Reviewerly/exHarmony huggingface dataset:
Enhanced Geometric Techniques for Point Marking in Model-Free Augmented Reality

Specifying points in three-dimensional (3D) space is an essential function in many augmented reality (AR) applications. When an environment model is not available, a straightforward solution is to perform geometric triangulation using two rays. However, naïve implementations suffer from low accuracy caused by technical limitations of AR devices and human motor constraints. To overcome these issues, we designed and evaluated two enhanced geometric techniques for 3D point marking. VectorCloud uses multiple rays to reduce the effects of pointing jitter, and ImageRefinement improves the accuracy by allowing users to refine the 3D direction of the two rays. We conducted studies to understand the characteristics of these techniques in both ecologically valid outdoor settings using a mobile AR display and in more controlled setting using virtual reality simulation. Our experiments demonstrate that both techniques improve the precision of 3D point marking, and that ImageRefinement is superior to VectorCloud overall. These results are particularly relevant in the design of mobile AR systems intended for use in large outdoor areas.
Adaptive deep residual network for single image super-resolution

Abstract In recent years, deep learning has achieved great success in the field of image processing. In the single image super-resolution (SISR) task, the convolutional neural network (CNN) extracts the features of the image through deeper layers, and has achieved impressive results. In this paper, we propose a single image super-resolution model based on Adaptive Deep Residual named as ADR-SR, which uses the Input Output Same Size (IOSS) structure, and releases the dependence of upsampling layers compared with the existing SR methods. Specifically, the key element of our model is the Adaptive Residual Block (ARB), which replaces the commonly used constant factor with an adaptive residual factor. The experiments prove the effectiveness of our ADR-SR model, which can not only reconstruct images with better visual effects, but also get better objective performances.
Shape Classification Using Hydrodynamic Detection via a Sparse Large-Scale 2D-Sensitive Artificial Lateral Line

Artificial lateral lines are fluid flow sensor arrays, bio-inspired by the fish lateral line organ, that measure a local hydrodynamic environment.These arrays are used to detect objects in water, without relying on light, sound, or on an active beacon.This passive sensing method, called hydrodynamic imaging, is complementary to sonar and vision systems and is suitable for collision avoidance and near-field covert sensing.This sensing method has so far been demonstrated on a biological scale from several to tens of centimeters.Here, we present measurements using a large-scale artificial lateral line of 3.5 meters, consisting of eight all-optical 2D-sensitive flow sensors.We measure the fluid flow as produced by the motion of five different objects, towed across a swimming pool.This results in repeatable stimuli, whose measurements demonstrate a complementary aspect of 2D-sensing.These measurements are both used for constructing temporal hydrodynamic signatures, which reflect the object's shape, and for flow-feature based near-field object classification.For the latter, we present a location-invariant feature extraction method which, using an Extreme Learning Machine neural network, results in a classification F1-score up to 98.6% with selected flow features.We find that, compared to the traditional sensing dimension parallel to the sensor array, the novel transverse fluid velocity component bears more information about the object shape.The classification of objects via hydrodynamic imaging thus benefits from 2D-sensing and can be scaled up to a supra biological scale of several meters.
A Scalable and Long-Term Wearable Augmented Reality System for Order Picking

Order picking is one of the most important tasks in modern warehouses, while augmented reality (AR) is a friendly alternative by conveying manual picking information into visual guidance to improve the picking performance. Nevertheless, there is still a lack of scalable and systematic consensus to deploy the pick-by-AR method associated with actual warehouse workplaces. This article describes a comprehensive methodology for the use of wearable AR in the order picking process. To enable a robust AR assisted manual order picking system in a warehouse floor, the marker-based global map about the actual shelves is established automatically, empowering a scalable, low-cost and long-term tracking capacity for pick-by-AR within the warehouse floor and allowing the operator to move freely in the actual workplace. The system is evaluated in the automobile assembly line, and experimental results illustrate that the proposed work can provide a systematic solution for pick-by-AR applications in actual warehouse fields.
A latent topic model with Markov transition for process data

We propose a latent topic model with a Markov transition for process data, which consists of time‐stamped events recorded in a log file. Such data are becoming more widely available in computer‐based educational assessment with complex problem‐solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying evolution of an examinee's latent state. Using topic transition probabilities along with response times enables us to capture examinees' learning trajectories, making clustering/classification more efficient. A forward‐backward variational expectation‐maximization (FB‐VEM) algorithm is developed to tackle the challenging computational problem. Useful theoretical properties are established under certain asymptotic regimes. The proposed method is applied to a complex problem‐solving item in the 2012 version of the Programme for International Student Assessment (PISA).
Optimal Feature Aggregation and Combination for Two-Dimensional Ensemble Feature Selection

Feature selection is a way of reducing the features of data such that, when the classification algorithm runs, it produces better accuracy. In general, conventional feature selection is quite unstable when faced with changing data characteristics. It would be inefficient to implement individual feature selection in some cases. Ensemble feature selection exists to overcome this problem. However, with the advantages of ensemble feature selection, some issues like stability, threshold, and feature aggregation still need to be overcome. We propose a new framework to deal with stability and feature aggregation. We also used an automatic threshold to see whether it was efficient or not; the results showed that the proposed method always produces the best performance in both accuracy and feature reduction. The accuracy comparison between the proposed method and other methods was 0.5–14% and reduced more features than other methods by 50%. The stability of the proposed method was also excellent, with an average of 0.9. However, when we applied the automatic threshold, there was no beneficial improvement compared to without an automatic threshold. Overall, the proposed method presented excellent performance compared to previous work and standard ReliefF.
Surveying public opinion using label prediction on social media data

In this study, a procedure is proposed for surveying public opinion from big social media domain-specific textual data to minimize the difficulties associated with modeling public behavior. Strategies for labeling posts relevant to a topic are discussed. A two-part framework is proposed in which semi-automatic labeling is applied to a small subset of posts, referred to as the "seed" in further text. This seed is used as bases for semi-supervised labeling of the rest of the data. The hypothesis is that the proposed method will achieve better labeling performance than existing classification models when applied to small amounts of labeled data. The seed is labeled using posts of users with a known and consistent view on the topic. A semi-supervised multi-class prediction model labels the remaining data iteratively. In each iteration, it adds context-label pairs to the training set if softmax-based label probabilities are above the threshold. The proposed method is characterized on four datasets by comparison to the three popular text modeling algorithms (n-grams + tfidf, fastText, VDCNN) for different sizes of labeled seeds (5,000 and 50,000 posts) and for several label-prediction significance thresholds. Our proposed semi-supervised method outperformed alternative algorithms by capturing additional contexts from the unlabeled data. The accuracy of the algorithm was increasing by (3-10%) when using a larger fraction of data as the seed. For the smaller seed, lower label probability threshold was clearly a better choice, while for larger seeds no predominant threshold was observed. The proposed framework, using fastText library for efficient text classification and representation learning, achieved the best results for a smaller seed, while VDCNN wrapped in the proposed framework achieved the best results for the bigger seed. The performance was negatively influenced by the number of classes. Finally, the model was applied to characterize a biased dataset of opinions related to gun control/rights advocacy. The proposed semi-automatic seed labeling is used to label 8,448 twitter posts of 171 advocates for guns control/rights. On this application, our approach performed better than existing models and it achieves 96.5% accuracy and 0.68 F1 score.
Variational Fisheye Stereo

Dense 3D maps from wide-angle cameras is beneficial to robotics applications such as navigation and autonomous driving. In this work, we propose a real-time dense 3D mapping method for fisheye cameras without explicit rectification and undistortion. We extend the conventional variational stereo method by constraining the correspondence search along the epipolar curve using a trajectory field induced by camera motion. We also propose a fast way of generating the trajectory field without increasing the processing time compared to conventional rectified methods. With our implementation, we were able to achieve real-time processing using modern GPUs. Our results show the advantages of our nonrectified dense mapping approach compared to rectified variational methods and non-rectified discrete stereo matching methods.
InFix: Automatically Repairing Novice Program Inputs

This paper presents InFix, a technique for automatically fixing erroneous program inputs for novice programmers. Unlike comparable existing approaches for automatic debugging and maintenance tasks, InFix repairs input data rather than source code, does not require test cases, and does not require special annotations. Instead, we take advantage of patterns commonly used by novice programmers to automatically create helpful, high quality input repairs. InFix iteratively applies error-message based templates and random mutations based on insights about the debugging behavior of novices. This paper presents an implementation of InFix for Python. We evaluate on 29,995 unique scenarios with input-related errors collected from four years of data from Python Tutor, a free online programming tutoring environment. Our results generalize and scale; compared to previous work, we consider an order of magnitude more unique programs. Overall, InFix is able to repair 94.5% of deterministic input errors. We also present the results of a human study with 97 participants. Surprisingly, this simple approach produces high quality repairs; humans judged the output of InFix to be equally helpful and within 4% of the quality of human-generated repairs.
Modelling and Simulation of ElasticSearch using CloudSim

Simulation can be a powerful technique for evaluating the performance of large-scale cloud computing services in a relatively low cost, low risk and time-sensitive manner. Large-scale data indexing, distribution and management is complex to analyse in a timely manner. In this paper, we extend the CloudSim cloud simulation framework to model and simulate a distributed search engine architecture and its workload characteristics. To test the simulation framework, we develop a model based on a real-world ElasticSearch deployment on Linknovate.com. An experimental evaluation of the framework, comparing simulated and actual query response time, precision and resource utilisation, suggests that the proposed framework is capable of predicting performance at different scales in a precise, accurate and efficient manner. The results can assist ElasticSearch users to manage their scalability and infrastructure requirements.
Debugging Smart Contract’s Business Logic Using Symbolic Model Checking

Smart contracts are a special type of programs running inside a blockchain. Immutable and transparent, they provide means to implement fault-tolerant and censorship-resistant services. Unfortunately, its immutability causes a serious challenge of ensuring that a business logic and implementation is correct upfront, before publishing in a blockchain. Several big accidents have indeed shown that users of this technology need special tools to verify smart contract correctness. Existing automated checkers are able to detect only well known implementation bugs, leaving the question of business logic correctness far aside. In this work, we present a symbolic model-checking technique along with a formal specification method for a subset of Solidity programming language that is able to express both state properties and trace properties; the latter constitutes a weak analogy of temporal properties. We evaluate the proposed technique on the MiniDAO smart contract, a young brother of notorious TheDAO. Our Proof-of-Concept was able to detect a non-trivial error in the business logic of this smart contract in a few seconds.
Predicting Global Computing Power of Blockchain Using Cryptocurrency Prices

Blockchain is a disruptive technology that enables disparate users to share their information in blocks trustworthily without a centralized entity. One fundamental problem is how to stable the block interval. To address this problem, our method is: 1. predict the computing power (i.e., hashrate) of a blockchain system by the cryptocurrency price; 2. stable the interval according to the predicted power. This paper focuses on the prediction of the global computing power. In our prediction, we adopt a LSTM-based regression algorithm to handle the hysteresis of computing power changes in response to the price changes. Taking the Bitcoin system as an example, we run extensive experiments that verify that our prediction algorithm is very accurate.
Elliptic curve‐based proxy re‐signcryption scheme for secure data storage on the cloud

Summary Consider the situation of a patient carrying a body area network with sensors measuring different body characteristics. These data should be securely sent to the cloud and only retrievable by doctors authorized by the patient. Even the cloud service provider should not be able to derive the sensible data. In order to address this problem, this paper first proposes a highly efficient certificate‐based signcryption scheme, able to execute signing and encryption in one single phase and to guarantee the link between identity and public key. We show that our proposed scheme outperforms others both in computation and communication complexity. Next, based on this primitive, a complete proxy re‐signcryption scheme is presented to be integrated in this smart body area network. The proposed schemes are based on elliptic curve operations and do not use compute intensive pairing operations, like previous proposals. We show that our scheme outperforms previous proposed proxy re‐signcryption schemes in the literature.
SCA2: Novel Efficient Swarm Clustering Algorithm

Clustering is a classical unsupervised learning task that aims to reveal data similarity patterns. Numerous algorithms have been proposed to address this task from different aspects. In the field of swarm intelligence and evolutionary algorithms, most existing algorithms strive to identify a set of cluster centers. However, it is difficult for centroid-based algorithms to process data with clusters of arbitrary shapes. Thus, a clustering algorithm named Swarm Clustering Algorithm (SCA) was proposed to cluster data from a novel aspect, which regards each point in the dataset as a particle, and particles fly towards denser areas to form clusters automatically. In this article, a novel efficient swarm clustering algorithm named SCA2 is proposed, which extends SCA in terms of three aspects: (1) the radial basis function network is adopted as the surrogate model to reduce the time complexity; (2) there are k leaders for each particle, and the particle may follow one of them to decrease misleading; and (3) a simplified strategy is used to update the position of each particle. The performance of SCA2 on different types of synthetic and real-world datasets was compared with the performance of four classical algorithms, SCA as well as a PSO-based clustering algorithm. The experimental results demonstrate that SCA2 is more competitive.
Human Action Recognition and Assessment Via Deep Neural Network Self-Organization

The robust recognition and assessment of human actions are crucial inHuman-Robot Interaction (HRI) human-robot interaction (HRI) domains. While state-of-the-art models of action perception show remarkable results in large-scale action datasets, they mostly lack the flexibility, robustness, and scalability needed to operate in natural HRI scenarios which require the continuous acquisition of sensory information as well as the classification or assessment of human body patterns in real time. In this chapter, I introduce a set of hierarchical models for the learning and recognition of actions from depth maps and RGB images through the use of neural network self-organization. A particularity of these models is the use of growing self-organizing networksSelf-organizing networks/maps that quickly adapt to non-stationary distributions and implement dedicated mechanisms for continual learningContinual learning from temporally correlated input.
Improvement of MNIST Image Recognition Based on CNN

Abstract At present, great progress has been made in the field of image recognition, especially in convolutional neural network. Lenet-5 convolutional neural network has been able to identify handwritten digit MNIST database with high precision. In this paper, experiments show that different activation functions, learning rates and the addition of the Dropout layer in front of the output layer will make the convergence speed different, weaken the influence of the initial parameters on the model, and improve the training accuracy. It is proved that the modified LeNet-5 model has a better improvement in handwritten digit recognition. This method is an efficient recognition method.
Is Intellectual Property Law Ready for Artificial Intelligence?

The point that Artificial Intelligence (AI) will change the law is trite and obvious by now. How it will change the law, and how the law will change AI, are much harder questions to answer, however. Most of the hard questions arise when what I have elsewhere called an ‘autonomy threshold’ has been crossed.1 One could also call it an agency threshold. What this means is that when an AI machine, whether in the form of a robot (such as a drone or self-driving vehicle), or a bot or other software-implemented AI system makes choices, the legal situation changes. To an outside observer, a self-driving car unquestionably makes choices: did it avoid an accident by going left or right? What if it caused a smaller accident to avoid a bigger one – that is the well-known trolley problem traditionally used by ethics professors. Machines make all kinds of choices. An AI machine is reportedly used in Canada to decide whether the marriage of someone applying for a residence permit after marrying a Canadian is a real marriage. AI machines suggest bail and sentencing decisions to judges, who by and large follow them. And the list goes on. The law will have to apportion liability for the choices made by those machines, their owners, and their users/operators because the choices made or informed by those AI systems impact real human lives.
A comparison of features extraction methods for Arabic sentiment analysis

Natural Language Processing (NLP) has built up so much importance in the past few years. With machine learning, NLP can detect a lot of unseen information from a huge volume of textual data, which can be helpful for sentiment analysis and text classification. For those tasks, features can be extracted, the operation bases on extracting an important subset of features from a data. However, identifying convenient features is very important for improving the NLP tasks. For Arabic Language the process of getting the related features is difficult due to many reasons, for example, this language has the most words compared to other languages. Our contribution in this paper is to analyze the impact of feature extraction methods such as Bag-of-Words, TF-IDF, and word2vec on the performance of sentiment analysis using Arabic language. The extracted features are evaluated using many machine learning algorithms like Logistic Regression and Support Vector Machine.
Taming the Beast: Harnessing Blockchains in Developing Country Governments

Amid pressing demands to tackle critical sustainable development goals, governments in developing countries face the additional complex task of embracing new digital technologies such as blockchains. This paper takes a conceptual approach and presents a framework that interlinks development, technology, and governments while highlighting the relevance of state capacity and democratic governance in such dynamics. With this in hand, blockchain technology is revisited from the perspective of governments in the Global South, identifying in the process key traits and presenting a new typology. Research gaps regarding the relationship between this technology and both development and developing country government are also identified. A closer look at some on the ground blockchain implementations yields some early evidence on the advantages and risks involved in the process. The paper closes with a discussion of the results within the proposed framework suggesting additional research themes. It also provides some basic guidance for development practitioners interested in enhancing current programs using blockchains as an enabler.
Semistructured Merge in JavaScript Systems

Industry widely uses unstructured merge tools that rely on textual analysis to detect and resolve conflicts between code contributions. Semistructured merge tools go further by partially exploring the syntactic structure of code artifacts, and, as a consequence, obtaining significant merge accuracy gains for Java-like languages. To understand whether semistructured merge and the observed gains generalize to other kinds of languages, we implement two semistructured merge tools for JavaScript, and compare them to an unstructured tool. We find that current semistructured merge algorithms and frameworks are not directly applicable for scripting languages like JavaScript. By adapting the algorithms, and studying 10,345 merge scenarios from 50 JavaScript projects on GitHub, we find evidence that our JavaScript tools report fewer spurious conflicts than unstructured merge, without compromising the correctness of the merging process. The gains, however, are much smaller than the ones observed for Java-like languages, suggesting that semistructured merge advantages might be limited for languages that allow both commutative and non-commutative declarations at the same syntactic level.