Reviewerly/exHarmony huggingface dataset:
A Kalman Filter-Based Kernelized Correlation Filter Algorithm for Pose Measurement of a Micro-Robot

This paper proposes a moving-target tracking algorithm that measures the pose of a micro-robot with high precision and high speed using the Kalman filter-based kernelized correlation filter (K2CF) algorithm. The adaptive Kalman filter can predict the state of linearly and nonlinearly fast-moving targets. The kernelized correlation filter algorithm then accurately detects the positions of the moving targets and uses the detection results to modify the moving states of the targets. This paper verifies the performance of the algorithm on a monocular vision measurement platform and using a pose measurement method. The K2CF algorithm was embedded in the micro-robot’s attitude measurement system, and the tracking performances of three different trackers were compared under different motion conditions. Our tracker improved the positioning accuracy and maintained real-time operation. In a comparison study of K2CF and many other algorithms on Object Tracking Benchmark-50 and Object Tracking Benchmark-100 video sequences, the K2CF algorithm achieved the highest accuracy. In the 400 mm × 300 mm field of view, when the target radius is about 3 mm and the inter-frame acceleration displacement does not exceed 5.6 mm, the root-mean-square error of position and attitude angle can satisfy the precision requirements of the system.
Research on Digital Resource System Construction of Smart Library Based on Computer Network and Artificial Intelligence

Abstract With the development of science and technology, artificial intelligence has become one of the main driving forces for the development of modern society, and has also injected new vitality into the future development of libraries. Based on the high efficiency and scientific of artificial intelligence technology, the technology has been widely used in intelligent search and reasoning, machine learning and human imitation. In the “internet plus” era, the information service carried out by libraries has changed to a new intelligent information service mode. With the support of new technologies, the intelligent library has gradually become a new form of library development, showing the characteristics of digitalization, networking, intelligence and mobility. Artificial intelligence technology has changed the management mode, management content and service mechanism of libraries, and promoted the transformation of traditional libraries towards smart libraries. This paper first expounds the basic concepts of artificial intelligence and smart library, then analyses the constituent elements of smart library, and finally focuses on the construction method of digital resource system of smart library.
Revealing Cyber Threat of Smart Mobile Devices within Digital Ecosystem: User Information Security Awareness

In recent years, the number of mobile device users has increased at a significant rate due to the rapid technological advancement in mobile technology. While mobile devices are providing more useful features to its users, it has also made it possible for cyber threats to migrate from desktops to mobile devices. Thus, it is important for mobile device users to be aware that their mobile device could be exposed to cyber threats and that users could protect their devices by employing cyber security measures. This study discusses how users in responded to the smart mobile devices (SMD) breaches. A number of behavioural model theories are used to understand the user behaviour towards security features of smart mobile devices. To assess the impact of smart mobile devices (SMD) security and privacy, surveys had been conducted with users, stressing on product preferences, user behaviour of SMD, as well as perceptions on the security aspect of SMD. The results was very interesting, where the findings revealed that there were a lack of positive relationships between SMD users and their level of SMD security awareness. A new framework approach to securing SMD is proposed to ensure that users have strong protection over their data within SMD.
Sports Recognition using Convolutional Neural Network with Optimization Techniques from Images and Live Streams

This paper deals with the issue of automated image and video recognition of sports. It is a category of appreciation of human behavior, which is a very difficult task in the present day to classify images and video clips into a categorized gallery. This research paper proposes a sports detection system using a deeper CNN model that combines a fully connected layer with fine-tuning. It is applied to classify five individual sports groups through images and videos. In this work, we use a video classification method based on the image. Extended Resnet50 and VGG16 two pre-trained Deep CNNs are applied to build this sports detection system. RMSProp, ADAM &amp; SGD optimizers are used to train the extended CNN models for five Epochs on the proposed 5sports dataset by handpicking thousands of sports images from the internet to very smoothly classify the five different types of sports. Training accuracy of approximately 83% is observed for ResNet50 with an SGD optimizer for 5 sports classes and 95% is observed for 3 sports classes.
CEAM: The Effectiveness of Cyclic and Ephemeral Attention Models of User Behavior on Social Platforms

To improve the user experience as well as business outcomes, social platforms aim to predict user behavior. To this end, recurrent models are often used to predict a user's next behavior based on their most recent behavior. However, people have habits and routines, making it plausible to predict their behavior from more than just their most recent activity. Our work focuses on the interplay between ephemeral and cyclical components of user behaviors. By utilizing user activity data from social platform Snapchat, we uncover cyclic and ephemeral usage patterns on a per user level. Based on our findings, we imbued recurrent models with awareness: we augment an RNN with a cyclic module to complement traditional RNNs that model ephemeral behaviors and allow a flexible weighting of the two for the prediction task. We conducted extensive experiments to evaluate our model's performance on four user behavior prediction tasks on the Snapchat platform. We achieve improved results on each task compared against existing methods, using this simple, but important insight in user behavior: Both cyclical and ephemeral components matter. We show that in some situations and for some people, ephemeral components may be more helpful for predicting behavior, while for others and in other situations, cyclical components may carry more weight.
Effective Batching for Recurrent Neural Network Grammars

As a language model that integrates traditional symbolic operations and flexible neural representations, recurrent neural network grammars (RNNGs) have attracted great attention from both scientific and engineering perspectives.However, RNNGs are known to be harder to scale due to the difficulty of batched training.In this paper, we propose effective batching for RNNGs, where every operation is computed in parallel with tensors across multiple sentences.Our PyTorch implementation effectively employs a GPU and achieves x6 speedup compared to the existing C++ DyNet implementation with model-independent auto-batching.Moreover, our batched RNNG also accelerates inference and achieves x20-150 speedup for beam search depending on beam sizes.Finally, we evaluate syntactic generalization performance of the scaled RNNG against the LSTM baseline, based on the large training data of 100M tokens from English Wikipedia and the broad-coverage targeted syntactic evaluation benchmark. 1
Benchmarking Robustness of Machine Reading Comprehension Models

Machine Reading Comprehension (MRC) is an important testbed for evaluating models' natural language understanding (NLU) ability.There has been rapid progress in this area, with new models achieving impressive performance on various benchmarks.However, existing benchmarks only evaluate models on indomain test sets without considering their robustness under test-time perturbations or adversarial attacks.To fill this important gap, we construct AdvRACE (Adversarial RACE), a new model-agnostic benchmark for evaluating the robustness of MRC models under four different types of adversarial attacks, including our novel distractor extraction and generation attacks.We show that state-of-the-art (SOTA) models are vulnerable to all of these attacks.We conclude that there is substantial room for building more robust MRC models and our benchmark can help motivate and measure progress in this area.We release our data and code at https://github.com/NoviScl/AdvRACE.
Dataset for Anomalies Detection in 3D Printing

Nowadays, the Internet of Things plays a significant role in many domains. Especially, Industry 4.0 is making significant usage of concepts like smart sensors and big data analysis. IoT devices are commonly used to monitor industry machines and detect anomalies in their work. This paper presents and describes a set of data streams coming from a working 3D printer. Among others, it contains accelerometer data of printer head, intrusion power and temperatures of the printer elements. In order to gain data, we lead to several printing malfunctions applied to the 3D model. The resulting dataset can therefore be used for anomalies detection research.
Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts

As predictive models are increasingly being deployed to make a variety of consequential decisions, there is a growing emphasis on designing algorithms that can provide recourse to affected individuals. Existing recourse algorithms function under the assumption that the underlying predictive model does not change. However, models are regularly updated in practice for several reasons including data distribution shifts. In this work, we make the first attempt at understanding how model updates resulting from data distribution shifts impact the algorithmic recourses generated by state-of-the-art algorithms. We carry out a rigorous theoretical and empirical analysis to address the above question. Our theoretical results establish a lower bound on the probability of recourse invalidation due to model shifts, and show the existence of a tradeoff between this invalidation probability and typical notions of "cost" minimized by modern recourse generation algorithms. We experiment with multiple synthetic and real world datasets, capturing different kinds of distribution shifts including temporal shifts, geospatial shifts, and shifts due to data correction. These experiments demonstrate that model updation due to all the aforementioned distribution shifts can potentially invalidate recourses generated by state-of-the-art algorithms. Our findings thus not only expose previously unknown flaws in the current recourse generation paradigm, but also pave the way for fundamentally rethinking the design and development of recourse generation algorithms.
Interdependent creativity for learning in a virtual world

Purpose The purpose of this paper is to understand how the creativity of pupils and teachers is nurtured through the use of a virtual world (VW) within a sociotechnical network affecting pupils’ learning in a pilot secondary school. Design/methodology/approach The analysis is the result of a pluri-disciplinary systemic analysis involving didactics, sociology, psychology and management science on an individual, collective and systemic scale. This participatory action research is based on interviews and systematic observations in class, in-world and in the global ecosystem. Linguistic and multimodal analysis is applied to the data, through teacher monographs that hint at the teachers’ activity. Findings Pupils’ and teachers’ creativity appeared to be anchored within four main interdependent nurturing conditions the personal inclinations and professional interactions in the sociotechnical network sustaining the VW; a creative regulation allowing compromises with the institutional constraints of pedagogical control; avatars and 3 D boundary objects that act as a motor of teachers-pupils inquiry and creativity; the sociotechnical network that contributes, through the actors’ play, to bringing the organisational rules of the school towards an innovation trajectory, that in turns mediates success in the use and the adoption of the new technology. Research limitations/implications Although this is a study within a specific school, the findings can be put to use by other pedagogical teams who would wish to integrate a VW to re-engage pupils. Practical implications The participatory design processes taking place within a sociotechnical network support teachers in the building of Virtual World scenarios negotiated with researchers and start-up developers. Social implications The pedagogical use of a virtual world opens new learning engagement opportunities for the pupils through enhanced experiential learning and sustains the transformation of teachers’ professionality. Originality/value The authors’ approach differs from the previous educational VW literature, in that they integrate the teachers’ creativity and their pedagogical scripts into their study, within a systemic approach, thus requiring a wider theoretical framework, necessary for understanding the building of strategies and knowledge that foster teachers’ and pupils’ creativity in educational settings using a VW.
Deep Learning Models for Intelligent Healthcare: Implementation and Challenges

The rapid developments of artificial intelligent (AI) is being transformed for its extensive use-cases, people-centered intelligent systems focusing on care delivery, research encounter complex problems related to improve the overall infrastructure and management of intelligent delivery service; for instance, bringing transformation in healthcare sector for monitoring patients with chronic disease. Most of these systems are driven by state-of-the-art learning algorithms i.e., Convolution Neural Network. The CNN algorithm is considered to be one of the most prominent architectures of DL. Recently, due to enormous growth in the amount of annotated data and the development of CNN hardware accelerator, further, boost the research on CNN and accomplished benchmark enactment on different applications. This paper presents cutting-edge applications of CNN for an intelligent healthcare system. We provide useful findings of different CNN features such as optimization, fast computation, design, activation function, and loss function. To our knowledge, this is the first comprehensive work to address the recent trends in the architecture of CNN, which offers insight to the underlying problems and provides the potential solutions for any given intelligent healthcare applications.
Optimized Scale-Invariant Feature Transform with Local Tri-directional Patterns for Facial Expression Recognition with Deep Learning Model

Abstract Facial expression recognition (FER) is the process of identifying human expressions. People vary in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively important research area. Various works have been conducted on automating the recognition of facial expressions. The main intent of this paper is to plan for the FER model with the aid of intelligent techniques. The proposed models consist of steps like data collection, face detection, optimized feature extraction and emotion recognition. Initially, the standard benchmark facial emotion dataset is collected, and it is subjected to face detection. The optimized scale-invariant feature transform (OSIFT) is adopted for feature extraction, in which the key points that are giving unique information are optimized by the hybrid meta-heuristic algorithm. Two meta-heuristic algorithms like spotted hyena optimization and beetle swarm optimization (BSO) are merged to form the proposed spotted hyena-based BSO (SH-BSO). Also, the local tri-directional pattern is extracted, which is further combined with optimized SIFT. Here, the proposed SH-BSO is utilized for optimizing the number of hidden neurons of both deep neural network and convolutional neural network in such a way that the recognition accuracy could attain maximum.
A Normative Supervisor for Reinforcement Learning Agents

Abstract We introduce a modular and transparent approach for augmenting the ability of reinforcement learning agents to comply with a given norm base. The normative supervisor module functions as both an event recorder and real-time compliance checker w.r.t. an external norm base. We have implemented this module with a theorem prover for defeasible deontic logic, in a reinforcement learning agent that we task with playing a “vegan” version of the arcade game Pac-Man.
Towards the Automatic Mathematician

Abstract Over the recent years deep learning has found successful applications in mathematical reasoning. Today, we can predict fine-grained proof steps, relevant premises, and even useful conjectures using neural networks. This extended abstract summarizes recent developments of machine learning in mathematical reasoning and the vision of the N2Formal group at Google Research to create an automatic mathematician. The second part discusses the key challenges on the road ahead.
Knowledge Informed Semantic Parsing for Conversational Question Answering

Smart assistants are tasked to answer various questions regarding world knowledge.These questions range from retrieval of simple facts to retrieval of complex, multi-hops question followed by various operators (i.e., filter, argmax).Semantic parsing has emerged as the state-of-the-art for answering these kinds of questions by forming queries to extract information from knowledge bases (KBs).Specially, neural semantic parsers (NSPs) effectively translate natural questions to logical forms, which execute on KB and give desirable answers.Yet, NSPs suffer from nonexecutable logical forms for some instances in the generated logical forms might be missing due to the incompleteness of KBs.Intuitively, knowing the KB structure informs NSP with changes of the global logical forms structures with respect to changes in KB instances.In this work, we propose a novel knowledgeinformed decoder variant of NSP.We consider the conversational question answering settings, where a natural language query, its context and its final answers are available at training.Experimental results show that our method outperformed strong baselines by 1.8 F1 points overall across 10 types of questions of the CSQA dataset.Especially for the "Logical Reasoning" category, our model improves by 7 F1 points.Furthermore, our results are achieved with 90.3% fewer parameters, allowing faster training for large-scale datasets.
Rudist bivalves (Hippuritoidea) from the Clifton Limestone (Lower Campanian) of western Jamaica and a reassessment of the genus Vaccinites in the Americas

The lower Campanian (Upper Cretaceous) Clifton Limestone of Jamaica yields three species of hippuritid bivalve: Barrettia ruseae CHUBB, Whitfieldiella luceae sp. nov. and Vaccinites vermunti MAC GILLAVRY, and the plagioptychid: Plagioptychus sp. The hippuritids are described in detail using statistics. Barrettia ruseae is demonstrated to be a more primitive species of Barrettia than B. monilifera WOODWARD or B. multilirata WHITFIELD, and the species Whitfieldiella luceae is shown to be a more primitive species of Whitfieldiella than W. gigas CHUBB. The specimens of Vaccinites from the Clifton Limestone are compared with populations of Vaccinites from elsewhere in the Americas, and five species (probably representing a single evolutionary lineage) are recognized: V. alencasteri sp. nov. (?late Turonian-?Coniacian), V. martini MAC GILLAVRY (probably early to mid Santonian), V. macgillavryi PALMER (probably mid to late Santonian), V. vermunti MAC GILLAVRY (earliest Campanian), and V. temazcali sp. nov. (late early Campanian). The Vaccinites species can be distinguished using statistical techniques. The ages of the Clifton Limestone and the five Vaccinites species are reviewed. This research demonstrates the value of using hippuritids for biostratigraphy in the Upper Cretaceous of the Americas.
A light field measurement system through PSF estimation by a morphology-based method

Abstract Light field imaging technology can obtain three-dimensional (3D) information of a test surface in a single exposure. Traditional light field reconstruction algorithms not only take a long time to trace back to the original image, but also require the exact parameters of the light field system, such as the position and posture of a microlens array (MLA), which will cause errors in the reconstructed image if these parameters cannot be precisely obtained. This paper proposes a reconstruction algorithm for light field imaging based on the point spread function (PSF), which does not require prior knowledge of the system. The accurate PSF derivation process of a light field system is presented, and modeling and simulation were conducted to obtain the relationship between the spatial distribution characteristics and the PSF of the light field system. A morphology-based method is proposed to analyze the overlapping area of the subimages of light field images to identify the accurate spatial location of the MLA used in the system, which is thereafter used to accurately refocus light field imaging. A light field system is built to verify the algorithm’s effectiveness. Experimental results show that the measurement accuracy is increased over 41.0% compared with the traditional method by measuring a step standard. The accuracy of parameters is also improved through a microstructure measurement with a peak-to-valley value of 25.4% and root mean square value of 23.5% improvement. This further validates that the algorithm can effectively improve the refocusing efficiency and the accuracy of the light field imaging results with the superiority of refocusing light field imaging without prior knowledge of the system. The proposed method provides a new solution for fast and accurate 3D measurement based on a light field.
Understanding parameter spaces using local optima networks

A major challenge with utilizing a metaheuristic is finding optimal or near optimal parameters for a given problem instance. It is well known that the best performing control parameters are often problem dependent, with poorly chosen parameters even leading to algorithm failure. What is not obvious is how strongly the complexity of the parameter landscape itself is coupled with the underlying objective function the metaheuristic is attempting to solve. In this paper local optima networks (LONs) are utilized to visualize and analyze the parameter landscapes of particle swarm optimization (PSO) over an array of objective functions. It was found that the structure of the parameter landscape is affected by the underlying objective function, and in some cases by a considerable degree across multiple metrics. Furthermore, despite PSO's parameter landscape having a relatively simple macro structure, the LONs demonstrate that there is actually a considerable amount of complexity at a micro level; making parameter tuning harder for PSO than would have been initially thought. Apart from the PSO specific findings this paper also provides a formalism of parameter landscapes and demonstrates that LONs can be used as an effective tool in the analysis and visualization of parameter landscapes of metaheuristics.