Reviewerly/exHarmony huggingface dataset:
Medieval Spanish (12th–15th centuries) named entity recognition and attribute annotation system based on contextual information

Abstract The recognition of named entities in Spanish medieval texts presents great complexity, involving specific challenges: First, the complex morphosyntactic characteristics in proper‐noun use in medieval texts. Second, the lack of strict orthographic standards. Finally, diachronic and geographical variations in Spanish from the 12th to 15th century. In this period, named entities usually appear as complex text structure. For example, it was frequent to add nicknames and information about the persons role in society and geographic origin. To tackle this complexity, named entity recognition and classification system has been implemented. The system uses contextual cues based on semantics to detect entities and assign a type. Given the occurrence of entities with attached attributes, entity contexts are also parsed to determine entity‐type‐specific dependencies for these attributes. Moreover, it uses a variant generator to handle the diachronic evolution of Spanish medieval terms from a phonetic and morphosyntactic viewpoint. The tool iteratively enriches its proper lexica, dictionaries, and gazetteers. The system was evaluated on a corpus of over 3,000 manually annotated entities of different types and periods, obtaining F1 scores between 0.74 and 0.87. Attribute annotation was evaluated for a person and role name attributes with an overall F1 of 0.75.
Video Key Frame Monitoring Algorithm and Virtual Reality Display Based on Motion Vector

In this article, a motion vector-based video key frame detection algorithm is proposed to solve the problem of miss election and missing selection caused by the difficulty in detecting the moving target characteristics of the video key frame.Firstly, the entropy of adjacent frame difference and the two-dimensional entropy of image are introduced, and the combination of the two is taken as the measurement of the difference between video frames.Secondly, outliers are detected by statistical tools to obtain the lens boundary, thus realizing the adaptive lens detection of video content.Then, ViBe algorithm is used to detect the foreground object in the video sequence and extract the scale-invariant feature transformation features of the foreground moving object.Finally, the motion vector is introduced, and the sum of the block matching results is motion vector by partitioning the two adjacent frames and performing block matching.The magnitude of motion vector reflects the intensity of motion in the video, so active and inactive motion regions are obtained, and the similarity of video frames is calculated according to the defined formula, and key frames are extracted in these regions respectively.The experimental results show that the detection algorithm proposed in this article improves the video results with rich motion information obviously, and the objective indexes and subjective scores are improved to some extent, which improves the universality of the algorithm.In addition, this article also studies the display mode of key frame extraction results in virtual reality environment.The key frame display mode of video in virtual reality is optimized mainly by changing the display mode of information and changing the scene and testing the result of user task execution.
Bibliometric Analysis of the Application of Convolutional Neural Network in Computer Vision

This article analyzes the research progress in field of Convolutional Neural Networks (CNNs) using the bibliometric method. Literature samples of CNNs are analyzed by a basic statistic and co-citation network. Experimental results show that CNNs are being utilized in many computer vision applications, such as fault and image recognition diagnosis, seismic detection, positioning, and automatic detection of cracks and signals, image classification and image segmentation. In addition, there is systematic research on unbalanced problems in CNNs. Quantitative experimental research, extensive application fields, and market research informatization will be the three vital research tendencies in the future. The ideas and conclusions of this article provide insights to the academic research of CNNs and their practical application in the corporate world.
Enhancing Listening Skills Using Games

This study aims at finding out the most appropriate games for enhancing listening skills to students of the Faculty of the Economics Muhammadiyah University of Ponorogo. The process and the result of enhancing students’ listening skills of English were the main focuses of this study. Echeverría et al.’s model of classroom action research method was used to do this study. The technique consists of four stages, namely: (1) planning, (2) implementing, (3) observing, and (4) reflecting. The data was collected through the instruments were the listening tests, observation checklist, and questionnaires. The gained data were analyzed descriptively to know the process and the result of improvement caused by the application of each game. Based on the application of the games, the criteria of success were effectively reached in Cycle 2. 74% of the quantity of the students could obtain the scores more than 60, and 90% have assertive responses on the application of games. This means that using games is significantly useful to be used for enhancing the students’ listening skills.
Automated reconstruction of multiple objects with individual movement based on PSP

Many methods have been proposed to reconstruct the moving object based on phase shifting profilometry. Quality reconstruction results can be achieved when a single moving object or multiple objects with same movement are measured. However, errors will be introduced when multiple objects with individual movements are reconstructed. This paper proposes an automated method to track and reconstruct the multiple objects with individual movement. First, the objects are identified automatically and their bounding boxes are obtained. Second, with the identified objects’ images before movement, the objects are tracked by the KCF algorithm in the successive fringe pattern after movement. Third, the SIFT method is applied on the tracked object images and the objects’ movement is described individually by the rotation matrix and translation vector. Finally, the multiple objects are reconstructed based on the different movement information. Experiments are presented to verify the effectiveness.
Application of Cloud Model in Qualitative Forecasting for Stock Market Trends

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. The use of technical analysis for financial forecasting has been successfully employed by many researchers. The existing qualitative based methods developed based on fuzzy reasoning techniques cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. Extended fuzzy sets (e.g., fuzzy probabilistic set) study the fuzziness of the membership grade to a concept. The cloud model, based on probability measure space, automatically produces random membership grades of a concept through a cloud generator. In this paper, a cloud model-based approach was proposed to confirm accurate stock based on Japanese candlestick. By incorporating probability statistics and fuzzy set theories, the cloud model can aid the required transformation between the qualitative concepts and quantitative data. The degree of certainty associated with candlestick patterns can be calculated through repeated assessments by employing the normal cloud model. The hybrid weighting method comprising the fuzzy time series, and Heikin–Ashi candlestick was employed for determining the weights of the indicators in the multi-criteria decision-making process. Fuzzy membership functions are constructed by the cloud model to deal effectively with uncertainty and vagueness of the stock historical data with the aim to predict the next open, high, low, and close prices for the stock. The experimental results prove the feasibility and high forecasting accuracy of the proposed model.
A Systematic Approach to Agile Development in Highly Regulated Environments

Abstract For established domains within highly regulated environments, a systematic approach is needed to scale agile methods and assure compliance with regulatory requirements. The presented approach works adequately in small agile teams – independently of the underlying method such as Scrum, Kanban, etc. – and is scalable to more and bigger teams or even entire subsidiaries. It is based on a compliance and a quality risk dimension respectively. Both dimensions are needed to fit regulatory requirements in our finance example with more than 100 developers in one subsidiary.
Learning View and Target Invariant Visual Servoing for Navigation

The advances in deep reinforcement learning recently revived interest in data-driven learning based approaches to navigation. In this paper we propose to learn viewpoint invariant and target invariant visual servoing for local mobile robot navigation; given an initial view and the goal view or an image of a target, we train deep convolutional network controller to reach the desired goal. We present a new architecture for this task which rests on the ability of establishing correspondences between the initial and goal view and novel reward structure motivated by the traditional feedback control error. The advantage of the proposed model is that it does not require calibration and depth information and achieves robust visual servoing in a variety of environments and targets without any parameter fine tuning. We present comprehensive evaluation of the approach and comparison with other deep learning architectures as well as classical visual servoing methods in visually realistic simulation environment [1]. The presented model overcomes the brittleness of classical visual servoing based methods and achieves significantly higher generalization capability compared to the previous learning approaches.
Unifying Top–Down Views by Task-Specific Domain Adaptation

In this article, we aim to learn a unified representation of images from satellite/aerial/ground views by exploring their underlying correlations. Inspired by recent advances in domain adaptation (DA), we propose a novel task-specific DA method for this purpose. Different from traditional DA methods, this proposed method not only applies task-specific classifiers <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> but also introduces domain-specific tasks for different domains during the adaptation process. The experiments are conducted on two newly proposed ground-/satellite-to-aerial scene adaptation (GSSA) data sets. Since the semantic gap between the ground/satellite scenes and the aerial scenes is much larger than that between ground scenes, the DA task between these scenes is more challenging than traditional DA tasks. On GSSA data sets, we not only demonstrate the proposed unsupervised DA method but also explore the few-shot DA in the discussion section. The proposed method is easy to implement, and our method substantially outperforms the state-of-the-art methods on the studied data sets.We hope that the proposed method for the novel GSSA data sets can be a good baseline for future researchers. The related data sets/codes will be available online.
Sensor Failure Recovery using Multi Look-back LSTM Algorithm in Industrial Internet of Things

In this paper, an algorithm to recover failure sensor data in real-time is proposed. In the current factory automation trends, each device senses the information directly, where humans are no longer involved in huge part of the task. The ability of the system to detect the failure in the device is one of the crucial issues as well as the ability to recover the data of the device when failure happens. The deep learning technique is proposed to learn the behavior of the sensor and to provide exact data as a recovery when the failure occurred. This scenario can be applied along with a faulty detection technique to provide seamless solutions in the current industrial environment.
A Super Resolution Method for Remote Sensing Images Based on Cascaded Conditional Wasserstein GANs

High-resolution (HR) remote sensing imagery is quite beneficial for subsequent interpretation. Obtaining HR images can be achieved by upgrading the imaging device. Yet, the cost to perform this task is very huge. Thus, it is necessary to obtain HR images from low-resolution (LR) ones. In the literature, the super-resolution image reconstruction methods based on deep learning have unparalleled advantages in comparison to traditional reconstruction methods. This work is inspired by these current mainstream methods and proposes a novel cascaded conditional Wasserstein generative adversarial network (CCWGAN) architecture with the residual dense block to generate high quality remote sensing images. We validate the proposed method on the NWPU VHR-10 dataset. Experimental results show our CCWGAN method has superior performance compared with the state-of-the-art GAN methods.
Two Routes to Scalable Credit Assignment without Weight Symmetry

The neural plausibility of backpropagation has long been disputed, primarily for its use of non-local transport $-$ the biologically dubious requirement that one neuron instantaneously measure the synaptic weights of another. Until recently, attempts to create local learning rules that avoid transport have typically failed in the large-scale learning scenarios where backpropagation shines, e.g. ImageNet categorization with deep convolutional networks. Here, we investigate a recently proposed local learning rule that yields competitive performance with backpropagation and find that it is highly sensitive to metaparameter choices, requiring laborious tuning that does not transfer across network architecture. Our analysis indicates the underlying mathematical reason for this instability, allowing us to identify a more robust local learning rule that better transfers without metaparameter tuning. Nonetheless, we find a performance and stability gap between this local rule and backpropagation that widens with increasing model depth. We then investigate several non-local learning rules that relax the need for instantaneous transport into a more biologically-plausible weight estimation process, showing that these rules match state-of-the-art performance on deep networks and operate effectively in the presence of noisy updates. Taken together, our results suggest two routes towards the discovery of neural implementations for credit assignment without symmetry: further improvement of local rules so that they perform consistently across architectures and the identification of biological implementations for non-local learning mechanisms.
Responding to the 2020 Magna, Utah, Earthquake Sequence during the COVID-19 Pandemic Shutdown

Abstract Two days after the University of Utah Seismograph Stations (UUSS) staff were required to leave campus and work remotely, an Mw 5.7 earthquake struck the Salt Lake Valley near the town of Magna, Utah. This event was the largest instrumentally recorded earthquake in the Salt Lake Valley and the largest earthquake ever felt by most residents. The timing of this event—at the start of a lockdown in response to the COVID-19 pandemic—made the UUSS response to this earthquake an extra challenge. Other factors such as a toxic plume caused by the ground shaking, inclement weather, and a mountain lion also impacted the work. The response tested the continuity of operations plan that had been in place since 2007, response protocols, and communications with partners and the public. Overall, the UUSS earthquake response was successful: A valuable and arguably unprecedented dataset of strong ground motions from normal faulting was generated, magnitudes and locations of thousands of earthquakes were shared in a timely fashion, unfounded rumors and general questions were promptly responded to via traditional and social media, and initial scientific results were submitted for publication.
The Effect of Reading Interest and Achievement Motivation on Students’ Discourse Analysis Competence

Discourse analysis involves analyzing the language being used. This research investigates the effect of reading interest and achievement motivation on students’ discourse analysis competence. To obtain the required data, questionnaires on reading interest and achievement motivation, and discourse analysis were used to test the aspects of cohesion and coherence and were administered to a group of 60 students of the English language study program at Bina Darma University in Palembang. Students’ reading interest and achievement motivation are some of the most crucial issues in the field of education. To analyze the data, path analysis was used and the results showed a positive direct effect on (1) reading interest of students’ discourse analysis competence with a coefficient value 0.253, and (2) achievement motivation on students’ discourse analysis competence with a coefficient value 0.284. The magnitude of the direct effects of reading interest and achievement motivation on students’ discourse analysis was 44.6%.
Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation

Transfer learning from high-resource languages is known to be an efficient way to improve end-to-end automatic speech recognition (ASR) for low-resource languages. Pre-trained or jointly trained encoder-decoder models, however, do not share the language modeling (decoder) for the same language, which is likely to be inefficient for distant target languages. We introduce speech-to-text translation (ST) as an auxiliary task to incorporate additional knowledge of the target language and enable transferring from that target language. Specifically, we first translate high-resource ASR transcripts into a target low-resource language, with which a ST model is trained. Both ST and target ASR share the same attention-based encoder-decoder architecture and vocabulary. The former task then provides a fully pre-trained model for the latter, bringing up to 24.6% word error rate (WER) reduction to the baseline (direct transfer from high-resource ASR). We show that training ST with human translations is not necessary. ST trained with machine translation (MT) pseudo-labels brings consistent gains. It can even outperform those using human labels when transferred to target ASR by leveraging only 500K MT examples. Even with pseudo-labels from low-resource MT (200K examples), ST-enhanced transfer brings up to 8.9% WER reduction to direct transfer.
Neural Architecture Search in Graph Neural Networks

Performing analytical tasks over graph data has become increasingly interesting due to the ubiquity and large availability of relational information. However, unlike images or sentences, there is no notion of sequence in networks. Nodes (and edges) follow no absolute order, and it is hard for traditional machine learning (ML) algorithms to recognize a pattern and generalize their predictions on this type of data. Graph Neural Networks (GNN) successfully tackled this problem. They became popular after the generalization of the convolution concept to the graph domain. However, they possess a large number of hyper-parameters and their design and optimization is currently hand-made, based on heuristics or empirical intuition. Neural Architecture Search (NAS) methods appear as an interesting solution to this problem. In this direction, this paper compares two NAS methods for optimizing GNN: one based on reinforcement learning and a second based on evolutionary algorithms. Results consider 7 datasets over two search spaces and show that both methods obtain similar accuracies to a random search, raising the question of how many of the search space dimensions are actually relevant to the problem.
Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning From Unlabeled Videos and 3D-Shape Retrieval

Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs. However, data of this kind is not always available since detailed keypoint correspondences can be hard to establish. On the other hand, we can often obtain labels for pairs of keypoint bags. For example, keypoint bags extracted from two images of the same object under different views form a matching pair, and keypoint bags extracted from images of different objects form a non-matching pair. On average, matching pairs should contain more corresponding keypoints than non-matching pairs. We describe an end-to-end differentiable architecture that enables the learning of local keypoint descriptors from such weakly-labeled data. Additionally, we discuss how to improve the method by incorporating the procedure of mining hard negatives. We also show how can our approach be used to learn convolutional features from unlabeled video signals and 3D models. Our implementation is available at https://github.com/nenadmarkus/wlrn
An information theoretic view on selecting linguistic probes

There is increasing interest in assessing the linguistic knowledge encoded in neural representations.A popular approach is to attach a diagnostic classifier -or "probe" -to perform supervised classification from internal representations.However, how to select a good probe is in debate.Hewitt and Liang (2019) showed that a high performance on diagnostic classification itself is insufficient, because it can be attributed to either "the representation being rich in knowledge", or "the probe learning the task", which Pimentel et al. ( 2020) challenged.We show this dichotomy is valid informationtheoretically.In addition, we find that the methods to construct and select good probes proposed by the two papers, control task (Hewitt and Liang, 2019) and control function (Pimentel et al., 2020), are equivalent -the errors of their approaches are identical (modulo irrelevant terms).Empirically, these two selection criteria lead to results that highly agree with each other.