Reviewerly/exHarmony huggingface dataset:
Deep Learning Approach for Human Action Recognition Using Gated Recurrent Unit Neural Networks and Motion Analysis

Human action recognition is a computer vision task.The evaluation of action recognition algorithms relies on the proper extraction and learning of the data.The success of the deep learning and especially learning layer by layer led to many imposing results in several contexts that include neural network.Here the Recurrent Neural Networks (RNN) with hidden unit has demonstrated advanced performance on tasks as varied as image captioning and handwriting recognition.Specifically Gated Recurrent Unit (GRU) is able to learn and take advantage of sequential and temporal data required for video recognition.Moreover video sequence can be better described on both visual and moving features.In this paper, we present our approach for human action recognition based on fusion and combination of sequential visual features and moving path.We evaluate our technique on the challenging UCF Sports Action, UCF101 and KTH dataset for human action recognition and obtain competitive results.
Distributed semi-supervised learning algorithm based on extreme learning machine over networks using event-triggered communication scheme

In this paper, we propose a distributed semi-supervised learning (DSSL) algorithm based on the extreme learning machine (ELM) algorithm over communication network using the event-triggered (ET) communication scheme. In DSSL problems, training data consisting of labeled and unlabeled samples are distributed over a communication network. Traditional semi-supervised learning (SSL) algorithms cannot be used to solve DSSL problems. The proposed algorithm, denoted as ET-DSS-ELM, is based on the semi-supervised ELM (SS-ELM) algorithm, the zero gradient sum (ZGS) distributed optimization strategy and the ET communication scheme. Correspondingly, the SS-ELM algorithm is used to calculate the local initial value, the ZGS strategy is used to calculate the globally optimal value and the ET scheme is used to reduce communication times during the learning process. According to the ET scheme, each node over the communication network broadcasts its updated information only when the event occurs. Therefore, the proposed ET-DSS-ELM algorithm not only takes the advantages of traditional DSSL algorithms, but also saves network resources by reducing communication times. The convergence of the proposed ET-DSS-ELM algorithm is guaranteed by using the Lyapunov method. Finally, some simulations are given to show the efficiency of the proposed algorithm.
Mapping Spatiotemporal Data to RDF: A SPARQL Endpoint for Brussels

This paper describes how a platform for publishing and querying linked open data for the Brussels Capital region in Belgium is built. Data are provided as relational tables or XML documents and are mapped into the RDF data model using R2RML, a standard language that allows defining customized mappings from relational databases to RDF datasets. In this work, data are spatiotemporal in nature; therefore, R2RML must be adapted to allow producing spatiotemporal Linked Open Data.Data generated in this way are used to populate a SPARQL endpoint, where queries are submitted and the result can be displayed on a map. This endpoint is implemented using Strabon, a spatiotemporal RDF triple store built by extending the RDF store Sesame. The first part of the paper describes how R2RML is adapted to allow producing spatial RDF data and to support XML data sources. These techniques are then used to map data about cultural events and public transport in Brussels into RDF. Spatial data are stored in the form of stRDF triples, the format required by Strabon. In addition, the endpoint is enriched with external data obtained from the Linked Open Data Cloud, from sites like DBpedia, Geonames, and LinkedGeoData, to provide context for analysis. The second part of the paper shows, through a comprehensive set of the spatial extension to SPARQL (stSPARQL) queries, how the endpoint can be exploited.
Cracking and Improvement of an Image Encryption Algorithm Based on Bit-Level Permutation and Chaotic System

In recent years, with the rapid development of the network communication, information security issues have become increasingly prominent. As an important carrier of information, image plays an important role in the transmission process. Therefore, lots of image encryption algorithms have been proposed. However, these algorithms are not designed in accordance with the unified security standards and requirements, so there may be a lot of security risks, and what is the worst thing is that some encryption algorithms may be cracked in an acceptable time complexity by some cryptographic analysis. This paper focuses on one of the most influential algorithm proposed by Zhang, which improved Zhu's algorithm. The non-fixed parameter and diffusion-permutation strategy are adopted by Zhang, but there are still problems that cannot resist chosen-plaintext attack and the key space is not enough to resist brute-force attack. We use chosen-plaintext attack to crack Zhang algorithm, and then propose a further improved scheme.
A Sound and Complete Axiomatisation for Spatio-Temporal Specification Language

Specifying spatio-temporal aspects is one of the important areas in cyber-physical systems.Spatio-temporal logic with changes of truth value in discrete time and dense time has been researched, but a combination of spatial and temporal components with changes of spatial entities in dense time hasn't been well-done.The major problem is dense time and real-valued variables of the spatio-temporal properties of cyber-physical systems.In this paper, we propose a spatio-temporal specification language, named STSL, which integrates Signal Temporal Logic (STL) with a spatial logic S4u to deal with the changes of realvalues spatial entities in dense time.The combined language is divided into two formalisms, ST SLP C and ST SLOC , which is applied to interpret the Boolean semantics and quantitative semantics, respectively.The syntax of the two formalism and the corresponding semantics are provided.Besides, we present a Hilbert-style axiomatization for the proposed STSL and provide the soundness and completeness result by the spatio-temporal extension of maximal consistent set and canonical model.
Interferometric measurement of freeform surfaces using irregular subaperture stitching

Irregular subaperture stitching interferometry (ISSI) is proposed for non-null optical freeform surface testing. Compared with circular and annular subapertures, irregular subapertures have a better performance in matching the locally resolvable interferograms due to the rotational asymmetry of freeform surfaces. A modified multi-aperture simultaneous reverse optimization reconstruction algorithm is employed for the subaperture stitching, allowing not only non-complementary subapertures but also no subaperture overlaps. Numerical simulations with error analyses display the accuracy of our method. Experiments showing the effectiveness of the ISSI are presented by testing a Φ20 mm biconic surface with cross-validation.
Introduction to Linear Algebra

Although the content in this chapter should be taught in much earlier life stages, graduate or undergraduate levels, of most possible reader, because this book mainly deals with somewhat data science oriented matters, it might not be a bad idea to reintroduce fundamental concepts in a data science oriented manner.
Gabor-based anisotropic diffusion with lattice Boltzmann method for medical ultrasound despeckling

Medical ultrasound images are corrupted by speckle noise, and despeckling methods are required to effectively and efficiently reduce speckle noise while simultaneously preserving details of tissues. This paper proposes a despeckling approach named the Gabor-based anisotropic diffusion coupled with the lattice Boltzmann method (GAD-LBM), which uses the lattice Boltzmann method (LBM) to fast solve the partial differential equation of an anisotropic diffusion model embedded with the Gabor edge detector. We evaluated the GAD-LBM on both synthetic and clinical ultrasound images, and the experimental results suggested that the GAD-LBM was superior to other nine methods in speckle suppression and detail preservation. For synthetic and clinical images, the computation time of the GAD-LBM was about 1/90 to 1/20 of the GAD solved with the finite difference, indicating the advantage of the GAD-LBM in efficiency. The GAD-LBM not only has excellent ability of noise reduction and detail preservation for ultrasound images, but also has advantages in computational efficiency.
Cryptanalysis of an Authentication Scheme Using an Identity Based Generalized Signcryption

Secure data transmission is a challenging issue in modern data communication. ID based generalized signcryption is a cost effective security primitive which provides authentication or confidentiality, or jointly confidentiality and authentication. Wei’s proposed an ID based generalized signcryption scheme for authentication and confidentiality of big data in a standard model, claiming that their scheme holds the security of indistinguishability against adaptive chosen-ciphertext attacks and existential unforgeability against adaptive chosen message attacks. In this paper, we analyzed Wei’s scheme by launching security attacks on the scheme to check its validity. As a result, it became clear and proved that the master secret key generated in the scheme is compromisable. Similarly, the mentioned scheme does not hold the security of indistinguishability against adaptive chosen-ciphertext attacks and existential unforgeability against adaptive chosen message attacks. Consequently, Wei’s schemes is prone to attacks and is insecure.
P(L)AY ATTENTION! Co-designing for and with Children with Attention Deficit Hyperactivity Disorder (ADHD)

In recent years, children's mental health problems, including Attention Deficit Hyperactivity Disorder (ADHD), have been a growing phenomenon. However, there are limited examples of designing for and with children with ADHD. This work views conditions such as ADHD through the lens of neurodiversity as different cognitive styles, focusing on "coolabilities" and enhanced competences instead of disabilities. This paper explores how to engage children with ADHD in co-design activities. Taking the Diversity for Design (D4D) framework as a starting point, an adaptation of the framework for ADHD was first driven by theoretical considerations and three expert interviews, followed by an empirical study consisting of three co-design workshops with four male participants (aged 7–10). Based on observations and audio recordings from the co-design workshops, a qualitative analysis was carried out. Our results show that when their needs, preferences, and individual desires are taken into account, children with ADHD can be meaningfully engaged in co-design activities. By offering an adapted version of the D4D framework tailored for ADHD, designers can structure the environment and provide scaffolds so that children with ADHD can become active participants in co-design workshops. This research informs the design community on how to engage and involve children with ADHD into the design process.
Shallow Domain Adaptive Embeddings for Sentiment Analysis

This paper proposes a way to improve the performance of existing algorithms for text classification in domains with strong language semantics.We propose a domain adaptation layer learns weights to combine a generic and a domain specific (DS) word embedding into a domain adapted (DA) embedding.The DA word embeddings are then used as inputs to a generic encoder + classifier framework to perform a downstream task such as classification.This adaptation layer is particularly suited to datasets that are modest in size, and which are, therefore, not ideal candidates for (re)training a deep neural network architecture.Results on binary and multi-class classification tasks using popular encoder architectures, including current state-of-the-art methods (with and without the shallow adaptation layer) show the effectiveness of the proposed approach.this is a great movie 1 0 Logistic Regression Maxpooling layer Convolution layer Input sentence embedding matrix Adaptation Layer
Hierarchical Deep Learning for Arabic Dialect Identification

In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification.The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification.The main idea is to separate the classification process for a sentence from a given text in two stages.We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes).The second approach is given by a voting system based on Naive Bayes and Random Forest.Our system achieves an F 1 score of 63.02% on the subtask evaluation dataset.
The Effect of Using Mobile Banking Services Applications on Electronic Word of Mouth: The Mediating Role of Perceived Trust

The objective of this study is to examine selected dimensions of mobile banking, (such as Ease of Navigation, Personalization Level, Information Quality, Rewards) on e-wom through the mediating role of perceived trust. Self-structured questionnaire is used to collect data which is then shared via Google forms online and targeted only to the users of mobile banking services application in Jordan. Quantitative and analytical methods were used to analyze the data. 499 questioners were returned, 30 of which were rejected as they were not using mobile banking service applications. 469 of the questionnaires were accepted and analyzed using reliability test analysis, descriptive statistics and regression process.&#x0D; &#x0D; The findings of the study indicate that there is a statistically significant effect of mobile banking (Ease of Navigation, Personalization Level, Information quality, Rewards) through the mediation role of perceived trust on relationships between using mobile banking services and e-wom. It is recommended that banks pay more attention to mobile banking and build powerful and good E-WOM in order to spread the use of mobile banking rapidly.
Multi-Resolution Weak Supervision for Sequential Data

Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to individual elements or collections of elements in a sequence. A key challenge in weak supervision is estimating the unknown accuracies and correlations of these sources without using labeled data. Multi-resolution sources exacerbate this challenge due to complex correlations and sample complexity that scales in the length of the sequence. We propose Dugong, the first framework to model multi-resolution weak supervision sources with complex correlations to assign probabilistic labels to training data. Theoretically, we prove that Dugong, under mild conditions, can uniquely recover the unobserved accuracy and correlation parameters and use parameter sharing to improve sample complexity. Our method assigns clinician-validated labels to population-scale biomedical video repositories, helping outperform traditional supervision by 36.8 F1 points and addressing a key use case where machine learning has been severely limited by the lack of expert labeled data. On average, Dugong improves over traditional supervision by 16.0 F1 points and existing weak supervision approaches by 24.2 F1 points across several video and sensor classification tasks.
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards

While using shaped rewards can be beneficial when solving sparse reward tasks, their successful application often requires careful engineering and is problem specific. For instance, in tasks where the agent must achieve some goal state, simple distance-to-goal reward shaping often fails, as it renders learning vulnerable to local optima. We introduce a simple and effective model-free method to learn from shaped distance-to-goal rewards on tasks where success depends on reaching a goal state. Our method introduces an auxiliary distance-based reward based on pairs of rollouts to encourage diverse exploration. This approach effectively prevents learning dynamics from stabilizing around local optima induced by the naive distance-to-goal reward shaping and enables policies to efficiently solve sparse reward tasks. Our augmented objective does not require any additional reward engineering or domain expertise to implement and converges to the original sparse objective as the agent learns to solve the task. We demonstrate that our method successfully solves a variety of hard-exploration tasks (including maze navigation and 3D construction in a Minecraft environment), where naive distance-based reward shaping otherwise fails, and intrinsic curiosity and reward relabeling strategies exhibit poor performance.
Situation prediction of large-scale Internet of Things network security

The Internet of Things (IoT) is a new technology rapidly developed in various fields in recent years. With the continuous application of the IoT technology in production and life, the network security problem of IoT is increasingly prominent. In order to meet the challenges brought by the development of IoT technology, this paper focuses on network security situational awareness. The network security situation awareness is basic of IoT network security. Situation prediction of network security is a kind of time series forecasting problem in essence. So it is necessary to construct a modification function that is suitable for time series data to revise the kernel function of traditional support vector machine (SVM). An improved network security situation awareness model for IoT is proposed in this paper. The sequence kernel support vector machine is obtained and the particle swarm optimization (PSO) method is used to optimize related parameters. It proves that the method is feasible by collecting the boundary data of a university campus IoT network. Finally, a comparison with the PSO-SVM is made to prove the effectiveness of this method in improving the accuracy of network security situation prediction of IoT. The experimental results show that PSO-time series kernel support vector machine is better than the PSO-Gauss kernel support vector machine in network security situation prediction. The application of the Hadoop platform also enhances the efficiency of data processing.
CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction

In this paper, we describe our systems submitted to the Building Educational Applications (BEA) 2019 Shared Task (Bryant et al., 2019).We participated in all three tracks.Our models are NMT systems based on the Transformer model, which we improve by incorporating several enhancements: applying dropout to whole source and target words, weighting target subwords, averaging model checkpoints, and using the trained model iteratively for correcting the intermediate translations.The system in the Restricted Track is trained on the provided corpora with oversampled "cleaner" sentences and reaches 59.39 F0.5 score on the test set.The system in the Low-Resource Track is trained from Wikipedia revision histories and reaches 44.13 F0.5 score.Finally, we finetune the system from the Low-Resource Track on restricted data and achieve 64.55 F0.5 score, placing third in the Unrestricted Track.
Design and Evaluation of an Augmented Reality App for Learning Geometric Shapes in 3D

Augmented Reality (AR) is increasingly used as an educational tool in a range of domains with the goal of enhancing students’ performance as well as their learning experience, thanks to the interactivity and visual appeal of AR objects. While some attempts, albeit limited, have been undertaken to prove these beneficial effects in learning 3D geometry, the results remain inconclusive and there are some methodological issues such as under-evaluation of user experience. With the aim to enrich the applied body of knowledge on this specific topic, we developed an AR application that allows its users to learn about cross-sectional shapes and variables in 3D geometry. We compared the AR-based approach with the traditional chalk-and-board approach by involving sixty 12–16 year-olds from two schools. The AR class showed a significantly stronger learning effect than the traditional class, especially for the more complex geometric concepts. The AR class found the application engaging, regardless of their level of knowledge gain, which bore no significant relation with their intention to use it. The methodological challenge for implementing control groups and the practical challenge for affordable emerging educational tools should be tackled in future research.
IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding

In order to reach human performance on complex visual tasks, artificial systems need to incorporate a significant amount of understanding of the world in terms of macroscopic objects, movements, forces, etc. Inspired by work on intuitive physics in infants, we propose an evaluation benchmark which diagnoses how much a given system understands about physics by testing whether it can tell apart well matched videos of possible versus impossible events constructed with a game engine. The test requires systems to compute a physical plausibility score over an entire video. To prevent perceptual biases, the dataset is made of pixel matched quadruplets of videos, enforcing systems to focus on high level temporal dependencies between frames rather than pixel-level details. We then describe two Deep Neural Networks systems aimed at learning intuitive physics in an unsupervised way, using only physically possible videos. The systems are trained with a future semantic mask prediction objective and tested on the possible versus impossible discrimination task. The analysis of their results compared to human data gives novel insights in the potentials and limitations of next frame prediction architectures.
Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF

In this paper we tackle multilingual named entity recognition task.We use the BERT Language Model as embeddings with bidirectional recurrent network, attention, and NCRF on the top.We apply multilingual BERT only as embedder without any fine-tuning.We test out model on the dataset of the BSNLP shared task, which consists of texts in Bulgarian, Czech, Polish and Russian languages.
Automatic Detection of Fake Profiles in Online Social Networks

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.