Reviewerly/exHarmony huggingface dataset:
Real Time Tracking of Traffic Signs for Autonomous Driving Using Monocular Camera Images

For autonomous vehicles to navigate safely through traffic, it is necessary to understand the status of the surrounding environment such as traffic lights and traffic signs present. The traffic sign detection algorithms designed miss to recognize the signs under occlusion scenarios. Therefore, including tracking along with the detection algorithm helps in increasing the detection rate and safe manoeuvring. The paper presents tracking by detection method for tracking multiple traffic signs in image coordinates with no ego motion information present. The existing tracking methods such as the Kalman filter fails to track multiple traffic signs in image coordinates due to camera perspective projection. Assuming the movement of the camera mounted on the vehicle is linear, the prediction of traffic sign location is made robustly by modeling the motion and using Mahalanobis distance measure and Hungarian Association algorithm the predictions and detections are associated. The proposed tracking method achieved a decent accuracy of 92.3%, and a precision of 96.4% when compared with other State-of-the-art methods.
Improved Sentence Similarity Measurement in the Medical Field Based on Syntactico-Semantic Knowledge

Computing semantic sentence similarity plays a vital role in a range of text mining applications. In the clinical domain, Semantic Textual Similarity can enable us to detect and eliminate redundant information that may lead to a reduction in cognitive burden and an improvement in the clinical decision-making process. Several methods have been proposed to measure the sentence Similarity based on semantic knowledge and learning models. Despite realized efforts, the results of these methods are unsatisfactory, as much relevant semantic knowledge, such as semantic class, thematic role and syntactico-semantic knowledge like the semantic predicates, are not taken into account. In this paper, we propose a novel method to measure semantic similarity between clinical sentences based on deep learning and using syntactico-semantic knowledge such as semantic argument and thematic role. An experiment was carried out on MedSTS dataset yielded better results, showing a high correlation (r = 0 89) with human ratings.
Leveled Multi-Hop Multi-Identity Fully Homomorphic Encryption

Gentry, Sahai, and Waters (CRYPTO 2013) proposed the notion of multi-identity fully homomorphic encryption (MIFHE), which allows homomorphic evaluation of data encrypted under multiple identities. Subsequently, Clear and McGoldrick (CANS 2014, CRYPTO 2015) proposed leveled MIFHE candidates. However, the proposed MIFHE is either based on i <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mi mathvariant="script">O</mi></math> , which is a nonstandard assumption or single hop; that is, an arbitrary “evaluated” ciphertext under a set of identities is difficult to further evaluate when new ciphertexts are encrypted under additional identities. To overcome these drawbacks, we propose a leveled multi-hop MIFHE scheme. In a multi-hop MIFHE scheme, one can evaluate a group of ciphertexts under a set of identities to obtain an “evaluated” ciphertext, which can be further evaluated with other ciphertexts encrypted under additional identities. We also show that the proposed MIFHE scheme is secure against selective identity and chosen-plaintext attacks (IND-sID-CPA) under the learning with errors (LWE) assumption.
Computational investment in generation of elliptic curves randomly over large prime fields

Abstract Randomly computed elliptic curves over prime fields are desirable for implementation in strategic or military grade cryptosystems. A randomly computed elliptic curve eliminates the possibility of exhibiting any pre‐studied value or special structure of the curve parameters which may be doubted to have intentionally non‐disclosed vulnerability. However, computing elliptic curves randomly over large prime fields for cryptography is an intricate task which requires reasonably high computational resources and time. These computational resources are considered in terms of the number of CPU clock cycles and the number of attempts or searches made in the security parameter space of elliptic curve. The estimate of the number of CPU clock cycles helps in determining processor requirements whereas the number of attempts or searches help to decide the number of CPU cores for speeding up the curve generation process. We present efficient statistical estimates of computational resources to randomly compute cryptographically safe elliptic curve over desired prime field size using a standard procedure.
Beyond the Failure of Direct-Matching in Keyword Evaluation: A Sketch of a Graph Based Solution

The starting point of this paper is the observation that methods based on the direct match of keywords are inadequate because they do not consider the cognitive ability of concept formation and abstraction. We argue that keyword evaluation needs to be based on a semantic model of language capturing the semantic relatedness of words to satisfy the claim of the human-like ability of concept formation and abstraction and achieve better evaluation results. Evaluation of keywords is difficult since semantic informedness is required for this purpose. This model must be capable of identifying semantic relationships such as synonymy, hypernymy, hyponymy, and location-based abstraction. For example, when gathering texts from online sources, one usually finds a few keywords with each text. Still, these keyword sets are neither complete for the text nor are they in themselves closed, i.e., in most cases, the keywords are a random subset of all possible keywords and not that informative w.r.t. the complete keyword set. Therefore all algorithms based on this cannot achieve good evaluation results and provide good/better keywords or even a complete keyword set for a text. As a solution, we propose a word graph that captures all these semantic relationships for a given language. The problem with the hyponym/hyperonym relationship is that, unlike synonyms, it is not bidirectional. Thus the space of keyword sets requires a metric that is non-symmetric, in other words, a quasi-metric. We sketch such a metric that works on our graph. Since it is nearly impossible to obtain such a complete word graph for a language, we propose for the keyword task a simpler graph based on the base text upon which the keyword sets should be evaluated. This reduction is usually sufficient for evaluating keyword sets.
Introduction by Judea Pearl

chapter Share on Introduction by Judea PearlProbabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 57–60https://doi.org/10.1145/3501714.3501722Online:04 March 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Preconditioned Diffusion Multitask Clustering Graph Filters

In this work, we are interested in the design of node-variant FIR graph filters, in which the graph filter estimates the filter coefficients from the stream data. Considering the estimation of filter coefficients as a task, we introduce concept of the multitask into graph filters. The filter coefficients can be divided into different clusters, and the cooperation between clusters is beneficial. Then, a multitask graph diffusion LMS algorithm is proposed. In order to improve convergence speed and performance, a multitask graph diffusion preconditioned algorithm is proposed. The simulation results verify the feasibility of algorithms.
General dependence structures for some models based on exponential families with quadratic variance functions

We describe a procedure to introduce general dependence structures on a set of random variables. These include order-q moving average-type structures, as well as seasonal, periodic, spatial and spatio-temporal dependences. The invariant marginal distribution can be in any family that is conjugate to an exponential family with quadratic variance function. Dependence is induced via a set of suitable latent variables whose conditional distribution mirrors the sampling distribution in a Bayesian conjugate analysis of such exponential families. We obtain strict stationarity as a special case.
Response Generation via Structure-Aware Constraints

End-to-end neural modeling with the encoder-decoder architecture has shown great promise in response generation. However, it often generates dull and generic responses due to its failure to effectively perceive various kinds of act, sentiment, and topic information. To address these challenges, we propose a response-generation model with structure-aware constraints to capture the structure of dialog and generate a better response with various constraints of the act, sentiment, and topic. In particular, given an utterance sequence, we first learn the representation of each utterance in the encoding stage. We then learn the turn, speaker, and dialog representation from the utterance representations and construct the structure of dialog. Third, we employ an attention mechanism to extract the constraints of act, sentiment, and topic based on the structure of the dialog. Finally, we utilize these structure-aware constraints to control the response-generation process in decoding stage. Extensive experimental results validate the superiority of our proposed model against the state-of-the-art baselines. In addition, the results also show that the proposed model can generate responses with more appropriate content based on the structure-aware constraints.
A Novel Method for Technical Candidate Assessment using Github Repository Inspection Automation

The IT industry is scaling new peaks of recruitment with every passing month of the year 2021. The recruiters and talent scouts for technical schools are having a hard time finding suitable technical candidates for a preliminary assessment by the resume alone. A solution to this problem can be envisaged in a summary inspection of the public Github repositories of the potential candidates. Given a flood of incoming candidate profiles, the manual process of repository inspection is time consuming and cumbersome, besides being inaccurate and therefore it is needed to automate the Github technical repository inspection process. This paper examines the use of GitHub REST API to fetch the details and repositories of a GitHub user by searching the username and making this a user interactive process through ReactJs. The paper has been written as a part of a Graduate project study, and the objective of repository collection automation has been successfully demonstrated with an appropriate user interface with discussion of the application use cases.
Hotel Review Classification Based on the Text Pretraining Heterogeneous Graph Neural Network Model

With the amount of online information continuously growing, it becomes more and more important for online stores to recommend corresponding products precisely based on users' preferences. Reviews for various products can be of great help for the recommendation task. However, most recommendation platforms only classify positive and negative reviews based on sentiment analysis, without considering the actual demands of users, and it will reduce the effectiveness on classification task. To count this issue, we propose a new model, which integrates heterogeneous neural network and text pretraining model into this task, and compare this model with others on a travel type classification task. The model combines a pretrained text model named Bidirectional Encoder Representation from Transformers (BERT) and heterogeneous graph attention network (HGAN). Firstly, we do a fine-tuning task on BERT by a dataset consisting of 1.4 million hotel reviews from the Ctrip website to obtain fine representations of trip-related words. Then, we proposed the similarity fussy-matching method to get the main topic of each review. Then, we construct a heterogeneous neural network and apply the attention mechanism to it to mine the preference of users for traveling. Finally, the classification task is done based on each user's preference. In Section 5 of this study, we do an experiment, which compares our model with five others. The results show that the accuracy of ours is 70%, which is higher than the other five models.
ODEN: Live Programming for Neural Network Architecture Editing

In deep learning application development, programmers tend to try different architectures and hyper-parameters until satisfied with the model performance. Nevertheless, program crashes due to tensor shape mismatch prohibit programmers, especially novice programmers, from smoothly going back and forth between neural network (NN) architecture editing and experimentation. We propose to leverage live programming techniques in NN architecture editing with an always-on visualization. When the user edits the program, the visualization can synchronously display tensor states and provide a warning message by continuously executing the program to prevent program crashes during experimentation. We implement the live visualization and integrate it into an IDE called ODEN that seamlessly supports the “edit→experiment→edit→···” repetition. With ODEN, the user can construct the neural network with the live visualization and transits into experimentation to instantly train and test the NN architecture. An exploratory user study is conducted to evaluate the usability, the limitations, and the potential of live visualization in ODEN.
Digital art holograms: approaches for the development of color choreographies

The production of digital holographic images comes from several artistic areas such as photography, film and video, after being mixed, electronically manipulated and digitally synthesized, offering visual effects and additional important resources for the artistic creation of images, which are different from analogue holography<sup>1</sup>. In digital holograms, which more easily reproduce colours and movement<sup>2</sup>, <sup>3</sup>, one of the main aesthetic features is the time orchestrated by the viewer, that is, the movement that the viewer develops in front of the holographic plate when looking for the holographic image, since the viewing area is larger than the holographic plate and attracted in some way by the perceptual and expressive possibilities that the interactive concept of the chromaticism of the holographic image gives rise to. The artistic use of colour in holography is a plastic and symbolic important resource. My intention is to discuss my artistic work and concepts, and to present some resources on the creation of image information, in the context of digital holograms. The topics developed are focused on visual aspects such as light and colour, third and fourth dimensions, the space-time relationship, and representation issues; in order to explore concepts such as memory and identity, materiality and immateriality; gender issues; thought and action.
Optical direction motion and optical optimistic density with magnetic energy

In this paper, we present the notion of optical direction motion using optical quasi optimistic density. Thus, we describe optical direction potential magnetic energy of Lorentz forces. Also, we characterize new approach for optical electroosmotic magnetic phase in cold plasma. Finally, we obtain energy variation of the optical direction potential magnetic energy in Heisenberg group.
Learning User Interface Semantics from Heterogeneous Networks with Multimodal and Positional Attributes

User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g. applications, screens, view class, and other types of design objects) with multimodal (e.g. textual, visual) and positional (e.g. spatial location, sequence order and hierarchy level) attributes. We can therefore represent a set of application UIs as a heterogeneous network with multimodal and positional attributes. Such a network not only represents how users understand the visual layout of UIs, but also influences how users would interact with applications through these UIs. To model the UI semantics well for different UI annotation, search, and evaluation tasks, this paper proposes the novel Heterogeneous Attention-based Multimodal Positional (HAMP) graph neural network model. HAMP combines graph neural networks with the scaled dot-product attention used in transformers to learn the embeddings of heterogeneous nodes and associated multimodal and positional attributes in a unified manner. HAMP is evaluated with classification and regression tasks conducted on three distinct real-world datasets. Our experiments demonstrate that HAMP significantly out-performs other state-of-the-art models on such tasks. We also report our ablation study results on HAMP.
Security based Electronic Voting Machine using Xilinx tool

Electronic voting machines are primary need in a democratic society to avoid rigging in the election process. Earlier in the age-old method, ballot papers are used for voting where there is a lot of scope for hindrance of the voting process. In this paper, we have considered a digital electronic voting system which is designed using VHDL coding in Xilinx tool. Venable switch is provided to start the voting process. 3 contestants are considered in the election process namely party0, party1 and party2. Vswitch is a 2-bit signal used to choose the contestant by the voter to vote for him/her. If venable is not activated and voter tries to vote, the invalid signal is given which indicates vote is not taken unless venable is activated. This digital electronic voting system provides privacy for voters as well as safe, secure and integrity in voting process.
Cloud resource management and scheduling

This extended chapter covers multiple research topics marked as (R). Cloud resource management requires complex policies and decisions for multiobjective optimization. Effective resource management is extremely challenging due to the scale of the cloud infrastructure and to the unpredictable interactions of the system with a large population of users. The scale makes it impossible to have accurate global state information, and the large user population makes it nearly impossible to predict the type and the intensity of the system workload. An overview of policies and mechanisms for cloud resource management in Section 9.1 is followed by a discussion of scheduling algorithms for computer clouds in Section 9.2 and by an analysis of delay scheduling and of data-aware scheduling in Sections 9.3 and 9.4, respectively. The Apache capacity scheduler is presented in Section 9.5, and start-time fair queuing and borrowed virtual time-scheduling algorithms are analyzed in Sections 9.6 and 9.7, respectively. Cloud scheduling subject to deadlines and MapReduce scheduling with deadlines are discussed in Sections 9.8 and 9.9, respectively. Resource bundling and combinatorial auctions are covered in Section 9.10. Cloud energy efficiency and cloud resource utilization and the impact of application scaling on resource management are analyzed in Sections 9.11 and 9.12, respectively. A control theoretic approach to resource allocations is discussed in Sections 9.13, 9.14, and 9.15, followed by a machine-learning algorithm for coordination of specialized autonomic performance managers in Section 9.16. A utility model for resource allocation for a web service is then presented in Section 9.17. The cloud infrastructure will most likely continue to scale up for accommodating the demands of an increasingly larger cloud user community. A fair question is whether current cloud management systems can sustain this expansion. Self-organization and self-management alternatives come to mind, but the very slow progress made by the autonomic computing initiative is likely to dampen the enthusiasm of those believing in self-management. We discuss emergence and self-organization in Section 9.18 and conclude this chapter with an in-depth analysis of cloud interoperability in Section 9.19. Sections 9.20 and 9.21 contain further readings and exercises and problems.
Role of Data Mining in Visual Communication in the Age of Computer Technology

Data mining usually refers to the process of finding useful patterns or knowledge from data sources (such as database, text, picture, world wide web, etc.) [1]. At present, data mining technology has been widely used in astronomy, military, management science and many other fields [2]. Data mining can be applied to different stages in visual communication design, especially in the stage of delegated investigation, positioning planning and execution inspection.
A survey on traditional Chinese characters learning needs of learners of Chinese as a second language

This paper aims to study through a questionnaire survey the needs of learners of Chinese as a second language for learning traditional Chinese characters. To achieve the aim, analysis was conducted from various perspectives such as the learners’ background, Chinese proficiency, Chinese instruction, exposure to traditional Chinese characters and attitudes towards traditional Chinese characters. Factors that influenced the participants’ learning needs and motivations were analyzed with the Statistical Package for the Social Sciences. The results showed that the participants had a moderate level of traditional Chinese characters learning needs, and that reading and appreciating arts were two most significant motivations for learning traditional Chinese characters.
Homomorphic Encryption Based Privacy Preservation Scheme for DBSCAN Clustering

In this paper, we propose a homomorphic encryption-based privacy protection scheme for DBSCAN clustering to reduce the risk of privacy leakage during data outsourcing computation. For the purpose of encrypting data in practical applications, we propose a variety of data preprocessing methods for different data accuracies. We also propose data preprocessing strategies based on different data precision and different computational overheads. In addition, we also design a protocol to implement the cipher text comparison function between users and cloud servers. Analysis of experimental results indicates that our proposed scheme has high clustering accuracy and can guarantee the privacy and security of the data.
How Knowledge-Driven Class Generalization Affects Classical Machine Learning Algorithms for Mono-label Supervised Classification

Beyond class imbalance and the sample size per class, this research paper studies the effect of knowledge-driven class generalization (KCG) on the accuracy of classical machine learning algorithms for mono-label classification. We apply our analysis on five classical machine learning models (Perception, Support Vectors, Random Forest, K-nearest neighbors, and Decision Tree) to classify a set of animal image files generated from Wikimedia Commons, a large-scale repository of free images. Thanks to our analysis, we found that the accuracy rates of mono-label classification models, mainly Support Vectors and K-nearest neighbors, are affected by KCG. The increasing or decreasing behavior of accuracy rates is driven by the settings of the classification categories and the generalization. The analysis of KCG should be useful to understand the limitations of classical machine-learning algorithms and to fuel debates about the improvement of classical models and supervised classification evaluation methods within the framework of Explainable Artificial Intelligence.