Reviewerly/exHarmony huggingface dataset:
Receptive field enrichment network for pedestrian detection

The current advanced pedestrian detection methods adopt feature maps with different resolutions to cover multiscale pedestrians. Despite multi-scale feature pyramid can alleviate the problems caused by scale variation, each layer used for detection has merely a fixed receptive field, which results in the defects related to pedestrians with wide range of scale and aspect ratio variation. In this paper, we propose the Receptive Field Enrichment Network (RFENet), an endto- end framework for fast and accurate pedestrian detection. Two blocks are introduced in this framework, a receptive field enrichment module (RFEM) and a hierarchy aggregation module (HAM). The former is designed to diversify receptive fields of features, so as to better adapt to pedestrians with different scales and aspect ratios. The latter is further applied to enhance the entire feature hierarchy by merging spatial information and high-level semantics from different layers simultaneously. To evaluate the effectiveness of our method, extensive experiments are conducted on CityPersons and Caltech datasets. The results show that our proposed RFENet achieves comparable performance with state-of-the-art methods.
NE-Table: A Neural Key-Value Table for Named Entities

Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources.While this is easy for humans, the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks, especially in the presence of Out-Of-Vocabulary (OOV) or rare NEs.In this paper, we propose a solution for this problem, and present empirical evaluations on: a) a structured Question-Answering task, b) three related Goal-Oriented dialog tasks, and c) a Reading-Comprehension task 1 , which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs.
Spatial Probabilistic Distribution Map Based 3D FCN for Visual Pathway Segmentation

Image-guided surgery has become an important aid in sinus and skull base surgery. In the preoperative planning stage, vital structures, such as the visual pathway, must be segmented to guide the surgeon during surgery. However, owing to the elongated structure and low contrast in medical images, automatic segmentation of the visual pathway is challenging. This study proposed a novel method based on 3D fully convolutional network (FCN) combined with a spatial probabilistic distribution map (SPDM) for visual pathway segmentation in magnetic resonance imaging. Experimental results indicated that compared with the FCN that relied only on image intensity information, the introduction of an SPDM effectively overcame the problem of low contrast and blurry boundary and achieved better segmentation performance.
Multi-scale Feature Fused Single Shot Detector for Small Object Detection in UAV Images

Small object detection is a challenging computer vision problem due to their low feature representation in the images and factors such as occlusions and noise. In images captured from a camera mounted on an unmanned aerial vehicle (UAV), objects are usually acquired in small sizes depending on the UAV flight altitude. The state-of-the-art object detectors often have lower detection accuracy with small objects. New approaches of combining features at multi-levels in the network helps in improving the object detection performance. In this paper, we propose a multi-scale approach of low-level feature combinations with deconvolutional modules on a single shot multibox detection (SSD) object detector to improve the small object detection in images acquired from a UAV. The proposed SSD based architecture is evaluated on UAV datasets to compare its performance with the state-of-the-art detectors.
Cybercrime Preparedness A Critical Snapshot of BRICS Countries

As the global stage prepares for the many benefits and new opportunities brought about by the advances in information and communications technology (ICT), particularly in the context of the fourth industrial revolution, there is little doubt that these new opportunities will usher in new challenges and threats deriving from the growing misuse of ICTs for criminal activities and the increasing malicious use of ICTs by state and non-state actors. Against this backdrop this paper provides a snapshot of cybersecurity ‘preparedness’ on the part of Brazil, Russia, India, China, and South Africa, (BRICS) countries, to mitigate these challenges. This has been obtained through discussion of literature and media reports written on the topic and through content analysis of some critical policy documents that govern cybersecurity frameworks in the BRICS countries. The paper makes recommendations for the BRICS Think Tanks Council (BTTC) to play a more significant role in shaping policy initiatives collectively, while maintaining sovereignty, should it see a role for itself in shaping a new world order.
O componente cultural nas interações de teletandem: teorias e reflexões

Teletandem Brasil: foreign languages for all" is a project developed by UNESP with the aim of providing the learning of foreign languages by placing pairs of speakers of different languages in contact synchronously, in a collaborative way, through communication resources on the Internet (TELLES; VASSALLO, 2006).Based on previous research about the cultural components in this context, this article aims to present and reflect on the theoretical concepts about culture addressed in such research, in order to identify how these concepts apply to the context of Teletandem, and how we can observe their presence in the participants' experience, both in interaction sessions and in subsequent mediations.We can conclude that this form of collaborative learning promotes the emergence of the cultural component dynamically among the participants as a process of reflection on their own language and culture provided by the interaction with the other.
25 Years of the Burrows-Wheeler Transform (Dagstuhl Seminar 19241)

Dagstuhl Seminar 19241 (25 Years of the Burrows-Wheeler Transform) took place from June 10th to 14th, 2019, and was attended by 45 people from 13 countries and the three fields of Algorithms and Data Structures, Bioinformatics, and Combinatorics on Words. There were four talks and a panel session for each field. Feedback was generally positive and we are confident the seminar fostered interdisciplinary connections and will eventually result in noteworthy joint publications.
Effect Pengaruh Kegiatan Ekstrakurikuler Bahasa Jepang terhadap Hasil Belajar Siswa SMA N 1 Ambarawa

The purpose of this study was to determine the effect of Japanese language extracurricular on study result of the students of SMA N 1 Ambarawa, and to find out the factors that influenced Japanese extracurricular activities at SMA N 1 Ambarawa.This quantitative descriptive method has a population and samples in this research are students of class X MIPA 4, X MIPA 6, X SOS 3, and X Language who have the results of learning Japanese and participating in Japanese extracurricular activities. The method of data collection is carried out observation sample and documented the activities. The data obtained is calculated using SPSS and produces a value of 0.483, and with N = 53 and a significance level of 5% is 0.266 (0.483 2 0.266). From the results of data processing, there was a contribution of 23.3%, which means that Japanese language extracurricular activities affect the Japanese study result of SMA N 1 Ambarawa students by 23.3%. The biggest factor affecting Japanese extracurricular activities is the student psychological factors in order to add more information about Japanese Leanguage and Culture (97.2%).
Visual Dynamics Models for Robotic Planning and Control

Author(s): Lee, Alex Xavier | Advisor(s): Abbeel, Pieter; Levine, Sergey | Abstract: For a robot to interact with its environment, it must perceive the world and understand how the world evolves as a consequence of its actions. This thesis studies a few methods that a robot can use to respond to its observations, with a focus on instances that can leverage visual dynamic models. In general, these are models of how the visual observations of a robot evolves as a consequence of its actions. This could be in the form of predictive models that directly predict the future in the space of image pixels, in the space of visual features extracted from these images, or in the space of compact learned latent representations. The three instances that this thesis studies are in the context of visual servoing, visual planning, and representation learning for reinforcement learning. In the first case, we combine learned visual features with learning single-step predictive dynamics models and reinforcement learning to learn visual servoing mechanisms. In the second case, we use a deterministic multi-step video prediction model to achieve various manipulation tasks through visual planning. In addition, we show that conventional video prediction models are unequipped to model uncertainty and multiple futures, which could limit the planning capabilities of the robot. To address this, we propose a stochastic video prediction model that is trained with a combination of variational losses, adversarial losses, and perceptual losses, and show that this model can predict futures that are more realistic, diverse, and accurate. Unlike the first two cases, in which the dynamics model is used to make predictions for decision-making, the third case learns the model solely for representation learning. We learn a stochastic sequential latent variable model to learn a latent representation, and then use it as an intermediate representation for reinforcement learning. We show that this approach improves final performance and sample efficiency.
Dynamic prediction using joint models of longitudinal and recurrent event data: a Bayesian perspective

In cardiovascular disease (CVD) studies, the events of interest may be recurrent (multiple occurrences from the same individual). During the study follow-up, longitudinal measurements are often available and these measurements are highly predictive of event recurrences. It is of great clinical interest to make personalized prediction of the next occurrence of recurrent events using the available clinical information, because it enables clinicians to make more informed and personalized decisions and recommendations. To this end, we propose a joint model of longitudinal and recurrent event data. We develop a Bayesian approach for model inference and a dynamic prediction framework for predicting target subjects' future outcome trajectories and risk of next recurrent event, based on their data up to the prediction time point. To improve computation efficiency, embarrassingly parallel MCMC (EP-MCMC) method is utilized. It partitions the data into multiple subsets, runs MCMC sampler on each subset, and applies random partition trees to combine the posterior draws from all subsets. Our method development is motivated by and applied to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), one of the largest CVD studies to compare the effectiveness of medications to treat hypertension.
From Intra-Modal to Inter-Modal Space: Multi-task Learning of Shared Representations for Cross-Modal Retrieval

Learning a robust shared representation space is critical for effective multimedia retrieval, and is increasingly important as multimodal data grows in volume and diversity. The labeled datasets necessary for learning such a space are limited in size and also in coverage of semantic concepts. These limitations constrain performance: a shared representation learned on one dataset may not generalize well to another. We address this issue by building on the insight that, given limited data, it is easier to optimize the semantic structure of a space within a modality, than across modalities. We propose a two-stage shared representation learning framework with intra-modal optimization and subsequent cross-modal transfer learning of semantic structure that produces a robust shared representation space. We integrate multi-task learning into each step, making it possible to leverage multiple datasets, annotated with different concepts, as if they were one large dataset. Large-scale systematic experiments demonstrate improvements over previously reported state-of-the-art methods on cross-modal retrieval tasks.
THE PROBLEM OF THE FORMATION OF LITERARY COMPETENCE OF SENIOR PUPILS IN THE PROCESS OF ANALYSIS OF ARTISTIC WORKS WITH THE USE OF INNOVATIVE MEANS OF EDUCATION

The article deals with the actual problems of emotional and intellectual development and education of youth in the process of studying literature; the motivational readiness of 10-11-grade students for the use of information and communication technologies (ICT) in the subject activity was determined; possible types of reading activity with the use of ICT during the event, analogous, problem-thematic analysis of the artistic work; experimental work evaluations are given. The emphasis is on the text-centric study of literature, the need for the emotional and intellectual perception of the art of the word, readiness for reader activity. The basic ideas of research are described. The reasons for teachers' unpreparedness to use ICT tools in the lessons of Ukrainian literature are singled out. The results of the questionnaire, conducted to determine the level of formation of literary competence (LC) of senior pupils, are summarized. In order to check the levels of literacy and skills development based on research and experimental learning. Developed tasks that take into account the level of the formation of readers' skills, individual peculiarities, the possibility of perceiving a representative school system, developed cross-cutting skills that a senior pupil-reader must possess for the purpose of continuous productive learning. The connection with important practical tasks, indicators of levels of LC formation for high school students by means of ICT is presented. In scientific research, the experience of using ICT during the analysis of artistic work is disclosed. The aesthetic reception and artistic communication (internal and external) between the author, the work and the reader are highlighted
E-Marketing Strategic for Jordanian Small Business to Increase Sale in Local E-Market

Purpose: This paper aims to explore different ways through which Jordanian small businesses can raise their respective total sales by the use of local e-market. Some of the components of the conceptual framework include after-sale services, e-marketing budget, tools for online marketing, e-promotions aimed at improving marketing performance and usefulness of marketing. Design: The researcher developed and tested four hypotheses related to different aspects of the e-market business model. Testing of measuresâ authenticity and hypothesis is usually essential. For accurate testing of both, confirmatory factor analysis and structured equations used respectively. Data collected from 110 general managers working in the marketing departments of small firms in Jordan. Findings: The studyâs findings indicated that the young population between the ages of 22-35 years has acknowledged and they usually embrace the use of digital marketing as a cheaper and efficient way of buying not only goods but also services. The aged are reluctant to adopt because they are not well conversant with online marketing whereas others do not trust online purchases. The research results have grave effects on not only the marketing strategies but also the development in the implementation of online marketing in small Jordanian businesses. In this modern era, the use of traditional marketing strategies is fading away with the penetration of online marketing in the social media platforms, emails, blogs, and general troubleshooting engines. Originality: There exist a gap in research in the Jordanian market where most researchers have explored less into the relationship of e-market to the market structure and the small market enterprises and the effects associated with the arising trends in the e-market. The research will also look into the correlation of these parameters to government support and policies. Thus, this research will prove to be instrumental in filling this gap as well as opening the stage for more research to been done on the same line.
ANALISIS USER INTERFACE DALAM APLIKASI MOBILE LIBRARY IPUSNAS

&lt;p&gt;This study to determine the application of the user interface in the IPusnas mobile library application. Analysis of the application of the user interface in this mobile library application uses 10 components, namely conectivity, directional, informative , interactivity, user friendliness, comprehensiveness, continuity, personalization, and internal (internal). In this article the research method uses a kaulitatif descriptive approach. Where from this study it can be concluded that the application of IPusnas makes it easy for users to search and read e-Book. This application is flexible because users can take advantage of this IPusnas application anywhere and anytime according to their wishes and this makes the IPusnas application easy to use. However, it is necessary to develop applications again to further optimize the appearance of the IPusnas application. In addition, the Perpustakaan Nasional (Perpusnas) needs to promote the Ipusnas mobile library application that has been made to the general public, so that applications can be fully utilized.&lt;/p&gt;
Differential Random Fault Attacks on Certain CAESAR Stream Ciphers

We show that a particular class of stream ciphers – namely those in which the output function contains a bitwise AND operation – are susceptible to a differential fault attack using random faults. Several finalists and other candidates from the recent CAESAR competition fall into this category, including the AEGIS variants, Tiaoxin and the MORUS family. Attack outcomes range from key or full state recovery for Tiaoxin, to full state recovery for the AEGIS family and partial state recovery for MORUS. We present attack requirements and success probabilities on these ciphers, along with design considerations to mitigate against this attack.
A Novel Chaotic Image Steganography Technique

In this paper, a lossy image steganography technique is introduced following the least significant bits method. Two chaotic maps are used for randomly selecting the cover image’s pixels to hide the message image. Generalized chaotic maps are used for adding extra degrees of freedom in designing the pseudorandom numbers used for pixels’ position selection rendering better security level. The chaotic maps used are the generalized logistic map and the delayed logistic map. Different image sets are employed for method validation. The Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), Image Fidelity (IF) as well as the capacity of the cover image are calculated as security metrics to ensure the high efficiency of the proposed technique compared to other works in literature.
Analisis Kesalahan Penggunaan Fukushi Kanarazu, Kitto, Zettai(ni) dan Zehi pada Mahasiswa Prodi Pendidikan Bahasa Jepang UNNES

The purpose of this study was to find out the errors and any factors that caused errors in the use of kanarazu, kitto, zettai (ni) and zehi on students of the UNNES Japanese Language Education Study Program. The population in this study were students of the 2015 Japanese Language Education Study Program UNNES. The sample was taken by a random sampling technique, which was 37 students. Retrieval of data using the test method as many as 20 questions. The results showed an average percentage of fukushi use errors of 71% in the high category. The highest error rate for using fukushi is zehi. The cause of the error is due to a lack of understanding of fukushi use, the similarity of meaning between the four fukushi, less careful attention to the patterns that follow, so they do not understand the context of the four uses of fukushi.
Financial Quantitative Big Data Platform Based on High Performance Computing

A big data platform to for financial quantitative data is designed and implemented on HPC system. Key technologies including data storage mechanism and distributed computing framework are resolved. Based on the platform, several important feature for financial quantitative strategy research is developed, which are indicator computing, large scale backtest and distributed hyperparameter tuning. Tests shows that the platform can achieve much higher performance than single PC program, and can be used to design strategy base on large scale financial data.
Parallel approach of algorithms

Nowadays the parallelization of various computations becomes more and more important. In this book the theoretical models of parallel computing are presented. Problems that can be solved and problems that cannot be solved in these models are described. The parallel extensions of the traditional computing models, formal languages and automata are presented, as well as properties and semi-automatic verifications and generations of parallel programs. The concepts of theory of parallel algorithms and their complexity measures are introduced by the help of the (parallel) Super Turing-machine. The parallel extensions of context-free grammars, such as L-systems, CD and PC grammar systems; multihead automata (including various Watson-Crick automata), traces and trace languages are described. The theory and applications of Petri nets are presented by their basic concepts, special and extended models. From the practical, programming point of view, parallelization of (sequential) programs is investigated based on discovering of dependencies.
Issues and Challenges for the Development of the Knowledge Economy in Sub-Saharan Africa

Africa and particularly Sub-Saharan Africa has the lowest tertiary gross enrolment ratio globally, significantly lower than South Asia.This low enrolment is happening at the time when the population of the region is growing at unprecedented rates creating a population age structure dominated by young people under the age of 25 years.Unfortunately, the high population growth rate is not corresponding to the level of adequate human capital required for economic growth.For the region to gain from its youthful population, there must be concerted efforts to enhance productivity through education and skills development.In other words, governments in the region must ensure that the growth in the youthful population must be such that they are capable of contributing to the economy through productive jobs.While higher education institutions in the continent, continue to fall short in number of students they enroll and the quality and relevance of training they offer.This paper argues that both the number, length and quality of schooling in Africa is responsible for low economic growth.The paper proposes that unless investment in higher education is enhanced, countries in the Sub Saharan Africa will not attain requisite economic growth commanded by knowledge in science, technology and innovation.The paper recommends that since higher education generally has a greater effect on development outcomes, there is a need for develop policies and strategies to ensure investment in higher education is given high priority.