Reviewerly/exHarmony huggingface dataset:
Fast Random Forest Algorithm via Incremental Upper Bound

Random forest is an ensemble approach based on decision trees. It computes the best split in each node in terms of impurity reduction. However, the impurity computations incur high computation cost in its training process. This paper proposes F-forest, an efficient variant of random forest. It incrementally estimates upper bounds for scores that correspond to impurity reductions to find the best split. Since we can safely skip unnecessary computations, it can guarantee the same training result as the original approach. Experiments show that our approach is faster than state-of-the-art approaches.
A Deep Neural Network to Detect Keyboard Regions and Recognize Isolated Characters

Some robot systems need to interactively auto-type on touch screens using a digital camera as the input source. To do so, it is important to have an algorithm that reliably detects the keyboard region and locates and recognizes its characters from an image. However, today most research efforts in the optical character recognition (OCR) area is focused on scene text detection and recognition. Though these algorithms work well on images with strong noise impacts, they perform poorly on keyboard texts that have isolated characters. In this paper, we present a framework to solve this problem. The algorithm consists of three steps: (i) a Single Short Detector (SSD) to search the keyboard regions from an input image, (ii) a deep neural network model that locates and recognizes the individual characters~(case sensitive) in one pass for a keyboard region, and (iii) a semi-supervised corrector that fixes the errors in step 2 using hand-crafted features. We will show that this algorithm is able to process different types of keyboards, and is robust to varying noise levels and text occlusions.
PHTI-WS: A Printed and Handwritten Text Identification Web Service Based on FCN and CRF Post-Processing

This paper introduces a lightweight model for printed and handwritten text identification in document images, which is then deployed as a web service to enable easy integration into existing workflows. Identifying printed and handwritten text in documents containing multiple handwritten annotations, which partially overlap machine printed text, is a challenging task. The lack of existence of a dataset containing pixel-level annotations for this task explains why the many papers tackling this problem employ word-or line-level classification, which cannot effectively solve the task mentioned above. In this work, we use a newly created dataset containing pixel-level annotations to train a lightweight, fully convolutional network, which we combine with a conditional random field for postprocessing. We measure the performance of our method on the aforementioned dataset and compare it to results achieved by the U-net fully convolutional architecture. Initial test results indicate a 90% mean IoU, which is a 5% improvement when comparing to the results produced by the U-net model after postprocessing. Hence, our contribution is two-fold. First, we introduce a model which combines a lightweight fully convolutional architecture with conditional random field postprocessing to solve the task of printed and handwritten text recognition on a pixel-level, and secondly, we describe how our model is deployed as a web service.
A New End-to-End Secondary Network for High-Efficient Vehicles and License Plates Detection

Up to now, a number of existing methods have worked fairly well in the field of vehicle and license plate detection. However, those methods are still not sufficient enough for satisfying practical industrial demands in terms of efficiency and accuracy, exhibiting poor performances such as incomplete detection of license plated when multiple vehicles are contained in the target image, and poor vehicle recognition when illumination is inadequate at night. To solve these limitations, a novel end-to-end secondary detection network named ES-Yolov3-tiny is proposed in this paper. ES-Yolov3-tiny can improve detection efficiency and accuracy by virtue of the secondary detection technology, and detect multiple vehicles in the same target image with the help of the interested region extraction technology. Compared with other existing state-of-the-art methods, the experiments show that the proposed method has better accuracy and lower computational cost.
Geological Mapping Using Multispectral Remote Sensing Data in the Western China

This study presents new results for geological mapping in arid and semi-arid regions using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. ASTER multispectral thermal-infrared (TIR) data have been processed for mapping soil desertification and salinization at the Taklimakan Desert and its surrounding areas, west China. The mineralogical indices such as Desertification Index (DI) and Salinity Index (SI) were proposed to estimate and monitor the silicate and soluble salt minerals which related with desertification and salinization in the arid and semi-arid environments. These mineralogical indices were verified by field observation and measurement data. It is concluded that the proposed methods have great potential for desertification and salinization mapping in arid and semi arid regions. Thus, it can provide an effective technology for the long-term monitoring and assessment of the eco-environment change in the dry lands on local, regional and global scales.
Automatic generation of software interfaces for supporting decision-making processes. An application of domain engineering and machine learning

Information dashboards are sophisticated tools. Although they enable users to reach useful insights and support their decision-making challenges, a good design process is essential to obtain powerful tools. Users need to be part of these design processes, as they will be the consumers of the information displayed. But users are very diverse and can have different goals, beliefs, preferences, etc., and creating a new dashboard for each potential user is not viable. There exist several tools that allow users to configure their displays without requiring programming skills. However, users might not exactly know what they want to visualize or explore, also becoming the configuration process a tedious task. This research project aims to explore the automatic generation of user interfaces for supporting these decision-making processes. To tackle these challenges, a domain engineering, and machine learning approach is taken. The main goal is to automatize the design process of dashboards by learning from the context, including the end-users and the target data to be displayed.
Virtual Immersive Educational Systems:The case of 360° video and co-learning design.

The focus of this research is to depict the design process of a cost-effective, robust but user-friendly Virtual Immersive Educational (VIE) system. Thus, assist researchers, instructors and designers in identifying an effective method to design VIE systems. In this report, we describe our initial steps to design such a system in order to educate engineering students on the basic health and safety guidelines of safe interaction with a robotic arm. To do so, a set of 360° videos have been designed, developed and tested.
DNA and Chaos Based Confusion-Diffusion for Color Image Security

Nowadays, secure transmission of multimedia files has become more significant concern with the evolution of technologies. Cryptography is the well-known technique to safeguard the files from various destructive hacks. In this work, a colour image encryption scheme is suggested using chaos and Deoxyribo Nucleic Acid (DNA) coding. The encryption scheme is carried out in two stages namely confusion and diffusion. As the first stage, chaos aided inter-planar row and column shuffling are performed to shuffle the image pixels completely. DNA coding and decoding operations then diffuse the resultant confused image with the help of eight DNA XOR rules. This confusion-diffusion process has achieved the entropy value equal to 7.9973 and correlation coefficient nearer to zero with key space of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">140</sup> . Various other analyses are also done to ensure the effectiveness of the developed algorithm. The results show that the proposed scheme can withstand different attacks and better than the recent state-of-art methods.
A Miniature CCA Public Key Encryption Scheme Based on Non-abelian Factorization Problem in Finite Groups of Lie Type

Abstract With the development of Lie theory, Lie groups have attained profound significance in several branches of Mathematics and Physics. In Lie theory, the matrix exponential plays a crucial role between Lie groups and Lie algebras. Meanwhile, as the finite analogue of Lie groups, finite groups of Lie type have potential applications in cryptography due to their unique mathematical structures. In this paper, we first put forward a novel idea of designing cryptosystems based on Lie theory. First of all, combing with discrete logarithm problem and group factorization problem, we proposed several new intractable assumptions based on the matrix exponential in finite groups of Lie type. Subsequently, in analog with Boyen’s scheme (Asiacrypt 2007), we designed a public-key encryption scheme based on the non-abelian factorization problem in finite groups of Lie type. Finally, our proposal was proved to be indistinguishable against adaptively chosen-ciphertext attack in the random oracle model. It is encouraging that our scheme also has the potential to resist against Shor’s quantum algorithm attack.
Grsa: Integrated ASIC for Spaceborne GNSS Remote Sensing

GRSA (GNSS Remote Sensing ASIC) is designed to be a radiation hardened GNSS remote sensing ASIC. GRSA follows the design of the first generation space-borne GNSS ASIC of NSSC named GOPA (GNSS Occultation Processing ASIC), but adds some new features to improve functions and the data quality. GRSA is capable of processing signals from all satellite navigation systems. It could be used in space missions for POD (Precise Orbit Determination), GNSS-RO (Radio Occultation), and GNSS-R (Reflectometry). On-chip processors will be integrated so that the signal processing board will be much simpler in the future than the existing designed. GRSA is now in architectural design and VHDL implementation phase. It will take 2 to 3 years to design and tape out.
Discovering Latent Threads in Entity Histories

Abstract Knowledge of entity histories is often necessary for comprehensive understanding and characterization of entities. Yet, the analysis of an entity’s history is often most meaningful when carried out in comparison with the histories of other entities. In this paper, we describe a novel task of history-based entity categorization and comparison . Based on a set of entity-related documents which are assumed as an input, we determine latent entity categories whose members share similar histories; hence, we are effectively grouping entities based on the correspondences in their historical developments. Next, we generate comparative timelines for each determined group allowing users to elucidate similarities and differences in the histories of entities. We evaluate our approach on several datasets of different entity types demonstrating its effectiveness against competitive baselines.
A framework for black-box SLO tuning of multi-tenant applications in Kubernetes

Resource management concepts of container orchestration platforms such as Kubernetes can be used to achieve multi-tenancy with quality of service differentiation between tenants. However, to support cost-effective enforcement of Service Level Objectives (SLOs) about response time or throughput, an automated resource optimization approach is needed for mapping custom SLOs of different tenants to cost-efficient resource allocation policies. We propose a versatile tool for cost-effective SLO tuning, named k8-resource-optimizer, that relies on black-box performance tuning algorithms. We illustrate and validate the tool for optimizing different resource configuration properties of a simple job processing application. Our experiments showed that k8-resource-optimizer can find near-optimal configurations for different multi-tenant deployment settings and different types of resource parameters. However an open research challenge is that, when the number of parameters increases, the total tuning cost may also increase beyond what is acceptable for contemporary cloud-native applications. We shortly discuss three possible complementary solutions to tackle this challenge.
Mutual Information Analysis of Social Media Images and Building Functions

Understanding urban dynamics requires detailed insights into urban land use. On the most fine-grained level this classification is done on single building instance levels. This level of detail can hardly be solved using remote sensing only, but requires complementary data. Social media images are a promising additional image data source since they are captured on a global scale in vast volumes.In this study we investigate the relation between objects showing up in geotagged social media images and functions of buildings proximate to the image location. We propose a rasterization approach to embed features from images and labels from a target domain to calculate mutual information both domains share. In our study area of Los Angeles, USA, we show that using object detection is a valuable way of extracting features from social media images to predict building functions. Furthermore, we present the most significant object types for five types of buildings.
Landslide Early Warning Model Based on the Coupling of Limit Learning Machine and Entropy Method

Monitoring and early warning are the means and methods to reduce landslide hazards. Traditional landslide monitoring methods are single, and the accuracy of statistical prediction model and deterministic prediction model are low. Aiming at this situation, a landslide warning model is proposed to comprehensively monitor the landslide impact factor and combine the Extreme Learning Machine with the Entropy method. The experimental results show that the results obtained by this method are consistent with the actual situation, and the predicted values are basically consistent with the measured values; the accuracy is 97.21%, which is higher than BP neural network; provide a feasible method for landslide warning model.
Development of Mathematics Module on the Material of Flat Side Space Building in DIY Culture-Based.

Abstract Research is based on the limited learning resources that can be used by students independently. The purpose of this research is to develop a Mathematics module in Jogja culture- based at junior high school with the material of Flat Side Space Building. To be able to find out the feasibility, effectiveness of the Jogja Culture-based Mathematics module that has been developed, and to determine the level of character of the love of the local culture of students through the implementation of the developed Mathematics module. This research use research and development design. Development carried out by referring to the 4-D model found by Tiagarajan is modified to 3-D including define, design, and develop. The subjects of this study are students of class VIIIB of SMP 2 Pajangan, Bantul Academic Year 2017 / 2018. The instruments used included Module assessment sheets, test instruments, questionnaire sheets, instrument validation sheets. The research data obtained is analyzed by descriptive percentage. The results show that the mathematics module based on DIY culture material Flat Side Space Building had fulfilled the eligibility criteria which included valid, effective, and practical. Valid based on expert judgment that shows the quality of worksheets is very good category with an ideal percentage of 85.7%. These results indicate that the Culture-based Mathematics module developed is suitable for use in the eighth grade mathematics learning in junior high school.
Information and Cognitive Technologies in the Context of the 4th Technological Revolution: Educational Aspects

Subject of the work: information and knowledge technologies as a key element of the 4th industrial revolution. These technologies are represented by a variety of Smart technologies, the scope of which is constantly expanding. However, there are still many unexplored features and capacities of these technologies. The purpose of this work is to analyse educational capacity of information-knowledge technologies and to formulate the strategies of their implementation at universities and secondary schools. The research methodology is based on the concept of convergence, according to which new technologies emerge from the convergence of both established and new technologies. The result of the analysis of the educational capabilities of information and cognitive technologies suggests that the technology of the full cycle information prevails in the field of education. This technology allows shifting from data to information and from information to knowledge. The area of application of the results includes educational standards, textbooks and teaching aids, programs of additional professional education
SYSTRAN @ WNGT 2019: DGT Task

This paper describes SYSTRAN participation to the Document-level Generation and Translation (DGT) Shared Task of the 3rd Workshop on Neural Generation and Translation (WNGT 2019).We participate for the first time using a Transformer network enhanced with modified input embeddings and optimising an additional objective function that considers content selection.The network takes in structured data of basketball games and outputs a summary of the game in natural language.
Construct Validation in Secondary Data: A Guideline for Medical Data Mining

Abstract Construct validation is an important step in the formulation of theory and its testing. Any information infers from the construct and its related hypothesis, without the validation, might be misleading the decision. In the literature, the construct validation guidelines for primary data are available but the exponential usage of secondary data mining approaches in recent times has created its need for secondary data users. However, it is a less addressed domain, specifically for medical sciences due to underdeveloped theoretical foundations of the field. Usually, an assessment of the validity of the secondary data is not performed because primary users already use validation process especially in the tool development phase. Further, secondary data users are more concerned with data mining that is a data-driven approach rather than theoretical knowledge of the field. Therefore, if researchers want to explore the hidden structures of data and try some different combinations of items which were not tested by primary users, then they should validate this newly explored group of items for the creation of knowledge of the particular field. In this paper, a guideline of the construct validation process for secondary data users with its practical issues especially validity and reliability coefficients is discussed. This paper concludes that the construct validation based on secondary data needs special attention of data mining experts and medical researchers while designing the studies to maximize the benefits of the secondary data.
Computer-Assisted Anthropology

Anthropology is the study of humans and human behavior (and also other primates). Various aspects of human life, including the objects made by humans (archeology), the biological aspects of humans (biological anthropology), how people in different parts of the world interact with each other and how their beliefs vary (sociocultural anthropology), and how humans communicate in different parts of the world (linguistic anthropology), are studied as a part of anthropology. This research is utilized in solving real-world problems in numerous fields, including healthcare, education, business, politics, the environment, interpreting history, etc. There has been a notable increase in the use of computers and related technologies for carrying out the required analysis in each of the four subfields of anthropology. Various data analytics and data visualization tools are used along with digital instruments. Many image-processing techniques along with computer graphics are employed in each of the four subfields. As currently used, the term digital anthropology overlaps with other terms such as virtual anthropology and cyber anthropology. Virtual anthropology involves the study of 3D digital models of anatomical structures of humans and nonhuman primates, as well as fossil specimens. Reconstruction of the fossils and the skeletons virtually is also extensively used. Virtual anthropology has a number of advantages, including the ability to study internal structures of the skeleton noninvasively and the availability of virtual objects due to the permanence of the data and the potential for data sharing. The use of digital imaging techniques, such as computed tomography (CT) or optical surface scanning, has contributed to numerous medical-related domains in archeology. As a part of sociocultural anthropology, people can be clustered based on their actions in social networks, online communities, and online games. This is a part of digital ethnography, which involves analyzing people on online social platforms, taking into consideration the conflicts of people within their communities and outside their communities and linguistic features and memetic dialects of people with respect to their cultures and geographic locations. Based on the huge amount of data which can be retrieved from online sites and taking into account the activities of different people in different situations online, conclusions can be drawn about the behavior of people. The chapter starts with an introduction to anthropology, followed by a description of each of the subfields. Then the computing technologies being employed in each of the fields of anthropology are described. Finally, digital ethnography and virtual anthropology are discussed.
Thread Structure Learning on Online Health Forums With Partially Labeled Data

Thread structures, the reply relationships between posts, in online forums are very important for readers to understand the thread content, and for improving the effectiveness of automated forum information retrieval, expert findings, and so on. However, most online forums only have partially labeled structures, which means that some reply relationships are known while the others are unknown. To address this problem, studies have been performed to learn and predict thread structures. However, existing work does not leverage the partially available thread structures to learn the complete thread structure. We have also observed that many online health forums are a type of person-centric forums, where persons are mentioned across posts, providing hints about the reply relationships between posts. In this article, we first proposed to learn the complete thread structures by leveraging the partially known structures based on a statistical machine learning model-thread conditional random fields (threadCRFs). Then, we proposed to use person resolution, the process of identifying the same person mentioned in different contexts, together with threadCRF for thread structure learning. We have empirically verified the effectiveness of the proposed approaches.