Reviewerly/exHarmony huggingface dataset:
Overview and Performance Analysis of Encryption Algorithms

Abstract Data protection is a complex problem affecting many areas including computers and communications. To ensure the necessary protection of applications, cryptographic methods and various algorithms are used. In this paper general overview of cryptography, information security and the comparison of algorithms with symmetric and asymmetric keys is presented. Factors ensuring the achievement of efficiency, flexibility and security are the basis of this research. As a result, the best solution for symmetric and asymmetric encryption is produced.
Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data

Depth completion aims to predict a dense depth map from a sparse depth input. The acquisition of dense ground truth annotations for depth completion settings can be difficult and, at the same time, a significant domain gap between real LiDAR measurements and synthetic data has prevented from successful training of models in virtual settings. We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain or additional sensors. Our approach simulates the real sensor noise in an RGB + LiDAR set-up, and consists of three modules: simulating the real LiDAR input in the synthetic domain via projections, filtering the real noisy LiDAR for supervision and adapting the synthetic RGB image using a CycleGAN approach. We extensively evaluate these modules against the state-of-the-art in the KITTI depth completion benchmark, showing significant improvements.
A Deep-forest based approach for detecting fraudulent online transaction

Fraudulent transaction is the one of the most serious threats to online security nowadays. Artificial Intelligence is vital for financial risk control in cloud environment. Many studies had attempted to explore methods for online transaction fraud detection; however, the existing methods are not sufficient to conduction detection with high precision. In this chapter, we propose a Deep-forest based approach for online transactions fraud detection, which integrates differentiation feature generation method and deep-forest based model. As a single-time transaction's information, which does not contain information such as the user's behavior, is not sufficient for detecting fraudulent transaction, we introduce a transaction time-based differentiation feature generation method into our scheme. Individual Credibility Degree (ICD) and Group Anomaly Degree (GAD), which are based on transaction time, are derived to distinguish between legal and fraudulent transaction. Furthermore, to deal with the extreme imbalance of online transactions, we apply Deep-forest algorithm to detect fraudulent transactions. While raw deep-forest model could ignore the outlier transaction samples, we enhance the raw Deep forest with detection mechanism for outliers, paying more attention on outliers to promote the precision of fraud detection model. Finally, we conduct test using one bank's transaction data. Compared with random Forest-detection model, our method improves precision rate by 15% and recall rate by 20%.
Machine translation model for effective translation of Hindi poetries into English

The Word Sense Disambiguation (WSD) is a process of disambiguating the sense of the text according to its context. Machine translation is one of the challenging task since it requires effective representation of the text to capture semantic relation between Hindi lyrics in English normal language behaviour. This paper focuses on WSD methods to deal with dialects that convert Hindi lyrics to English in its syntactic structure of the words. WSD is a phenomenon for disambiguating the text so that machine would be capable to deduce correct sense of individual given words. WSD is critical for solving natural language tasks such as Machine Translation (MT) and speech processing. The distinguishing proof of significant words in Hindi as the language is not as simple as that of dialects in English. The interpretations of sonnets through the machines are exceptionally essential and deliberate about mind-blowing events. The interpretation of English ballads into other local dialects can turn out to be very straightforward, however, vice-versa is troublesome. This is due to the assortment of structures, classes, and feelings of the local dialects. Various endeavours have been connected far and wide towards the programmed interpretation of ballads from local dialects into English. In this paper, we propose a half breed MT (HBMT) procedure driven by the standard based MT together with measurements based on statistical machine translation (SMT) and rule-based machine translation (RBMT) for WSD in natural script Hindi in English Lyrics. This proposed method improves the semantic and syntactic accuracy of a machine interpretation framework. Finally, the proposed approach result is compared with the machine translation methods such a Google and Microsoft Bing Babylonian and HMT translators provided achieves a better outcome compared to the existing standards.
High-Speed Tiny Tennis Ball Detection Based on Deep Convolutional Neural Networks

Recently, object detection has become a popular research direction in the field of robot vision. Sports ball detection is one of the most challenging tasks because of high speed and tiny size, which is an essential part of a sports ball robot. In this paper, we focus on the detection of tennis balls. The tennis ball is tiny and flies very fast, which causes movement deformation. While some popular detectors, like YOLO family, still cannot effectively solve the challenge of detecting high-speed tiny objects. We conduct a lot of research and find that the anchor-based detector is not good at high-speed tiny object detection. Therefore, we propose an approach to detect tennis balls based on an anchor-free detector. The network architecture is altered to get better results. Moreover, a data augmentation method named "magnifier" is proposed for detecting extremely small tennis balls. Our approach is evaluated on the images of the tennis competition at 2017 Summer Universiade and the images including tennis in MS COCO dataset. The precision, recall, and f1-measure of our approach reach 91.77%, 93.43%, and 92.59%, respectively. Our approach outperforms YOLOv3 and achieves exceptional tennis detection performance.
Parametric analysis of KLT algorithm in autonomous driving

The Kanade-Lucas-Tomasi(KLT) tracking algorithm is a widely used feature tracking algorithm in the field of computer vision(CV). The selection of proper warping parameters for the estimation of optical flow between adjacent image frames is crucial to obtain accurate and robust tracking results. We compare the various warping parameter settings in an autonomous driving environment based on the modified KLT algorithm with some well-known techniques. The skew and rotation parameters did not show better performance, but rather made convergence more difficult. The scale-parameter-added model has the best performance among the sets of warping parameters.
A Deep Reinforcement Learning-Based Power Resource Management for Fuel Cell Powered Data Centers

With the increase of data storage demands, the energy consumption of data centers is also increasing. Energy saving and use of power resources are two key problems to be solved. In this paper, we introduce the fuel cells as the energy supply and study power resource use in data center power grids. By considering the limited load following of fuel cells and power budget fragmentation phenomenon, we transform the main two objectives into the optimization of workload distribution problem and use a deep reinforcement learning-based method to solve it. The evaluations with real-world traces demonstrate the better performance of this work over state-of-art approaches.
Is the Classical GMW Paradigm Practical? The Case of Non-Interactive Actively Secure 2PC

One of the most challenging aspects in secure computation is offering protection against active adversaries, who may arbitrarily alter the behavior of corrupted parties. A powerful paradigm due to Goldreich, Micali, and Wigderson (GMW), is to follow a two-step approach: (1) design a passively secure protocol π for the task at hand; (2) apply a general compiler to convert π into an actively secure protocol π' for the same task.
Efficient Conditional Pre-training for Transfer Learning

Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This strategy helps reduce dependence on the target dataset and improves convergence rate and generalization on the target task. Although pre-training on large-scale datasets is very useful for new methods or models, its foremost disadvantage is high training cost. To address this, we propose efficient filtering methods to select relevant subsets from the pre-training dataset. Additionally, we discover that lowering image resolutions in the pre-training step offers a great trade-off between cost and performance. We validate our techniques by pre-training on ImageNet in both the unsupervised and supervised settings and finetuning on a diverse collection of target datasets and tasks. Our proposed methods drastically reduce pre-training cost and provide strong performance boosts. Finally, we improve the current standard of ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.
iCaps: An Interpretable Classifier via Disentangled Capsule Networks

We propose an interpretable Capsule Network, $$\textit{iCaps}$$ , for image classification. A capsule is a group of neurons nested inside each layer, and the one in the last layer is called a class capsule, which is a vector whose norm indicates a predicted probability for the class. Using the class capsule, existing Capsule Networks already provide some level of interpretability. However, there are two limitations which degrade its interpretability: 1) the class capsule also includes classification-irrelevant information, and 2) entities represented by the class capsule overlap. In this work, we address these two limitations using a novel class-supervised disentanglement algorithm and an additional regularizer, respectively. Through quantitative and qualitative evaluations on three datasets, we demonstrate that the resulting classifier, $$\textit{iCaps}$$ , provides a prediction along with clear rationales behind it with no performance degradation.
Analysis of physical protection system effectiveness of nuclear power plants based on performance approach

The physical protection system is a set of essential components for the protection of nuclear power plants against all types of threats. There are two methods for analyzing the protection systems effectiveness such as the performance method and the compliance method. In this study, the performance method is used to analyze and evaluate the physical protection system based on the Estimate of Adversary Sequence Interruption (EASI) model. The performance approach makes it possible to evaluate the functioning of the physical protection system elements to verify the overall of the protection system effectiveness. The performance method is easy to apply and gives effective results. The physical protection system effectiveness depends on the nature of the threat, either internal or external.
Exploiting Long-Term Dependency for Topic Sentiment Analysis

Most existing unsupervised approaches to detect topic sentiment in social texts consider only the text sequences in corpus and put aside social dynamics, as leads to algorithm's disability to discover true sentiment of social users.To address the issue, a probabilistic graphical model LDTSM (Long-term Dependence Topic-Sentiment Mixture) is proposed, which introduces dependency distance and uses the dynamics of social media to achieve the perfect combination of inheriting historical topic sentiment and fitting topic sentiment distribution underlying in current social texts.Extensive experiments on real-world SinaWeibo datasets show that LDTSM significantly outperforms JST, TUS-LDA and dNJST in terms of sentiment classification accuracy, with better inference convergence, and topic and sentiment evolution analysis results demonstrate that our approach is promising.
Design and Application of Smart Vocational Education Platform Based on New Generation Information Technology

Smart vocational education is the specific application of new information technologies such as the Intemet of Things, cloud computing, mobile Internet and artificial intelligence technology in the field of education. Smart vocational education is an important way for vocational colleges to use information-based teaching methods to innovate educational models, relying on a new generation of information The technology-based information education platform provides a support carrier for the implementation of smart vocational education. This article discusses the connotation of smart vocational education from the background of smart vocational education, studies the design scheme of smart vocational education platform and smart classroom based on a new generation of information technology, and analyzes various education under the smart vocational education platform Technology, and finally put forward a variety of main teaching modes relying on smart vocational education platforms.
Personalized Recommendation System with User Interaction based on LMF and Popularity Model

With the popularization of social media and similar web-based applications, the information generated by the users of these applications is growing exponentially. Thus, more attention is required on the “recommender system”. The recommender system fails to determine the real-time user interest. This paper uses an instant input mechanism to overcome this problem i.e. based on user interaction, a recommender system determines users' real-time interest. This paper uses “Significance analysis” to refine the recommender process performance. The most successful predictions are given to the client during communication to assess the real-time desires of the users and as soon as the model identifies the user it determines the expected recommendations for that user, thus, providing “Personalization of expectations.” There are two problems in the recommender scheme “cold-start” or “potential-false termination”. This problem is dealt using the user interaction which provides interactive recommendation although the system doesn't have any information related to the user. Potential-false dismissal occurs due to “tag sparsity”. To overcome this problem, associations between various tags are taken into account, which refine the recommendations. The paper uses “low rank matrix factorization (LMF)” approach to determine the potential interests and personalize the recommendations during each iteration. This method overcomes the rating sparsity and is enhanced by embedding the similar user and resource information. For new users, real time input is used to recommend similar items.
Behind the Scenes: Comparing X-Ray Visualization Techniques in Head-mounted Optical See-through Augmented Reality

Locating objects in the environment can be a difficult task, especially when the objects are occluded. With Augmented Reality, we can alternate our perceived reality by augmenting it with visual cues or removing visual elements of reality, helping users to locate occluded objects. However, to our knowledge, it has not yet been evaluated which visualization technique works best for estimating the distance and size of occluded objects in optical see-through head-mounted Augmented Reality. To address this, we compare four different visualization techniques derived from previous work in a laboratory user study. Our results show that techniques utilizing additional aid (textual or with a grid) help users to estimate the distance to occluded objects more accurately. In contrast, a realistic rendering of the scene, such as a cutout in the wall, resulted in higher distance estimation errors.
Faster, Better and More Detailed: 3D Face Reconstruction with Graph Convolutional Networks

This paper addresses the problem of 3D face reconstruction from a single image. While available solutions for addressing this problem do exist, to our knowledge, we propose the very first approach which is robust, lightweight and detailed i.e. it can reconstruct fine facial details. Our method is extremely simple and consists of 3 key components: (a) a lightweight non-parametric decoder based on Graph Convolutional Networks (GCNs) trained in a supervised manner to reconstruct coarse facial geometry from image-based ResNet features. (b) An extremely lightweight (35K parameters) subnetwork – also based on GCNs – which is trained in an unsupervised manner to refine the output of the first network. (c) A novel feature-sampling mechanism and adaptation layer which injects fine details from the ResNet features of the first network into the second one. Overall, our method is the first one (to our knowledge) to reconstruct detailed facial geometry relying solely on GCNs. We exhaustively compare our method with 7 state-of-the-art methods on 3 datasets reporting state-of-the-art results for all of our experiments, both qualitatively and quantitatively, with our approach being, at the same time, significantly faster.
Convergence of Momentum-Based Stochastic Gradient Descent

With the rapid increase of data amount in many fields, such as machine learning and networked systems, optimization-based methods inevitably confront the computational issues, which can be well dealt by the stochastic optimization strategies. As one of the most fundamental stochastic optimization algorithms, stochastic gradient descent (SGD) has been intensively developed and employed in the machine learning in the past decade. But unfortunately, due to the technical difficulties, other SGD based algorithms which could achieve better performance, such as momentum-based SGD (mSGD), still lack theoretical basis. Based on this fact, in this paper, we prove that the mSGD algorithm is almost surely convergent at each trajectory. The convergence rate of mSGD is also analyzed.
Designing Data Science Workshops for Data-Intensive Environmental Science Research

Over the last 20 years, statistics preparation has become vital for a broad range of scientific fields, and statistics coursework has been readily incorporated into undergraduate and graduate programs. However, a gap remains between the computational skills taught in statistics service courses and those required for the use of statistics in scientific research. Ten years after the publication of “Computing in the Statistics Curriculum,” the nature of statistics continues to change, and computing skills are more necessary than ever for modern scientific researchers. In this article, we describe research on the design and implementation of a suite of data science workshops for environmental science graduate students, providing students with the skills necessary to retrieve, view, wrangle, visualize, and analyze their data using reproducible tools. These workshops help to bridge the gap between the computing skills necessary for scientific research and the computing skills with which students leave their statistics service courses. Moreover, though targeted to environmental science graduate students, these workshops are open to the larger academic community. As such, they promote continued learning of the computational tools necessary for working with data, and provide resources for incorporating data science into the classroom.
Processing of Incomplete Images by (Graph) Convolutional Neural Networks

We investigate the problem of training neural networks from incomplete images without replacing missing values. For this purpose, we first represent an image as a graph, in which missing pixels are entirely ignored. The graph image representation is processed using a spatial graph convolutional network (SGCN) – a type of graph convolutional networks, which is a proper generalization of classical CNNs operating on images. On one hand, our approach avoids the problem of missing data imputation while, on the other hand, there is a natural correspondence between CNNs and SGCN. Experiments confirm that our approach performs better than analogical CNNs with the imputation of missing values on typical classification and reconstruction tasks.
Causal Agency and Responsibility: A Refinement of STIT Logic

We propose a refinement of STIT logic to make it suitable to model causal agency and responsibility in basic multi-agent scenarios in which agents can interfere with one another. We do this by supplementing STIT semantics, first, with action types and, second, with a relation of opposing between action types. We exploit these novel elements to represent a test for potential causation, based on an intuitive notion of expected result of an action, and two tests for actual causation from the legal literature, i.e., the but-for and the NESS tests. We then introduce three new STIT operators modeling corresponding notions of causal responsibility, which we call potential, strong, and plain responsibility, and use them to provide a fine-grained analysis of a number of case studies involving both individual agents and groups.
Domain Adaptation Gaze Estimation by Embedding with Prediction Consistency

Gaze is the essential manifestation of human attention. In recent years, a series of work has achieved high accuracy in gaze estimation. However, the inter-personal difference limits the reduction of the subject-independent gaze estimation error. This paper proposes an unsupervised method for domain adaptation gaze estimation to eliminate the impact of inter-personal diversity. In domain adaption, we design an embedding representation with prediction consistency to ensure that linear relationships between gaze directions in different domains remain consistent on gaze space and embedding space. Specifically, we employ source gaze to form a locally linear representation in the gaze space for each target domain prediction. Then the same linear combinations are applied in the embedding space to generate hypothesis embedding for the target domain sample, remaining prediction consistency. The deviation between the target and source domain is reduced by approximating the predicted and hypothesis embedding for the target domain sample. Guided by the proposed strategy, we design Domain Adaptation Gaze Estimation Network(DAGEN), which learns embedding with prediction consistency and achieves state-of-the-art results on both the MPIIGaze and the EYEDIAP datasets.
A Survey on Chatbots Using Artificial Intelligence

The major role of today's technology is played by the artificial intelligence along with the NLP processing integrated with the machine learning algorithms. The computer program which uses artificial intelligence to imitate the behavior of the human decision making as well as providing the various kind of services forms the basis for the survey on artificial intelligence on the chatbots. Thus, the paper provides a survey based on the different platforms used to build a chatbot for providing various kind of services to different kind of users. The design techniques for building the chatbot depends on the services meant to provide for the users. The chatbot will get the experience by learning through the past experience using various algorithms. The data can be trained to the chatbot which will enable it to check with the knowledge base for providing accurate results to the query of the user through client side applications.