Reviewerly/exHarmony huggingface dataset:
Depth and Breadth of Pie Menus for Mid-air Gesture Interaction

Mid-air gesture interaction has become one of the most promising human–computer interaction manners. However, the principles of designing pie menus for mid-air gesture interaction are largely lacking. Currently, no study has addressed the breadth and depth of the pie menu for mid-air gesture interaction. Here, in two experiments, we found that the breadth and depth of a pie menu had distinct influences on the operation performance in mid-air gestural interaction: breadth affects both the accuracy and reaction time of the operation, while depth only affects reaction time. Overall, when operation accuracy is the main concern, the breadth of the pie menu will be the key factor, and it should not exceed eight items (Experiments 1 and 2). However, when the operation also emphasizes efficiency, the depth of the pie menu should be considered: fewer layers lead to faster responses, with two layers yielding the best performance (Experiment 2).
A method of semi-automated ontology population from multiple semi-structured data sources

Organisations use data in different formats: Word documents, Excel spreadsheets, databases, HTML pages and so on. It is not easy to make decisions with such data due to the lack of integration between the different sources and built-in decision-making rules. Decisions can be reached with knowledge bases, which, unlike databases, make it possible to store not only objects, facts and attributes but also more sophisticated patterns such as rules and axioms. The article proposes an ontology-based method for knowledge base creation that allows for the simultaneous integration of semi-structured data sources and extendibility while remaining context independent. At the initial steps of the method, data specification should be performed with the Data Sources Ontology developed by the authors. This ontology provides data structure description that forms supportive knowledge graph. The graph’s schema should be mapped with the domain ontology to be populated. Finally, the data are inserted into the domain ontology according to the mapping rules. Manual input is needed during data specification and data-to-ontology schema mapping.
Secure and Efficient Delegation of Elliptic-Curve Pairing

Many public-key cryptosystems and, more generally, cryptographic protocols, use pairings as important primitive operations. To expand the applicability of these solutions to computationally weaker devices, it has been advocated that a computationally weaker client delegates such primitive operations to a computationally stronger server. Important requirements for such delegation protocols include privacy of the client's pairing inputs and security of the client's output, in the sense of detecting, except for very small probability, any malicious server's attempt to convince the client of an incorrect pairing result. In this paper we show that the computation of bilinear pairings in essentially all known pairing-based cryptographic protocols can be efficiently, privately and securely delegated to a single, possibly malicious, server. Our techniques provides efficiency improvements over past work in all input scenarios, regardless on whether inputs are available to the parties in an offline phase or only in the online phase, and on whether they are public or have privacy requirements. The client's online runtime improvement is, for some of our protocols, almost 1 order of magnitude, no matter which practical elliptic curve, among recently recommended ones, is used for the pairing realization.
A heterogeneous microkernel OS for Rack-Scale systems

Datacenters are adopting heterogeneous hardware in the form of different CPU ISAs and accelerators. Advances in low-latency and high-bandwidth interconnects enable hardware vendors to tighten the coupling of multiple CPU servers and accelerators. The closer connection of components facilitates bigger machines, which pose a new challenge to operating systems. We advocate to build a heterogeneous OS for large heterogeneous systems by combining multiple OS design principles to leverage the benefits of each design. Because a security-oriented design, enabled by simplicity and clear encapsulation, is vital in datacenters, we choose to survey various design principles found in microkernel-based systems. We explain that heterogeneous hardware employs different mechanisms to enforce access rights, for example for memory accesses or communication channels. We outline a way to combine enforcement mechanisms of CPUs and accelerators in one system. A consequence of this is a heterogeneous access rights management which is implemented as a heterogeneous capability system in a microkernel-based OS.
Grammatically Recognizing Images with Tree Convolution

Similar to language, understanding an image can be considered as a hierarchical decomposition process from scenes to objects, parts, pixels, and the corresponding spatial/contextual relations. However, the existing convolutional networks concentrate on stacking redundant convolutional layers with a large number of kernels in a hierarchical organization to implicitly approximate this decomposition. This may limit the network to learn the semantic information conveyed in the internal feature maps that may reveal minor yet crucial differences for visual understanding. Attempting to tackle this problem, this paper proposes a simple yet effective tree convolution (TreeConv) operation for deep neural networks. Specifically, inspired by the image grammar techniques[73] that serve as a unified framework of object representation, learning, and recognition, our TreeConv designs a generative image grammar, i.e., tree generation rule, to parse the hierarchy of internal feature maps by generating tree structures and implicitly learning the specific visual grammars for each object category. Extensive experiments on a variety of benchmarks, i.e., classification (ImageNet / CIFAR), detection & segmentation (COCO 2017), and person re-identification (CUHK03), demonstrate the superiority of our TreeConv in both boosting the accuracy and reducing the computational cost. The source code will be available at: https://github.com/wanggrun/TreeConv.
RE_BBC: Requirements Engineering in a Blockchain-Based Cloud System: Its Role in Service-Level Agreement Specification

The cloud has several security and trust issues with the third-party provider. The blockchain-based cloud (BBC) system is an emerging technology that promises safe storage of big data on a cloud. The development of such a system needs requirements engineering (RE) in determining the requirements based on service-level agreements (SLAs). RE can provide a functional process to model and specify a SLA-based smart contract. The RE process for BBC ensures that the resulting SLA specifications are consistent, accurate, complete, verified, and validated. The RE process responds to the many challenges of the BBC and determines a novel direction to provide efficient and quality development of blockchainbased applications.
Reconstruction of Strings from their Substrings Spectrum

This paper studies reconstruction of strings based upon their substrings spectrum. Under this paradigm, it is assumed that all substrings of some fixed length are received and the goal is to reconstruct the sequence. While many existing works assumed that substrings are received error free, we follow in this paper the noisy setup of this problem that was first studied by Gabrys and Milenkovic. The goal of this study is twofold. First we study the setup in which not all substrings in the multispectrum are received, and then we focus on the case where the read substrings are not error free. In each case we provide specific code constructions of strings that their reconstruction is guaranteed even in the presence of failure in either model. We present efficient encoding and decoding maps and analyze the cardinality of the code constructions.
Content-Based Hybrid Deep Neural Network Citation Recommendation Method

The rapid growth of scientific papers makes it difficult to query related papers efficiently, accurately and with high coverage. Traditional citation recommendation algorithms rely heavily on the metadata of query documents, which leads to the low quality of recommendation results. In this paper, DeepCite, a content-based hybrid neural network citation recommendation method is proposed. First, the BERT model was used to extract the high-level semantic representation vectors in the text, then the multi-scale CNN model and BiLSTM model were used to obtain the local information and the sequence information of the context in the sentence, and the text vectors were matched in depth to generate candidate sets. Further, the depth neural network was used to rerank the candidate sets by combining the score of candidate sets and multi-source features. In the reranking stage, a variety of Metapath features were extracted from the citation network, and added to the deep neural network to learn, and the ranking of recommendation results were optimized. Compared with PWFC, ClusCite, BM25, RW, NNRank models, the results of the Deepcite algorithm presented in the ANN datasets show that the precision (P@20), recall rate (R@20), MRR and MAP indexesrise by 2.3%, 3.9%, 2.4% and 2.1% respectively. Experimental results on DBLP datasets show that the improvement is 2.4%, 4.3%, 1.8% and 1.2% respectively. Therefore, the algorithm proposed in this paper effectively improves the quality of citation recommendation.
Use of data mining in a two‐step process of profiling student preferences in relation to the enhancement of English as a foreign language teaching

Abstract The paper pursues a twofold goal. The first goal refers to the identification of university students' needs regarding such modifications to English language courses that would improve English as a foreign language (EFL) teaching outcomes. The other goal refers to the methodical issue of achieving the first one. In this aspect, the use of selected data mining techniques in a hierarchical way in real data processing is proposed. These are: (a) Self‐Organizing Map (SOM) dataset segmentation and then (b) market basket analysis applied to the individual SOM segments. The research data were collected from the students' survey concerning their opinion of the EFL teaching process; 347 students of a faculty of a technical university in Poland completed the questionnaire. The use of SOM allowed the identification of homogeneous groups of students, while market basket analysis allowed indicating, within each group, the relationships between student opinions of effective methods of teaching English. In such a way, satisfactory student preference profiles as regards their approach to the improvement of English language competences were developed. On this basis, EFL teaching methods appropriate for the specific profile can be adapted.
An Efficient and Secure Students Online Voting Application

The aim of this proposed project is to make the democratic process simple for the students at the college level. Presently in our college, vote casting is performed by utilizing paper and counting is done manually so it expends students as well as educators valuable time, also there can be a possibility of error while tallying the cast votes. All this makes the vote casting process very dreary so in our project, the vote capturing and tallying is done on the web. It saves processing time, avoids human errors and there won’t be any invalid votes It has a basic user interface of application which attracts users. As this application is planned for students so verification happens on the basis of unique ID code which is the students’ registered ID, with this goal the students can cast their votes remotely from anyplace. This is a combo box application so it additionally comprises university question papers, syllabus, and college fundamental data or different activities of the college.
A Survey of Depth Estimation Based on Computer Vision

Currently, the method based on computer vision for depth information extraction and depth estimation is widely used. It can get depth information from 2D images, depth maps, or binocular vision images and has been a popular application in the field of artificial intelligence such as depth detection, pose estimation, as well as 3D reconstruction. This paper introduces the basic theory and some implementation methods of depth information acquisition based on computer vision. As well, it briefly summarizes the existing research results and makes an outlook on the future development trend of the field.
On Effective Stochastic Mechanisms for On-The-Fly Codebook Regeneration

This paper proposes an effective mechanism for stochastic codebook generation for lossy coding, using source examples. Earlier work has shown that the rate-distortion bound can be asymptotically achieved by a “natural type selection” (NTS) mechanism which iteratively considers asymptotically long source strings (from given distribution P) and regenerates the codebook according to the type of the first codeword to “d-match” the source string (i.e., satisfy the distortion constraint), where the sequence of codebook generating types converges to the optimal reproduction distribution. While ensuring optimality, earlier results had a significant practical flaw, due to the order of limits at which the convergence is achieved. More specifically, NTS iterations indexed by n presume asymptotically large ℓ, but the codebook size grows exponentially with ℓ. The reversed order of limits is practically preferred, wherein most codebook regeneration iterations involve manageable string lengths. This work describes a dramatically more efficient mechanism to achieve the optimum within a practical framework. It is specifically shown that it is sufficient to individually encode many source strings of short fixed length ℓ, then find the maximum likelihood estimate for the distribution Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+1</sub> that would have generated the observed sequence of d-matching codeword strings, then use Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+1</sub> to generate a new codebook for the next iteration. The sequence of distributions Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , ... converges to the optimal reproduction distribution Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</sub> *(P, d), achievable at finite length ℓ. It is further shown that Q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</sub> *(P, d) converges to the optimal reproduction distribution Q*(P, d) that achieves the rate-distortion bound R(P, d), asymptotically in string length ℓ.
Tracking Neutrophil Migration in Zebrafish Model Using Multi-Channel Feature Learning

Tracking cells over time is crucial in the fields of computer vision and biomedical science. Studying neutrophils and their migratory profile is the highly topical fields in inflammation research due to determining role of these cells during immune responses. As neutrophils generally are of various shapes and motion, it remains challenging to track and describe their behaviours from multi-dimensional microscopy datasets. In this study, we propose a robust novel multi-channel feature learning (MCFL) model inspired by deep learning to extract the complex behaviour of neutrophils moved in time lapse images. In this model, the convolutional neural networks along with cell relocation distance and orientation channels learn the robust significant spatial and temporal features of an individual neutrophil. Additionally, we also proposed a new cell tracking framework to detect and track neutrophils in the original time-laps microscopy images, entails sampling, observation, and visualisation functions. Our proposed cell tracking-based-multi channel feature learning method has remarkable performance in rectifying common cell tracking problem compared with state-of the-art methods.
Analytical review of approaches for tonality recognition of Russian text data

Introduction: In recent years, sentiment analysis has found practical application in many areas, such as evaluating the quality of products and services based on customers’ online reviews, analyzing negative emotions in messages, forecasting stock markets or political situations based on news data. In this regard, a large number of systems and methods for Russian text sentiment analysis are being developed. Purpose: A detailed review of approaches, and comparative analysis of available databases in the field of Russian text sentimental analysis. Results: Our analytical review of the approaches to Russian text data sentiment analysis has shown that there are a large number of ways for preprocessing, vectorization and machine classification of the text data. Studying the available databases shows that the Russian text sentimental analysis is less developed than that for other major world languages. Studying the existing software systems for Russian text analysis reveals their low accuracy compared to English, which can be caused by the sophisticated structure of Russian. Discussion: In our further research, we plan to implement sentiment analysis of spoken speech using audio data. To do this, we will need to obtain a spelling transcription of speech for each speaker.
Optimal Hierarchical Learning Path Design With Reinforcement Learning

E-learning systems are capable of providing more adaptive and efficient learning experiences for learners than traditional classroom settings. A key component of such systems is the learning policy. The learning policy is an algorithm that designs the learning paths or rather it selects learning materials for learners based on information such as the learners' current progresses and skills, learning material contents. In this article, the authors address the problem of finding the optimal learning policy. To this end, a model for learners' hierarchical skills in the E-learning system is first developed. Based on the hierarchical skill model and the classical cognitive diagnosis model, a framework to model various mastery levels related to hierarchical skills is further developed. The optimal learning path in consideration of the hierarchical structure of skills is found by applying a model-free reinforcement learning method, which does not require any assumption about learners' learning transition processes. The effectiveness of the proposed framework is demonstrated via simulation studies.
FUPA: Future Utilization Prediction Algorithm based Load Balancing Scheme for Optimal VM Migration in Cloud Computing

As a competitive model, Cloud Computing (CloudC) is fast growing model that allows users to access on-demand systems and web services. In CloudC, Load-Balancing (LB) is one of the most vital problems to allocate the workload uniformly among each node. Typically, LB defines the task of allocation and re-allocation of the workload among all the available resources to reduce the resource usages. For this task, different algorithms have been proposed. In recent year, an Osmotic Hybrid artificial Bee and Ant Colony (OHBAC) optimization algorithm has been proposed for achieving LB in the dynamic CloudC. In this algorithm, automated Virtual Machines (VMs) that are migrated via cloud systems were enabled by the osmotic behavior. But, this algorithm was only focused on minimizing the number of active Physical Machines (PMs) based on their current resource requirements and neglecting the potential resource requirements. This creates the unessential VM migrations and increases the Service Level Agreement (SLA) violations in data centers. Therefore in this article, OH-BAC algorithm with the Future Utilization Prediction (FUP) is proposed to reduce the amount of VM migrations and enhance the LB. In this newly proposed OH-BACFUP algorithm, both the current and future utilization of resources are considered to migrate the VMs into the least amount of active PMs. The future resource utilization is estimated by two different prediction models such as linear regression and optimal piecewise linear regression algorithms. These regression models can estimate the potential resource usages of VMs and PMs. Then, the predicted value can be used in the fitness function of OH-BAC to choose the best VM for migration to the most suitable PM.
Toward Predicting Active Participants in Tweet Streams: A case study on Two Civil Rights Events

Online social media arouse much research interest in recent years. In contrast to previous work that focused on the detection of emerging topics, this paper undertakes the prediction of active users in online social events, which is so far rarely explored. This prediction task is formulated as a binary classification problem that built on real-world tweet streams, respectively referring to Ferguson event and New York Chockhold event in this paper. Then, a comprehensive user feature system is designed to characterize the users participated in the events, which includes not only basic statistical characteristics or image-pixel-level features, but also some emotional features and personality features. Next, the Weighted Random Forest (Weighted-RF) classifier is adopted to solve the classification problem. Based on the user feature system and the classifier, the experience of a previous event can be archived and applied to the prediction of later similar events. Experimental results show that the Weighted-RF trained by samples of Ferguson event can effectively predict active users in NYC event, with an AUC value around 0.8392. Besides, the image-content based personality model contributes to the quantitative analysis of population portrait, which brings new perspective to understand the participants of events.
Faster, Simpler, More Accurate

Automated machine learning (AutoML) offers the promise of translating raw data into accurate predictions with just a few lines of code. Rather than relying on human time/effort and manual experimentation, models can be improved by simply letting the AutoML system run for more time. In this hands-on tutorial, we demonstrate fundamental techniques that enable powerful AutoML. We consider standard supervised learning tasks on various types of data including tables, text, images, as well as multi-modal data comprised of multiple types. Rather than technical descriptions of how individual ML models work, we emphasize how to best use models within an overall ML pipeline that takes in raw training data and outputs pre-dictions for test data. A major focus of our tutorial is on automating deep learning, a class of powerful techniques that are cumbersome to manage manually. Despite this, hardly any educational material describes their successful automation. Each topic covered in the tutorial is accompanied by a hands-on Jupyter notebook that implements best practices (which will be available on Github before and after the tutorial). Most of this code is adopted from AutoGluon (autogluon.mxnet.io), a recent AutoML toolkit for automated deep learning that is both state-of-the-art and easy-to-use.