Reviewerly/exHarmony huggingface dataset:
Iterative Strategy for Named Entity Recognition with Imperfect Annotations

Named entity recognition (NER) systems have been widely researched and applied for decades. Most NER systems rely on high quality annotations, but in some specific domains, annotated data is usually imperfect, typically including incomplete annotations and non-annotations. Although related studies have achieved good results on specific types of annotations, to build a more robust NER system, it is necessary to consider complex scenarios that simultaneously contain complete annotations, incomplete annotations, non-annotations, etc. In this paper, we propose a novel NER system, which could use different strategies to process different types of annotations, rather than simply adopts the same strategy. Specifically, we perform multiple iterations. In each iteration, we first train the model based on incomplete annotations, and then use the model to re-annotate imperfect annotations and update their weights, which could generate and filter out high quality annotations. In addition, we fine-tune models through high quality annotations and its augmentations, and finally integrate multiple models to generate reliable prediction results. Comprehensive experiments are conducted to demonstrate the effectiveness of our system. Moreover, the system is ranked first and second respectively in two leaderboards of NLPCC 2020 Shared Task: Auto Information Extraction (https://github.com/ZhuiyiTechnology/AutoIE).
Orgatronics: A Physically Interactive Videogame for Learning Biology Concepts Using IoT Technologies

New forms of learning, such as game-based learning, enable students to have a more meaningful educational experience and this concept, applied alongside that of new technologies such as the Internet of Things, offers new opportunities to improve teaching in the field of education. In this work, we analyze the initiatives already implemented within these two concepts and present the Orgatronics project, a videogame combining Game-Based Learning with IoT technologies for learning in the field of biology. Our proposal seeks to contribute to the exploration of these topics with the aim of innovating in learning by means of these technologies..
Proxemic Environments: A Framework for Developing Mobile Applications based on Proxemic Interactions

The widespread diffusion of smart and mobile devices continuously connected to the Internet has facilitated the users' contact and interaction with other people, devices, and with their physical surroundings.Proxemic interaction, derived from proxemics theory, focuses on how Human-Computer Interaction (HCI) works with smart devices, using the five proxemic dimensions: Distance, Identity, Location, Movement, and Orientation (DILMO).The current tools for developing proxemic applications require fixed devices that make it difficult to build proxemic mobile apps.In this work, we propose a framework to manage all components in a proxemic environment (i.e., interaction objects and DILMO dimensions that govern the HCI).We demonstrate and evaluate the effectiveness and suitability of our framework, through the development of two proxemic mobile applications, as proof-of-concept.
A Literature Review in Support of Blockchain Technologies

Blockchain technology has to be one of the biggest innovations of the 21st century given the ripple effect it is having on various sectors, from financial to manufacturing as well as education. Unknown to many is that Blockchain history dates back to the early 1990s. Since its popularity started growing a few years back, a number of applications have cropped up all but underlining the kind of impact it is destined to have as the race for digital economies heats up. This chapter examines the literature on Blockchain technologies and potential benefits of Blockchain technologies wherever they are used. This research is being carried out because there is no literature review on Blockchain Technology and this chapter will ease the research of the scholars who want to know about Blockchain Technology. This research is important because it will make people aware about Blockchain and where all it is used and how people can benefit from this technology. Search terms identified more than 30 papers in support of blockchain technologies. The methodology used to carry out the research is also discussed in this report such as various databases searched using various search terms. From the papers identified, criteria were determined to identify further appropriate articles. Data analysis verified the purpose of blockchain, categories of blockchain and alternatives of blockchain. The findings revealed that blockchain is one of the leading technologies in the world currently with a more secure platform for carrying any task.
Generic Object Tracking with NVIDIA Jetson Nano Using Siamese Convolutional Neural Networks

In this thesis, a generic object tracker was constructed that was applied to both a commonly used tracking dataset using a regular computer as well as a robot powered by a small NVIDIA computer. The architecture of the tracker consisted of two parallel convolutional neural networks convolving to a single output. The input consisted of two separate cropped images that were fed into the networks separately. The images depicted an object from an image sequence at time t and t + 1, both centered at the object at time t. The purpose of the network is then to compare the two images and output coordinates for the object’s position at time t + 1.The tracker was successful in following several objects from a commonly used visual object tracking dataset but performed inconsistently for different scenarios based on its training time. The size of the tracker became a problem when applying it to the robot, requiring significant size reduction. This had a negative effect on the trackers’ performance. The tracker managed to track at up to 60 fps when used on the computer but only around 10 fps for the robot. It’s likely that the tracking performance and speed of the robot can be improved significantly by optimizing the trackers neural network structure aswell as adjusting its training duration. (Less)
Stroke-speech: A Multi-channel Input Method for Chinese Characters

Text input has always been one of the key areas of human-computer interaction research. At present, most of the mainstream input methods are single-channel methods, such as speech input and keyboard input, but single-channel input methods have disadvantages of slow speed and low accuracy. This paper proposes a multi-channel input method combining speech input and stroke input, which supports both single character input and continuous input of a whole sentence. Experimental results show that Stroke-speech significantly improves the accuracy of single character and two-word words input and also has a promising prospect in the correction of wrong words in the whole sentence input.
A New Semantic-based Multi-Level Classification Approach for Activity Recognition Using Smartphones

In this paper, we address the problem of recognizing the semantic human activities through the analysis of large dataset collected from users’ sensor-based smartphones. Our approach is unique in terms of covering a large number of activities that users could possibly engage in, and considering the multi-level-based classification model. Our model has three properties that never seemed to be addressed by existing approaches dealing with the same problem. These are: (1) comprehensiveness — in terms of the activity set, (2) accuracy — in terms of the activity classification, and (3) applicability — in terms of flexibility in being applied in real-life settings. Current approaches do not tackle all these properties. When tested on realistic dataset, our multi-level-based model achieved promising results despite the large number of activities being considered. When compared to similar approaches, our approach achieved comparable results in terms of accuracy and outperformed them in terms of the activity types, environment and settings covered, comprehensiveness, and applicability.
Providing SLOs for Resource-Harvesting VMs in Cloud Platforms

Cloud providers rent the resources they do not allocate as evictable virtual machines (VMs), like spot instances. In this paper, we first characterize the unallocated resources in Microsoft Azure, and show that they are plenty but may vary widely over time and across servers. Based on the characterization, we propose a new class of VM, called Harvest VM, to harvest and monetize the unallocated resources. A Harvest VM is more flexible and efficient than a spot instance, because it grows and shrinks according to the amount of unallocated resources at its underlying server; it is only evicted/killed when the provider needs its minimum set of resources. Next, we create models that predict the availability of the unallocated resources for Harvest VM deployments. Based on these predictions, we provide Service Level Objectives (SLOs) for the survival rate (e.g., 65% of the Harvest VMs will survive more than a week) and the average number of cores that can be harvested. Our short-term predictions have an average error under 2% and less than 6% for longer terms. We also extend a popular cluster scheduling framework to leverage the harvested resources. Using our SLOs and framework, we can offset the rare evictions with extra harvested cores and achieve the same computational power as regular-priority VMs, but at 91% lower cost. Finally, we outline lessons and results from running Harvest VMs and our framework in production.
Towards the Development of 3D Engine Assembly SimulationLearningModulefor Senior High School

The focus of the study is to develop a 3D engine assembly simulation learning module to address the lack of equipment in one senior high school in the Philippines. The study used mixed-method to determine the considerations needed in developing an application for educational use particularly among laboratory/practical subjects like engine assembly. The study used ISO 25010 quality standards in evaluating the application(n=153 students and 3 ICT experts).Results showed that the application is moderately acceptable(overall mean = 3.52) under ISO 25010 quality standards. The study created an engine simulation learning assembly in which teachers can use to augment their lesson. The study also highlights the applicability of using 3D-related technologies for practical and laboratory subjects particularly highly technical-related subjects. Future studies may develop a similar application in the same context using mobile and other emerging technology(i.e., Virtual Reality, Augmented Reality) as well as making the content more customizable. Effectivity of the system in an actual setting is also worth pursuing. The study highlighted the potential use of 3D technology in a classroom setting.
Using SPARK to Ensure System to Software Integrity

This paper describes work in progress on a workflow that supports consistent property-preservation proofs from early stages of system requirements specifications down to software requirements and final implementation. This workflow, called System-to-Software Integrity (SSI), demonstrates that the implemented software satisfies constraints defined in system requirements.
Evaluasi Pembangunan Sistem Pakar Penyakit Tanaman Sawit dengan Metode Deep Neural Network (DNN)

The limited knowledge of oil palm farmers on oil palm pests and diseases is related to oil palm productivity. Jambi Province is one of the largest oil palm producers on the island of Sumatra. Usually, to find out the types of pests and diseases in oil palm in the field, farmers need knowledge like that of experts about oil palm diseases. However, the limitation of facilities and capabilities becomes an obstacle. This study offers an expert system to analyze oil palm disease using deep learning. This method is deep learning with excellent accuracy. Various recent studies using DNN state that the classification accuracy results are very good. The data used for the expert system using the DNN algorithm comes from oil palm diagnostic data from the Jambi Provincial Plantation Office. After the oil palm disease diagnosis data is trained, the training data model will be stored for the oil palm disease diagnosis testing process. With a total of 11 classes (Leaf Spot Disease, Anthrox Leaf Blight, Leaf Rust Disease, Leaf Canopy Disease, Bud Rot Disease, Root Rot Disease, Fire Caterpillar or Setora Nitens, Red Mites or Oligonychus, Horn Beetle or Orycte rhinoceros, Bunch Borer Fruits and Nematodes Rhadinaphelenchus Cocophilus), with test variables including the number of classes, TP, TN, FP, FN, precision, recall, F1-score, accuracy, and Missclassificaion rate. The highest accuracy value was 0.88, while the lowest value was 0.83 and the average accuracy was 0.86. This shows that the results of expert system diagnosis on oil palm disease data with DNN are quite good.
A Cohort of SARS-CoV-2 Infected Asymptomatic and Pre-Symptomatic Contacts from COVID-19 Contact Tracing in Hubei Province, China: Short-Term Outcomes

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Hierarchical Active Learning with Overlapping Regions

Learning of classification models from real-world data often requires substantial human effort devoted to instance annotation. As this process can be very time-consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. To address this problem we explore a new type of human feedback - region-based feedback. Briefly, a region is defined as a hypercubic subspace of the input data space and represents a subpopulation of data instances; the region's label is a human assessment of the class proportion of the data subpopulation. By using learning from label proportions algorithms one can learn instance-based classifiers from such labeled regions. In general, the key challenge is that there can be infinite many regions one can define and query in a given data space. To minimize the number and complexity of region-based queries, we propose and develop a hierarchical active learning solution that aims at incrementally building a concise hierarchy of regions. Furthermore, to avoid building a possibly class-irrelevant region hierarchy, we further propose to grow multiple different hierarchies in parallel and expand those more informative hierarchies. Through experiments on numerous data sets, we demonstrate that methods using region-based feedback can learn very good classifiers from very few and simple queries, and hence are highly effective in reducing human annotation effort needed for building classification models.
Steganalysis of Adaptive Steganography Under the Known Cover Image

Digital image steganography is one of the important ways to achieve covert communication to protect users’ data privacy. Steganalysis technology is the key technology to check the security of steganography, and the extraction of embedded messages is an important challenge for image steganalysis technology. Existing methods are for plain text embedding or only for certain special scenarios, they are not applicable to the extraction under the known cover image. To this end, this paper proposes a method of extracting embedded messages under the condition of the known cover images. First, the STCs encoding process is analyzed using the syndrome trellis. Second, a path in the syndrome trellis can be obtained by using the stego sequence and a certain parity-check matrix. Meanwhile, the embedding process can also be partially simulated using the cover sequence and the parity-check matrix. By comparing whether the paths are consistent, the coding parameters can be quickly filtered to find the correct submatrices and extract the correct embedded messages. This algorithm avoids the second embedding of all possible secret messages, which significantly improves the efficiency of coding parameter recognition. The experimental results show that the proposed method can identify STCs parameters of stego images using HUGO steganography in a short time, so as to realize the extraction of embedded messages.
Relative Flatness and Generalization in the Interpolation Regime

Traditional generalization bounds are based on analyzing the limits of the model capacity. Therefore, they become vacuous in the \emph{interpolation} (over-parameterized) regime of modern machine learning models where training data can be fitted perfectly. This paper proposes a new approach to meaningful generalization bounds in the interpolation regime by decomposing the generalization gap into a notion of \emph{representativeness} and \emph{feature robustness}. Representativeness captures properties of the data distribution and mitigates the dependence on the data dimension by exploiting the low-dimensional feature representation used implicitly by the model, and feature robustness captures the expected change in loss resulting from perturbations of these implicit features. We show that feature robustness can be bounded by a relative flatness measure of the empirical loss surface for models that locally minimize the training loss. This yields an algorithm-agnostic bound potentially explaining the abundance of empirical observations that flatness of the loss surface is correlated with generalization.
Increasing The Community Capacity for Facing of Disaster Multi-Risk (Case Study in Dayu Village, Gondangrejo District, Karanganyar Regency)

&lt;p&gt;&lt;em&gt;Dayu Village which located in Gondangrejo Subdistrict, Karangnyar Regency has the potential disaster multi risks. Therefore, necessary to increase the capacity of Dayu Village community in facing disaster multi risks as anticipatory effort to minimize the impact of losses disasters in the future. The research used quantitative descriptive method with scoring and overlay analysis techniques, while for increasing community capacity through socialization related to the physical conditions of the area, FGD, participatory mapping, training and making evacuation routes. Based on the analysis results, it is known that Dayu Village has high level of disaster multi-risk. The results of FGD and participatory mapping showed that there were 6 points landslide and 1 point flood in Dusun Dayu and 4 landslide points and 2 flood points in Dusun Tanjung. These location points used as reference in determining the location of the gathering point along with the evacuation route to that gathering point.&lt;/em&gt;&lt;/p&gt;
Combining a quantum random number generator and quantum-resistant algorithms into the GnuGPG open-source software

Abstract The “quantum threat” to our current, convenient cryptographic algorithms is getting closer, with demonstrable progress by commercial quantum computing efforts. It is now more important than ever that we combine all of our tools into a new quantum-safe toolbox to develop the next generation of quantum-safe networking solutions. Here we combine an integrated quantum entropy source with quantum-resistant algorithms in the GnuGPG open-source software; leading to a fully quantum-safe version of GnuGPG. The quantum entropy source itself is capable of a raw rate of randomness in excess of 10 Gbps. After post-processing, quantum random numbers are used by the quantum-resistant algorithms to allow GnuGPG to perform its usual public-key cryptographic tasks, such as digitally signing documents, but now in a secure quantum-safe way.
A nonlinear Lazarev–Lieb theorem: L2-orthogonality via motion planning

Lazarev and Lieb showed that finitely many integrable functions from the unit interval to [Formula: see text] can be simultaneously annihilated in the [Formula: see text] inner product by a smooth function to the unit circle. Here, we answer a question of Lazarev and Lieb proving a generalization of their result by lower bounding the equivariant topology of the space of smooth circle-valued functions with a certain [Formula: see text]-norm bound. Our proof uses a variety of motion planning algorithms that instead of contractibility yield a lower bound for the [Formula: see text]-coindex of a space.
Cuestiones constitucionales sobre el estado de alarma en España y la pandemia del COVID-19

In this work we propose to describe from the constitutional point of view the key aspects of the declaration of the state of alarm, in connection with the problem of COVID-19, and also to carry out a more explanatory and analytical approach that will lead us to carry out a constructive criticism of the current situation in Spain. Geopolitical considerations are also made to help better understand the different aspects.
Fixed-point algorithms for inverse of residual rectifier neural networks

<p style='text-indent:20px;'>A deep neural network with invertible hidden layers has a nice property of preserving all the information in the feature learning stage. In this paper, we analyse the hidden layers of residual rectifier neural networks, and investigate conditions for invertibility under which the hidden layers are invertible. A new fixed-point algorithm is developed to invert the hidden layers of residual networks. The proposed inverse algorithms are capable of inverting some residual networks which cannot be inverted by existing inverting algorithms. Furthermore, a special residual rectifier network is designed and trained on MNIST so that it can achieve comparable performance with the state-of-art performance while its hidden layers are invertible.
A new weighted fuzzy C-means clustering approach considering between-cluster separability

Fuzzy C-means clustering algorithm (FCM) is an effective approach for clustering. However, in most existing FCM type frameworks, only in-cluster compactness is taken into account, whereas the between-cluster separability is overlooked. In this paper, to enhance the clustering, by incorporating the feature weighting and data weighting method, we put forward a new weighted fuzzy C-means clustering approach considering between-cluster separability, in which for achieving good compactness and separability, making the in-cluster distances as small as possible and making the between-cluster distances as large as possible, the in-cluster distances and between-cluster distances are taken into account; To achieve the optimal clustering result, the iterative formulas of the feature weights, membership degrees, data weights and cluster centers are obtained by maximizing the in-cluster compactness and the between-cluster separability. Experiments on real-world datasets were carried out, the results showed that the new approach could obtain promising performance.
Internet Japanese Teaching Based on Computer Aided Technology and Cultivation of Practical Senior Japanese Talents

Abstract As one of the most important Chinese foreign languages, Japanese teaching has been carried out for many years, and a large number of basic Japanese talents have been cultivated for various industries. The effectiveness of Japanese teaching and the deepening and expansion of the language application abilities of Japanese talents are not only of great significance to the long-term development of Japanese teaching and the training results of Japanese advanced application talents, but also have an important practical impact on the development of foreign exchange and cooperation. This article starts with the relationship between online Japanese teaching and the training of advanced Japanese practical talents, and analyzes the influence of computer-aided technology network teaching on the training of advanced Japanese practical talents and the ways to realize the training of Japanese basic practical talents.