Technology in Adaptive Learning System for Interpretation of Figures

With the deepening of globalization and the increasing amount of information in international communication, requirements for accuracy in interpretation of figures are more demanding. Training methods in interpretation of figures, however, are not efficient enough for interpreters to cope with the challenges. They still make mistakes in interpretation of large integers, fractions and percentages. The types of errors include omission, syntactic error and lexical error. In this paper, machine learning based text categorization technology is used to accurately categorize a large number of texts and provide high-quality training materials for interpreters. Results show that training the interpreters with categorized texts has greatly improved the accuracy, familiarity and sensitivity in interpretation of figures. In the era of artificial intelligence, problems in interpretation also need to be solved by artificial intelligence. In the future, a large number of artificial intelligence technologies similar to machine-learning-based text categorization technology will be inevitably adopted in the field of interpretation.
BASIC WAYS OF USING MOBILE DEVICES AT THE MATHEMATICS AND INFORMATICS LESSONS

The various possibilities of using mobile devices at the math and informatics lessons are discussed in the article. Three main alternatives for the use of mobile devices in the educational process are highlighted. The first alternative is the mobile device instead of specific tool. Next alternative is the mobile device while it is a tool for a working with specialized and universal applications. Last alternative is the mobile device helping to use augmented reality.
Collective Behavior Under the Umbrella of Blockchain

Any social or organization system will fetch the properties from economics, sociology, and social psychology. In the digital world everyone is trying to cope with the new technologies for the survival. The dynamics of such a system are very multifarious due to the complexity in the convergence of the digital, physical, and biological realms. The dynamics of the society and organization are rapidly changing due to the imparting of the new technologies, such as artificial intelligence, internet of things, virtual reality, etc. The resultant is revolutionizing of opportunities and expectations due to the changes in the values, norms, identities, and future potential. The collective behavior (CB) plays an important role in predicting the various dynamics which are not only coherent but also paying attention. Blockchain will not only help in detecting but also help in finding the major causes and challenges for current scenario dynamics. The chapter describes the agent-based modeling and ant colony optimization components of the CB.
Application of image navigation algorithm in UAV intelligent patrol channels

Traditional image navigation system is realized by map matching or location of surrounding scene. However, this method is difficult to be applied to the field of inspection channel and other applications. In this paper, a navigation algorithm based on image processing is proposed, and experimental research is carried out in channel inspection of UAV. The gray image of channel is obtained by image preprocessing, the edge information is extracted by the Canny operator, and the position information and angle information of channel are obtained by the Hough detection. Finally, the UAV flight attitude adjustment is realized according to the position information and angle information of channel.
Research on binocular clear image hazing algorithm based on depth map

At present, most of the dehazing algorithms are the dehazing process of a single hazy image, and the hazy image resources taken by the binocular camera are scarce. Therefore, in order to get rid of the weather restrictions, to more clearly study the hazy images mechanism and increase the data source of binocular hazy images, so as to lay a foundation for the later research on the dehazing algorithm based on binocular stereo vision. This paper proposes a binocular clear image hazing algorithm based on depth map, which combines atmospheric attenuation models with the characteristics of hazing image depth, and derives a model of the hazy image and then simulates the effect of the hazy image on this basis. In practical applications, simply take a clear image and you can get the desired stereo hazy image with this hazing model. Thereby, it provides convenience for the shooting of haze scene effects such as 3D movies and TV dramas, and has a very large practical application value.
Enacting Sonic-Cyborg Performance through the Hybrid Body in <i>Teka-Mori</i> and <i>Why Should Our Bodies End at the Skin?</i>

In “A Cyborg Manifesto,” Donna Haraway explores implications of the increasing hybridization of humans and machines. While society has long been concerned with the encroachment of technology onto human activity, Haraway challenges this concern, suggesting instead a kinship between organism and machine, a hybrid body. A sonic-cyborg performance realizes this understanding of the human-machine hybrid through movement and sound, incorporating a “kinesonic” approach to composition and an exploration of “mechatronic” expression. In this article, the authors describe their approach to enacting sonic-cyborg performance by outlining the creative framework and associated technologies involved in two collaborative pieces that explore questions of fluidity between organism and machine: Teka-Mori and Why Should Our Bodies End at the Skin?
Classifying Relation via Piecewise Convolutional Neural Networks with Transfer Learning

Relation classification is an important semantic processing task in natural language processing (NLP). Traditional works on relation classification are primarily based on supervised methods and distant supervision which rely on the large number of labels. However, these existing methods inevitably suffer from wrong labeling problem and may not perform well in resource-poor domains. We thus utilize transfer learning methods on relation classification to enable relation classification system to adapt resource-poor domains along with different relation type. In this paper, we exploit a convolutional neural network to extract lexical and syntactic features and apply transfer learning approaches for transferring the parameters of convolutional layer pre-training on general-domain corpus. The experimental results on real-world datasets demonstrate that our approach is effective and outperforms several competitive baseline methods.
What are the features of shapes easy to remember in the visual search?

How do we find objects in scenes? When we recognize things in the outside world, we feel that we recognize shapes as one entire object, not an ensemble of inconsistent characteristics. In perception theory, it is thought that features are integrated in structures that define objects, but how these structures are maintained in the working memory remains unknown. Therefore submitting clear evidence of the features binding in the memory is the first goal of this study. In this experiment, firstly the target is displayed. Then a panel is shown with the target and some distractors. Firstly, we generated eight different individual targets in this experiment. We show the targets in different presentation patterns: for each target, the presentation time is 300ms or 600ms, and the number of distractors can be 15 and 23 for a total of four different trial combinations. We measured the correct answer rate and the reaction time in those four tasks. The targets are generated with the using Genetic Algorithm. The targets more easy to remember will have a lower reaction time and better correct answer rate. Using these results, the genetic algorithm produces next generation targets in order to be more easy to remember. As a result, the “correct answer” rate is 51% at stimulation time of 300ms, 61% at 600ms in the first generation, and it is 79% at 300ms, 73% at 600ms in the second generation. In addition, we found that in the second generation there is a clear reduction of reaction times. When the number of distractors increases we found that the reaction time gets longer and this is preserved in the second generation too. This behavior is in accord with the visual search “conjunction search” theory. We will describe our Genetic Algorithm model able to generate targets easy to remember.
Model Free Approach to Adaptive Parameters Optimisation using Adaptive Decaying Merit Score (ADMS): A Preliminary Study

Adaptive parameter control is a proven method to increase efficiency of any optimisation algorithm and have has been universally proven on various benchmark problems. In this research report, a Universal model free method of adaptive control of optimisation parameters was proposed. The goal of the research is to explore the method that can be used for any model of optimisation algorithm (`Swiss army knife' approach). The proposed method utilises a score system in which points are rewarded for improvement found on individual agent performance while non-improving will experience a score-decay. The proposed method was tested on two types of common optimisation algorithm, Truncated Lěvy flight and the Differential Evolution (DE). This Adaptive parameter strategy is solely based on search agent's performance and hence is not algorithm specific (model free). As a preliminary study, this report will only focus on Lower Dimensional problems (D=10) using CEC 2014 benchmark test sets. The results showed that it work well on DE but gave mediocre results for Le \checkvy Flight when compared with their static parameter counterparts. This showed performance varies across algorithms types but verified the adaptive capability of the proposed method.
A Nonlocal Feature-Driven Exemplar-Based Approach for Image Inpainting

We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework.The recovery of geometric structures is achieved by using general convolution operators as a measure of behavior within an image.These are combined with a nonlocal exemplar-based approach to exploit the self-similarity of an image in the selected feature domains and to ensure the inpainting of textures.We also introduce an anisotropic patch distance metric to allow for better control of the feature selection within an image and present a nonlocal energy functional based on this metric.Finally, we derive an optimization algorithm for the proposed variational model and examine its validity experimentally with various test images.
A Model for Classification of Breast Cell Density Using ANN and Shift Invariance Wavelet Transform ConvNet

Breast cancer is among world’s second most happening cancer in a wide range of cancer. Early location of cancer followed by the best possible treatment can decrease the danger of passings. AI can assist restorative experts with diagnosing the illness with more precision. Where deep learning or neural networks is one of the strategies which can be utilized for the characterization of ordinary and strange breast detection. This exploration presents a double tree complex valued discrete wavelet transform constructed in ConvNet and ANN to conduct breast cell density grouping out of mammography. For mammogram image characterization assignments, customary Convolutional Neural Networks (ConvNet) are: (1) slanted to disregard significant surface data of the image because of the constraints of pooling methodologies, and (2) inadequately vigorous to commotion. To defeat the hindrances, an other area transformation procedure is embraced in ConvNet. In ConvNet (WConvNet) the convolution layer is connected with double-tree complex valued wavelet Through complex valued discrete wavelet transformation, the picture winds up flexible dimensional way, enabling exact characterization. The WConvNet deteriorates the image into various wavelet subbands, and lessens boisterous information. The exhibition of WConvNet is tried on MIAS datasets, and connected for early conclusion of cancer. Contrasted with the conventional ConvNets utilizing maximum valued pooling, trial outcome show that the WConvNet method acquires remarkable evenness and exactness. The study is finished utilizing 322 images of the MIAS database and has brought about characterization achievement rates running from 90% to 94.00% for various breast cell density category (Scattered fibroglandular density, Heterogeneously dense, Extremely dense).
Addressing Information Overload through Text Mining across News and Social Media Streams

The state-of-the-art in topic detection and tracking, structured summarization and news recommendation has moved to alternative document representations beyond keywords, in an attempt to utilize the available metadata in the form of timestamps, entities, topics, categories, author information, sentiment, political stance. Yet, despite the availability and the advantages of social metadata, only a few methods have attempted to utilize social annotations for document representation beyond social posts. This report briefly introduces the use of social annotations for news in near-real-time settings and answers the question - Are the social annotations useful for tackling single-domain multiple-document tasks in the news domain, such as topic detection and tracking or story structure extraction?
Infusing Software Quality Concepts in Computer Science Engineering Courses

There is a continuous complaint from industry about the poor quality of computer science engineering graduates. This paper examines the recommendations of various agencies like NBA (National Board of Accreditation), AICTE (All India Council for Technical Education), ABET (Accreditation Board for Engineering and Technology) and ACM-IEEE Software Engineering 2014 curriculum and finds a gap in AICTE model curriculum 2018. A survey was administered to assess faculty's perception of the student project quality with respect to software product quality standard ISO/IEC 25023. This paper opens a way for designing software quality framework that can be integrated within the existing curriculum to bridge the gap.
Multi-layered edits for meaningful interpretation of textual differences

The way humans and algorithms look at and understand differences between versions and variants of the same text may be very different. While correctness and overall byte length are fundamental aspects of good outputs of diff algorithms, they do not usually provide immediately interesting values for humans trying to make sense of the events that lead from one version to another of a text.
Models for Web Applications Data Analysis

Data analysis is a data inspecting and transforming process for the purpose of making conclusions and obtaining useful information to help make a decision about disclosed forecasts. There are numerous technologies and methods for analyzing data that are mainly determined by the type of data.