Reviewerly/exHarmony huggingface dataset:
Detailed Review of Different Security Techniques for Data Protection in Cloud Computing

Now, a day’s Data protection in cloud has become a vital and tremendously increasing technology. Many international organizations are listening in cloud computing and its wonderful features but they're troubled about the protection, privacy, and availability of data because it rests in the cloud. Cloud computing contains plenty of security issues that are in advance great attention nowadays, as well as the information shield, network security, virtualization security, application integrity, and identity management. Data protection is one among the foremost important security issues, because organizations won’t relocate its data to remote machines if there isn't any guaranteed data protection from the cloud service providers. A lot of techniques are recommending for data protection in cloud computing, but there are still plenty of challenges during this subject. The leading popular security techniques contain SSL (Secure Socket Layer) Encryption, Intrusion Detection System; Multi contract based Access Control, etc. also, security techniques for data protection are visiting recommending so on acquire improved security in cloud computing.
Background subtraction for night videos

Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training.
Photonics for AI and AI for Photonics: Material and Characteristics Integration

We are living in the technological era, where everything is integrated with each other. If we are discussing regarding communication, it is integrated with one or two technologies. If we are discussing regarding automation, discussing regarding Image processing, discussing regarding embedded system, they all are integrated with a combination of technologies. Correspondingly Artificial Intelligence (AI) and Photonics are also integrated with each other. Now a day as AI is utilizing with photonics in abundant fields as well photonics is also serving AI to facilitate ultrafast AI networks to offer a novel class of Information Processing Machines (IPM). This chapter is based on identification and implementation of photonics for AI utility and AI for photonics. In this category a Dual core Photonics crystal fiber (PCF) is proposed which serve to identify infected cells of human being along with the integration of AI. This proposed design of PCF is providing relative sensitivity and confinement loss in an optimized manner with the impact of AI. Here potency of AI as well as of Photonics is explained to serve their applications related to each other.
Tech Tackles Violent Crime

KNIFE CRIME offences in the UK rose by 7 per cent in the 12 months to the end of June 2019, reaching a record high. It's a worrying trend, both in itself and because it makes people feel less safe on the streets. But there are advanced technological solutions to halting attacks, including the use of controversial predictive policing tools.
Kernel Continual Learning

This paper introduces kernel continual learning, a simple but effective variant of continual learning that leverages the non-parametric nature of kernel methods to tackle catastrophic forgetting. We deploy an episodic memory unit that stores a subset of samples for each task to learn task-specific classifiers based on kernel ridge regression. This does not require memory replay and systematically avoids task interference in the classifiers. We further introduce variational random features to learn a data-driven kernel for each task. To do so, we formulate kernel continual learning as a variational inference problem, where a random Fourier basis is incorporated as the latent variable. The variational posterior distribution over the random Fourier basis is inferred from the coreset of each task. In this way, we are able to generate more informative kernels specific to each task, and, more importantly, the coreset size can be reduced to achieve more compact memory, resulting in more efficient continual learning based on episodic memory. Extensive evaluation on four benchmarks demonstrates the effectiveness and promise of kernels for continual learning.
Technical Domain Classification of Bangla Text using BERT

Coarse-grained tasks are primarily based on Text classification, one of the earliest problems in NLP, and these tasks are done on document and sentence levels. Here, our goal is to identify the technical domain of a given Bangla text. In Coarse-grained technical domain classification, such a piece of the Bangla text provides information about specific Coarse-grained technical domains like Biochemistry (bioche), Communication Technology (com-tech), Computer Science (cse), Management (mgmt), Physics (phy) Etc. This paper uses a recent deep learning model called the Bangla Bidirectional Encoder Representations Transformers (Bangla BERT) mechanism to identify the domain of a given text. Bangla BERT (Bangla-Bert-Base) is a pretrained language model of the Bangla language. Later, we discuss the Bangla BERT accuracy and compare it with other models that solve the same problem.
Authorship Attribution Using Capsule-Based Fusion Approach

Authorship attribution is an important task, as it identifies the author of a written text from a set of suspect authors. Different methodologies of anonymous writing, have been discovered with the rising usage of social media. Authorship attribution helps to find the writer of a suspect text from a set of suspects. Different social media platforms such as Twitter, Facebook, Instagram, etc. are used regularly by the users for sharing their daily life activities. Finding the writer of micro-texts is considered the toughest task, due to the shorter length of the suspect piece of text. We present a fusion based convolutional Neural Network model, which works in two parts i) feature extraction, and ii) classification. Firstly, three different types of features are extracted from the input tweet samples. Three different deep-learning based techniques, namely capsule, LSTM, and GRU are used to extract different sets of features. These learnt features are combined together to represent the latent features for the authorship attribution task. Finally the softmax is used for predicting the class labels. Heat-maps for different models, illustrate the relevant text fragments for the prediction task. This enhances the explain-ability of the developed system. A standard Twitter dataset is used for evaluating the performance of the developed systems. The experimental evaluation shows that proposed fusion based network is able to outperform previous methods. The source codes are available at https://github.com/chanchalIITP/AuthorIdentificationFusion .
Distortion correction based on off-axis Schwarzschild two-mirror hyperspectral imager

A new method of distortion correction based on the off-axis Schwarzschild two-mirror hyperspectral imager is proposed in this paper. The polynomial model is used to indicate the distortion in the pushbroom direction and the correction method is based on linear feature. The radial distortion model is used to indicate the vertical pushbroom direction of distortion and the correction method is based on the cross ratio invariance. Compared with the common precision angle measurement method, this method is simple and efficient. This method can be applied to the distortion correction of the non-mapping pushbroom hyperspectral imager.
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Word Embeddings and the Implications to Representation Learning

Human judgments of word similarity have been a popular method of evaluating the quality of word embedding. But it fails to measure the geometry properties such as asymmetry. For example, it is more natural to say ``Ellipses are like Circles'' than ``Circles are like Ellipses''. Such asymmetry has been observed from the word evocation experiment, where one word is used to recall another. This association data have been understudied for measuring embedding quality. In this paper, we use three well-known evocation datasets for the purpose and study both static embedding as well as contextual embedding, such as BERT. To fight for the dynamic nature of BERT embedding, we probe BERT's conditional probabilities as a language model, using a large number of Wikipedia contexts to derive a theoretically justifiable Bayesian asymmetry score. The result shows that the asymmetry judgment and similarity judgments disagree, and asymmetry judgment aligns with its strong performance on ``extrinsic evaluations''. This is the first time we can show contextual embeddings's strength on intrinsic evaluation, and the asymmetry judgment provides a new perspective to evaluate contextual embedding and new insights for representation learning.
Design, Analysis, and Implementation of a Twofactor Authentication Scheme using Graphical Password

With the increase in the number of e-services, there is a sharp increase in online financial transactions these days. These services require a strong authentication scheme to validate the users of these services and allow access to the resources for strong security. Since two-factor authentication ensures the required security strength, various organizations employ biometric-based or Smart Card or Cryptographic Token-based methods to ensure the safety of user accounts. But most of these methods require a verifier table for validating users at a server. This poses a security threat of stolen-verifier attack. To address this issue, there is a strong need for authentication schemes for e-services that do not require a verifier table at the server. Therefore, this paper proposes the design of an authentication scheme for eservices which should be resistant to various attacks including a stolen verifier attack. The paper will also discuss: 1) The proposed scheme analyzed for security provided against the known authentication attacks 2) The concept mplementation of the proposed scheme.
Zero-Shot Text-to-Speech for Text-Based Insertion in Audio Narration

Given a piece of speech and its transcript text, text-based speech editing aims to generate speech that can be seamlessly inserted into the given speech by editing the transcript. Existing methods adopt a two-stage approach: synthesize the input text using a generic text-to-speech (TTS) engine and then transform the voice to the desired voice using voice conversion (VC). A major problem of this framework is that VC is a challenging problem which usually needs a moderate amount of parallel training data to work satisfactorily. In this paper, we propose a one-stage context-aware framework to generate natural and coherent target speech without any training data of the target speaker. In particular, we manage to perform accurate zero-shot duration prediction for the inserted text. The predicted duration is used to regulate both text embedding and speech embedding. Then, based on the aligned cross-modality input, we directly generate the mel-spectrogram of the edited speech with a transformer-based decoder. Subjective listening tests show that despite the lack of training data for the speaker, our method has achieved satisfactory results. It outperforms a recent zero-shot TTS engine by a large margin.
White Paper - IEEE Federated Machine Learning

Data privacy and information security pose significant challenges to the big data and artificial intelligence (AI) community as these communities are increasingly under pressure to adhere to regulatory requirements, such as the European Union’s General Data Protection Regulation. Many routine operations in big data applications, such as merging user data from various sources in order to build a machine learning model, are considered to be illegal under current regulatory frameworks. The purpose of federated machine learning is to provide a feasible solution that enables machine learning applications to utilize the data in a distributed manner that does not exchange raw data directly and does not allow any party to infer private information of other parties. This white paper intends to present an overview of the Federated Machine Learning (FML) technology that can be used as a basis for standards, certifications, laws, policies, and/or product ratings. This white paper targets an educated audience, including lawmakers, corporate and governmental policy makers, manufacturers, engineers, and standard setting bodies. However, this white paper is also easily understood by non?technical managers and policy makers as it provides system developers and manufacturers with an overview of Federated Machine Learning techniques. Finally, one must give credit to the IEEE Federated Machine Learning (P3652.1) working group participants for their tremendous dedication, expertise and thoughtful collaborations, without which the publication of IEEE Std 3652.1?2020 [1] would not have been possible .
Rule Augmented Unsupervised Constituency Parsing

Recently, unsupervised parsing of syntactic trees has gained considerable attention.A prototypical approach to such unsupervised parsing employs reinforcement learning and auto-encoders.However, no mechanism ensures that the learnt model leverages the wellunderstood language grammar.We propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic rules, thus inducing better syntactic structures.We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.We achieve new state-of-the-art results on two benchmarks datasets, MNLI and WSJ.
Ternary and Quaternary Curves of Small Fixed Genus and Gonality With Many Rational Points

We extend the computations from our previous work to determine the maximum number of rational points on a curve over F3 and F4 with fixed gonality and small genus. We find, for example, that there is no curve of genus 5 and gonality 6 over a finite field. We propose two conjectures based on our data. First, an optimal curve of genus g has gonality at most ⌊g+32⌋. Second, an optimal curve of gonality γ and large genus over Fq has γ(q+1) rational points.
Dynamic Automaton-Guided Reward Shaping for Monte Carlo Tree Search

Reinforcement learning and planning have been revolutionized in recent years, due in part to the mass adoption of deep convolutional neural networks and the resurgence of powerful methods to refine decision-making policies. However, the problem of sparse reward signals and their representation remains pervasive in many domains. While various rewardshaping mechanisms and imitation learning approaches have been proposed to mitigate this problem, the use of humanaided artificial rewards introduces human error, sub-optimal behavior, and a greater propensity for reward hacking. In this paper, we mitigate this by representing objectives as automata in order to define novel reward shaping functions over this structured representation. In doing so, we address the sparse rewards problem within a novel implementation of Monte Carlo Tree Search (MCTS) by proposing a reward shaping function which is updated dynamically to capture statistics on the utility of each automaton transition as it pertains to satisfying the goal of the agent. We further demonstrate that such automaton-guided reward shaping can be utilized to facilitate transfer learning between different environments when the objective is the same.
Waste Segregation to Ease Recyclability

A multi-label image classification is used to classify an input image into one of the labeled classes of the dataset. Authors are trying to build an image classification model to classify solid waste into different categories to make the process of recycling automated and efficient. The objective of this paper is to improve the efficiency of the multi-class image classification by using capsule neural network. The capsule neural network will take the input of single piece image of solid waste and classify it into one of the six different classes of cardboard, glass, metal, paper, plastic and trash. The dataset of Thung and Yang research paper has been used, and more images are added to extend the dataset and make it suitable for training the model. By using capsule neural network, this paper has proved that even small dataset of images can also be used to train the model to enhance the accuracy of image classification. This task marks a major contribution to the environment due to large amount of waste generated daily, but only a small amount of waste is segregated and recycled. The proposed model gives an accuracy of 79.27% when trained with the dataset of 4956 images, while in Thung and Yang model [1], the accuracy was 22% with a dataset of 2500 images. This will bless the world with the hygienic way of disposing and recycling waste.
Teaching transparency: principles and practical considerations with illustrations in R and Stata

This chapter advocates incorporating principles and practices of research transparency and reproducibility into undergraduate research methods courses. These principles and practices are increasingly essential and should be incorporated into methods courses as early as possible in order to develop an awareness of transparency and good habits. The chapter proceeds in five sections: (1) a clarification about what constitutes transparency; (2) a discussion of why transparency is important, including a the ongoing reproducibility crisis across disciplines, as well as material and nonmaterial motivations for transparency; (3) practices of transparency, including illustrations in R and Stata; and (5) a discussion of implementation tradeoffs. The conclusion closes with a discussion of the ongoing debates about transparency in our discipline and the need for a diversity of approaches.
A contingent review on cloud computing trends predicting viable possibilities for future of computing

Cloud computing was maybe the wildest stage in innovation as well as business press all through 2019. This's absolutely nothing unexpected as the cloud sector has been creating quickly throughout the final several years. Synergy Research by Group a short while ago detailed a thirty-seven % by and huge development year-over-year within the public cloud. They as well bear in mind it's taken just 2 years for the open PaaS and IaaS marketplaces to twofold in size as well as their conjecture provides them multiplying once again within the following 3 years. As year that is New begins with targets in the lives of ours, likewise, for that IT business additionally constantly accompanies an objective in Cloud Computing. Cloud is cost-productive, robust, and scalable. Cloud innovation is beneficial for program development, using the cloud for specific application development has to be common. We're just beginning to view the improvement of this concept into a transformation. Cloud computing changes the fashion in that we consider information, the manner where businesses consider the operations of theirs as well as the manner where engineers consider building. Let us take a look at several of the cloud computing trends which will take place in deep 2020 in terms of serverless computing, omni cloud, Quantum computing. Kubernetes, digital natives and here try to predict the future scope of computing in terms of compute, network, store and mark analytics.
A Deontic Stit Logic Based on Beliefs and Expected Utility

The formalization of action and obligation using logic languages is a topic of increasing relevance in the field of ethics for AI. Having an expressive syntactic and semantic framework to reason about agents' decisions in moral situations allows for unequivocal representations of components of behavior that are relevant when assigning blame (or praise) of outcomes to said agents. Two very important components of behavior in this respect are belief and belief-based action. In this work we present a logic of doxastic oughts by extending epistemic deontic stit theory with beliefs. On one hand, the semantics for formulas involving belief operators is based on probability measures. On the other, the semantics for doxastic oughts relies on a notion of optimality, and the underlying choice rule is maximization of expected utility. We introduce an axiom system for the resulting logic, and we address its soundness, completeness, and decidability results. These results are significant in the line of research that intends to use proof systems of epistemic, doxastic, and deontic logics to help in the testing of ethical behavior of AI through theorem-proving and model-checking.
Scalable Partial Explainability in Neural Networks via Flexible Activation Functions (Student Abstract)

Current state-of-the-art neural network explanation methods (e.g. Saliency maps, DeepLIFT, LIME, etc.) focus more on the direct relationship between NN outputs and inputs rather than the NN structure and operations itself, hence there still exists uncertainty over the exact role played by neurons. In this paper, we propose a novel neural network structure with Kolmogorov-Arnold Superposition Theorem based topology and Gaussian Processes based flexible activation function to achieve partial explainability of the neuron inner reasoning. The model feasibility is verified in a case study on binary classification of the banknotes.
Human Fall Detection Using Motion History Image and SVM

Fall detection system is a most challenging system in artificial intelligence research, especially in healthcare. It is very vital for elderly people who are frequent victims of age-related factors. In this paper, we propose a vision-based fall detection system. This paper compares the performance of HOG (Histogram of Oriented Gradients) features and Motion History Images. The feature extracted is given to SVM (Support Vector Machine) classifier for classifying fall or non-fall movements in the image. UMA fall dataset is used for the experiment. Experiment result shows Motion History Image features gives better performance than the hog features. Further, the performance of Motion History Image based SVM classifier is tested on many other data sets, including the author’s own dataset.
Assurance on data integrity in cloud data centre using PKI built RDIC method

Cloud computing has become a standard in many business applications. It is popular because cloud computing makes it possible to access large volume of data/information ubiquitously. Due to attractive options, many organizations/associations prefer to store their data/information in cloud. However simultaneously a couple of ventures are not using cloud offices. The security dangers are the reasons why distributed computing isn’t used by all. There are various kinds of security challenges in Cloud structure. Such risks are recognized as perils to secrecy, honesty, accessibility, responsibility and security. In this work, we center around the information/data trustworthiness as a significant quality of security. At the hour of putting away data to cloud, customer doesn’t have even a thought where the data is put away. The security of the client’s information should be ensured to preserve the integrity of the information.The proposed method based on PKI built RDIC method ensures the rightness of stored data in the cloud. It is confirmed with the assistance of outsider verifier.