Reviewerly/exHarmony huggingface dataset:
Heterogeneous Network Based Semi-supervised Learning for Scene Text Recognition

Scene text recognition is an important research topic in both academia and industry. Remarkable progress has been achieved by recent works, which are based on abundant labeled data for model training. Obtaining text images is a relatively easy process, but labeling them is quite expensive. To alleviate the dependence on labeled data, semi-supervised learning which combines labeled and unlabeled data seems to be a reasonable solution, and is proved to be effective for image classification. However, due to the apparent differences between scene text recognition and image classification, there are very few related works which incorporate semi-supervised learning with scene text recognition. To the best of our knowledge, this paper is the first time to propose a semi-supervised learning method for scene text recognition. In our framework, given a text image, two heterogeneous networks are adopted to produce independent predictions from different perspectives, namely, context information and visual representation. Then, a discriminator is introduced for pseudo-labeling and instance selection using the predictions. With these two modules, the generalization performance and the precision of pseudo-labeling are improved. Comprehensive experiments show that our framework leverages the unlabeled data effectively, and brings consistent improvements with different unlabeled data scales on both English and Chinese datasets.
EFFECT OF ZOOM MEETING ON E-LEARNING IN STUDENT SPEAKING SKILL

This research aims to find out the effect of zoom meeting on e-learning in student speaking skill at ninth grade of SMPN 35 Bekasi. The use of zoom meetings supports the learning process in the covid-19 pandemic. This research was conducted in July 2021. The analysis used in quantitative research is experimental design. This research sample is 40 students in grade ninth with divided into two groups, namely a control group with a total of 20 students and an experiment group with a total of 20 students. Data collection is carried out with a test technique system to get the results of students' speaking skills. Statistical test using SPSS 22. The results showed that there was a difference between the control group and the experiment group. This can be proven by; 1. Instrument test results with a reliability level of 0.800 which means that the results are valid. 2. 2. the test result t is a significance score (2-tailed) from the test result of 0.00 &lt; 0.05 so that a sig score of 0.00 is seen to show the effect of using zoom meetings in speech ability in SMPN 35 Bekasi.
Machine Learning in Discriminating Active Volcanoes of the Hellenic Volcanic Arc

Identifying the provenance of volcanic rocks can be essential for improving geological maps in the field of geology and providing a tool for the geochemical fingerprinting of ancient artifacts like millstones and anchors in the field of geoarchaeology. This study examines a new approach to this problem by using machine learning algorithms (MLAs). In order to discriminate the four active volcanic regions of the Hellenic Volcanic Arc (HVA) in Southern Greece, MLAs were trained with geochemical data of major elements, acquired from the GEOROC database, of the volcanic rocks of the Hellenic Volcanic Arc (HVA). Ten MLAs were trained with six variations of the same dataset of volcanic rock samples originating from the HVA. The experiments revealed that the Extreme Gradient Boost model achieved the best performance, reaching 93.07% accuracy. The model developed in the framework of this research was used to implement a cloud-based application which is publicly accessible at This application can be used to predict the provenance of a volcanic rock sample, within the area of the HVA, based on its geochemical composition, easily obtained by using the X-ray fluorescence (XRF) technique.
Dual-containing Alternant Codes for Applications in the Calderbank-Shor-Steane Construction

Dual-containing classical error-correcting codes can be used in the Calderbank-Shor-Steane (CSS) construction for quantum error-correcting codes (QECCs). Dual-containing Bose-Chaudhuri-Hocquenghem (BCH) codes have been extensively studied in this regard. Research on the more general alternant codes is far less complete, despite the perspective of finding superior codes among them. In this paper, conditions for an alternant code to contain its Euclidean dual code are derived. The necessity of having dual-containing generalized Reed-Solomon (GRS) codes is shown and the structure of such codes is studied. An approach for constructing dual-containing alternant codes from BCH codes is proposed. In addition, a method for finding GRS codes over a given finite field with dual-containing alternant subfield-subcodes is presented. This method encompasses dual-containing BCH codes as a special case.
Feedback of Rotational Sensation Experienced by Body for Immersive Telepresence

In our previous study, we proposed a telepresence system that can transfer the riding sensation of a vehicle (Segway) for assisting collaborative task. The system could provide a local expert who remotely attend the task not only the view of a remote environment that is captured by a camera but also the vestibular perception during the movement of the camera. In this study, we examined the rotation feedback by the rotary seat when the camera is rotated. The measured intensity adjustment showed that the angular acceleration of the rotary seat was about half that of the camera rotation. Further, the result of the simulator sickness questionnaire scores showed that the inphase rotation of the seat with the camera is appropriate for suppressing virtual reality sickness, indicating that the requirement of vestibular intensity is quite low compared with the visual cue showed on the head mounted display, which allows a designer to develop a sensation feedback device that has an actuator of low strength.
Technology Inclusion via Endangered Language Learning Application: An Explorative Case Study with Remote Nganasan Community

Digital learning, although extremely popular with tech-savvy users, is still not widely studied in the context of remote communities for endangered language learning. To support technology inclusion, it is also imperative to motivate and engage users for long-term use. We have developed a gamified storyline application for a moribund language, Nganasan (Samoyedic branch of the Uralic language family), and conducted a remote user study, where three teachers autonomously utilized the application at a local school in the Taymyr Peninsula (Northern Siberia, Russia) with 22 children, aged from 7 to 12 years. The results indicate that the application was fun, engaging, and appealing for most children, whereas teachers demonstrated the desire to use the application further and contribute to its development. The analysis of usage patterns, obtained from the three tablets sent to the school, suggests that cooperative work was motivating and engaging for the children from the remote community in Taymyr. We further discuss the value, drawbacks, and learned lessons that can be useful for future research on digital learning and technology inclusion with remote communities.
A full-body 3D reconstruction using planar mirrors

Structured light sensing system can be used to obtain a high-precision 3D model of an object. In this paper, we present a mirror-based structured light sensing system for acquiring complete 3D surface models. Our system can obtain three point clouds by using planar mirror and register them together. First, we describe the mathematical model of our system, and propose a method to register the point clouds gained by our system. Finally, we evaluate the feasibility and accuracy of our mirror-based structured light sensing system by extensive experiments. The result shows that our system can reconstruct a full-body 3D model of the object successfully.
Enhancing learning-based computer vision algorithms accuracy in sUAS using navigation wide-angle cameras

The new generation of sUAS (small Unmanned Aircraft Systems) aims to extend the range of scenarios in which sense-and-avoid functionality and autonomous operation can be used. Relying on navigation cameras, having a wide field of view can increase the coverage of the drone surroundings, allowing ideal fly path, optimal dynamic route planning and full situational awareness. The first part of this paper will discuss the trade-off space for camera hardware solution to improve vision performance. Severe constraints on size and weight, a situation common to all sUAS components, compete with low-light capabilities and pixel resolution. The second part will explore the benefits and impacts of specific wide-angle lens designs and of wide-angle images rectification (dewarping) on deep-learning methods. We show that distortion can be used to bring more information from the scene and how this extra information can increase the accuracy of learning-based computer vision algorithm. Finally, we present a study that aims at estimating the link between optical design criteria degradation (MTF) and neural network accuracy in the context of wide-angle lens, showing that higher MTF is not always linked to better results, thus helping to set better design targets for navigation lenses.
SDP Methods for Sensitivity-Constrained Privacy Funnel and Information Bottleneck Problems

We generalize the information bottleneck (IB) and privacy funnel (PF) problems by introducing the notion of a sensitive attribute, which arises in a growing number of applications. In this generalization, we seek to construct representations of observations that are maximally (or minimally) informative about a target variable, while also satisfying constraints with respect to a variable corresponding to the sensitive attribute. In the Gaussian and discrete settings, we show that by suitably approximating the Kullback-Liebler (KL) divergence defining traditional Shannon mutual information, the generalized IB and PF problems can be formulated as semi-definite programs (SDPs), and thus efficiently solved, which is important in applications of high-dimensional inference. We validate our algorithms on synthetic data and demonstrate their use in imposing fairness in machine learning on real data as an illustrative application.
Poker: visual instrumentation of reactive programs with programmable probes

This paper presents Poker, a visual instrumentation platform for reactive programs. Similar to other platforms, Poker features a visual dashboard that allows the programmer to inspect the flow of values through the reactive program. The novelty of Poker is that: (a) It features a canvas of so-called probes that can be dynamically wired into a running reactive program in order to instrument the running system. (b) In addition to focusing on the values flowing through the program, a probe can measure a particular property about the way these values flow through the instrumented program. (c) The set of probes is open because a probe is programmed in the same language as the instrumented program.
A Node Preference-Aware Delegated Proof of Stake Consensus Algorithm With Reward and Punishment Mechanism

Blockchain is a distributed data structure that ensures data security and reliability, and has been widely used in different application scenarios. As the core part of blockchain, consensus algorithm has become the bottleneck of blockchain development, which directly determines the performance of blockchain system. Therefore, considering the preference of nodes, we propose a node preference-aware delegated proof of stake consensus algorithm with reward and punishment mechanism (NPRP-DPoS) to improve the reputation of witness nodes. NPRP-DPoS designs a reward and punishment mechanism to reward or punish generators and voters, and constructs a reputation mechanism to motivate the positive behavior of nodes. At the same time, NPRP-DPoS uses Borda count to select the witness nodes which are more in line with the preference of the voting nodes. The experimental results shows that NPRP-DPoS algorithm can quickly eliminate exception nodes and motivate nodes to vote effectively. Also, NPRP-DPoS algorithm makes the election more fair and reasonable, ensures the interests of nodes and enhances the security of system.
Contextual and Behavior Factors Extraction from Pedestrian Encounter Scenes Using Deep Language Models

This study introduces an NLP framework including deep language models to automate the contextual and behavior factors extraction from a narrative text that describes the environment and pedestrian behaviors at the pedestrian encounter scenes. The performance is compared against a baseline BiLSTM-CRF model trained for each factor separately. The evaluation results show that the proposed NLP framework outperforms the baseline model. We show that the proposed framework can successfully extract nested, overlapping, and flat factors from sentences through the case studies. This model can also be applied to other descriptions when physical context and human behaviors need to be extracted from the narrative content to understand the behavioral interaction between subjects further.
Dual Attention Network Based on Knowledge Graph for News Recommendation

With the continuous development of Internet technology, there are more and more online news readers, and the amount of news is huge and growing explosively. In addition, the news language is highly concentrated and contains a large number of entities. Currently, the commonly used recommendation methods are difficult to make full use of knowledge information and discover the potential interests of users. To solve the above problems, we propose a Dual attention network based on knowledge graph for news recommendation (DAKRec), which takes news titles, the entities contained in them and the contexts as input, and uses knowledge graph to extract news features. In order to better characterize the diversity of users’ interests, a dual attention network is constructed to obtain the weight of users’ historical news through word-level attention mechanism and item-level attention mechanism (integrating news words, entities, and contexts). Finally, the multi-head attention module is used to connect historical news and candidate news, and the click-through rate is calculated through a fully connected multilayer perceptron after feature fusion. Through a large number of experiments, we prove that our model DAKRec is better than the advanced DKN model and the other comparison models (FM, DMF) in AUC and MSE, further improves the recommendation performance.
Evaluating Multiple Guesses by an Adversary via a Tunable Loss Function

We consider a problem of guessing, wherein an adversary is interested in knowing the value of the realization of a discrete random variable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> on observing another correlated random variable Y. The adversary can make multiple (say, k) guesses. The adversary's guessing strategy is assumed to minimize a-loss, a class of tunable loss functions parameterized by a. It has been shown before that this loss function captures well known loss functions including the exponential loss (a = 1/2), the log-loss (a = 1) and the 0–1 loss (a = ∞). We completely characterize the optimal adversarial strategy and the resulting expected α-loss, thereby recovering known results for a = ∞. We define an information leakage measure from the k-guesses setup and derive a condition under which the leakage is unchanged from a single guess.
Towards a Micron-level Line Structured Light

Line structured light is a classic and popular approach for 3D object measurement. However, in many prior works, it is only evaluated on the application level, and researchers may ignore comparisons between measured values and highly accurate labels. The calibration accuracy may also not be the primary factor for specific high-precision measurement tasks: the calibration method is sophisticated and only responsible for determining fixed transformations between the camera and the laser plane. In this paper, we propose a semantic centroid method to extract laser lines in various gray values accurately, and several other factors are analyzed when measuring object thicknesses. On a customized dataset sampled from gauge blocks with ground-truth thicknesses, under strict conditions, our line structured light obtains measurement errors at the micron level.
Conditional Quantization for SHVC Quality Scalability of Depth Maps

3D-High Efficiency Video Coding, the latest standard for depth maps compression, is adapted to point-to-point applications where the transmission chain consists of one transmitter and receiver. However, a scalable coding is solicited for multipoint applications where the data source is unique, but serves heterogeneous receivers through different transmission supports. The Scalable High Efficiency Video Coding (SHVC), the latest standard for scalable compression of texture images, can provide the scalability asset for depth maps compression. However, this is at the expense of the depth discontinuities quality. In this paper, we propose a quality scalable compression method adapted to the depth maps context. Besides the enhancement layers that ameliorate the base layer quality in SHVC, we use an importance map to conditionally quantize the transform coefficients of the coding units according to the sub_enhancement layers they include. Our experiments show that our approach allows higher Rate-Distortion trade-offs, compared to the unmodified SHVC.
Image Retrieval by Hierarchy-aware Deep Hashing Based on Multi-task Learning

Deep hashing has been widely used to approximate nearest-neighbor search for image retrieval tasks. Most of them are trained with image-label pairs without any inter-label relationship, which may not make full use of the real-world data. This paper presents deep hashing, named HA2SH, that leverages multiple types of labels with hierarchical structures that an ethnological museum assigns to their artifacts. We experimentally prove that HA2SH can learn to generate hashes that give a better retrieval performance. Our code is available at https://github.com/wbw520/minpaku.
Computational Bounds for Doing Harmonic Analysis on Permutation Modules of Finite Groups

We develop an approach to finding upper bounds for the number of arithmetic operations necessary for doing harmonic analysis on permutation modules of finite groups. The approach takes advantage of the intrinsic orbital structure of permutation modules, and it uses the multiplicities of irreducible submodules within individual orbital spaces to express the resulting computational bounds. We conclude by showing that these bounds are surprisingly small when dealing with certain permutation modules arising from the action of the symmetric group on tabloids.
A Comprehensive Study on Attention-Based NER

Named Entity Recognition (NER) is a part of extraction and is used for Natural Language Processing (NLP). NER system helps us to find various names from unstructured text or a text file and classifies them into various categories. The attention-based keyword extracting concept has been established to solve the problem of detecting redundant data and inessential data and does not consider them. Researchers are highly concerned about attention mechanisms. In this study, we focus on the most recent algorithms which are trained with the attention-based mechanism for NER. We briefly describe attention-based models, objectives of these models, datasets used in each method, and efficiency. Our focus is to give some decisions on which model is exceptionally efficient depending on the dataset and NER category.
Protection Against Online Fraud Using Blockchain

In this paper we present an application that, by using the blockchain, tries to inform the user about possible fraud attempts on the internet. The application presented is intended to prevent the user from landing on websites that have recently been the target of various attacks. Installed as a browser plugin it should alert the user upon visiting such a website. Alerts are triggered once a generated hash of the website no longer matches a hash that was stored on the blockchain. Included inside the hash is not only the HTML but also further website information such as used JavaScript and Cascading Style Sheets. By implementing a package into the build process the developer can easily publish the website hashes that are then stored on the blockchain for comparison. To see if our plugin can withstand common website attacks we compared it with the OWASP top 10 risks and check if an attack is mitigated or completely solved. We came to the conclusion that attacks directly targeting the front end of the website can be mitigated. Not included are attack vectors that directly target web servers and the data stored on them.
Synthesised Optimal Control for a Robotic Group by Complete Binary Genetic Programming

The paper continues the study of symbolic regression methods for control learning. The optimal control problem with phase constraints for a group of robots is considered. To solve the problem, the method of synthesized optimal control is used. At the first stage the stabilization problem is solved for each robot. Using a new hybrid evolutionary algorithm, built on the basis of the genetic algorithm, the particle swarm optimization and the gray wolf optimizer, stable equilibrium points are found. Next, the original optimization problem by piece-wise linear approximation of the equilibrium points is solved. In contrast to the known methods for solving the synthesis problem, the control learning by the complete binary genetic programming is used. The advantage of this approach is that the resulting control is realizable on board of mobile robots. Simulation is given for a group of two mobile robots.
A Modified Multiplicative Intrinsic Component and Local Intensity Inhomogeneity for Brain Image Segmentation

Intensity inhomogeneity is common in medical images and inevitably leads to many challenges for accurate image segmentation. Image segmentation is a foundation step for computer vision and computing. This paper proposes an energy minimization method based on fuzzy C-means (FCM), which combines global with local clustering method for bias field estimation and segmentation of magnetic resonance (MR) brain images. The proposed method takes full advantage of the decomposition of MR images into two multiplicative components as the global clustering term, which uses the true image that characterizes a physical property of the tissues and the bias field that accounts for the intensity inhomogeneity, and their respective spatial properties. The decomposition of MR images describes the varies of bias field of the whole image, where the details of some deep changes of tissue boundary may lose. And the local clustering term which uses varies of bias field in local regions of image for the proposed method can well deal with the deep change of intensity between different tissues. Because of there is a lack of global control for distribution of bias field in local clustering method, we use the advantages of global and local clustering, and consider the combination of them. In the proposed method, bias field estimation and tissue segmentation are simultaneously achieved by an energy minimization process. The energy minimization is iteratively optimized by FCM. Comparison experiments on some real and synthetic images with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy.