Reviewerly/exHarmony huggingface dataset:
Manipulated-object Detection and Pose Estimation Based on Multimodal Feature Points and Neighboring Patches

The detection and pose estimation of objects in human demonstrations remain challenging yet crucial tasks.The increasing availability of red-green-blue and depth sensors makes it possible to synthetize local features of color and three-dimensional (3D) geometry, which are useful for processing a wider range of objects.However, existing methods fail to combine the inherent advantages of these two features.Moreover, pose refinement methods based on whole point clouds are often affected by occlusion and background noise.In this paper, feature points of the speeded-up robust feature and the fast point feature histogram were transformed into the same 3D space.After matching them separately, multimodal feature points were jointly used to estimate a coarse pose.Subsequently, the coarse pose was refined by aligning point clouds composed of feature points' neighboring patches.During the iterative closest point process, we selected corresponding points in matched local patches.In our first and second comparative experiments, F1 scores were respectively increased by 0.1349 and 0.1633, which verified the validity of our method.Finally, the third qualitative experiment showed that the proposed method is applicable to manipulated-object detection and pose estimation.
Disentangling Controllable Object Through Video Prediction Improves Visual Reinforcement Learning

In many vision-based reinforcement learning (RL) problems, the agent controls a movable object in its visual field, e.g., the player's avatar in video games and the robotic arm in visual grasping and manipulation. Leveraging action-conditioned video prediction, we propose an end-to-end learning frame-work to disentangle the controllable object from the observation signal. The disentangled representation is shown to be useful for RL as additional observation channels to the agent. Experiments on a set of Atari games with the popular Double DQN algorithm demonstrate improved sample efficiency and game performance (from 222.8% to 261.4% measured in normalized game scores, with prediction bonus reward).
Learning to Rank Images with Cross-Modal Graph Convolutions

We are interested in the problem of cross-modal retrieval for web image search, where the goal is to retrieve images relevant to a text query. While most of the current approaches for cross-modal retrieval revolve around learning how to represent text and images in a shared latent space, we take a different direction: we propose to generalize the cross-modal relevance feedback mechanism, a simple yet effective unsupervised method, that relies on standard information retrieval heuristics and the choice of a few hyper-parameters. We show that we can cast it as a supervised representation learning problem on graphs, using graph convolutions operating jointly over text and image features, namely cross-modal graph convolutions. The proposed architecture directly learns how to combine image and text features for the ranking task, while taking into account the context given by all the other elements in the set of images to be (re-)ranked. We validate our approach on two datasets: a public dataset from a MediaEval challenge, and a small sample of proprietary image search query logs, referred as WebQ. Our experiments demonstrate that our model improves over standard baselines.
Segmentation of Overlapped Cervical Cells Using Asymmetric Mixture Model and Shape Constraint Level Set Method

Accuracy segmentation of the nuclei and cytoplasm in Pap smear images is challenging in cervix cytological analysis. In this paper, a new fusion algorithm combining the asymmetric generalized Gaussian and Cauchy mixture model (GGCMM) with a shape constraint level set method to segment overlapping cervical smear cells is put forward. The proposed approach starts by separating nuclei and cytoplasm cluster through asymmetric GGCMM, where each component is a mixture of generalized Gaussian distribution and Cauchy distribution. The proposed asymmetric GGCMM takes into account the asymmetry of generalized Gaussian distribution and the heavier tail of Cauchy distribution. New probability distribution fits different shapes of observed data more flexibly. Then, we apply the morphological operation to remove fake nuclei which is usually much smaller than real nuclei. After that, the improved level set energy function with a distance map and a new shape prior term are applied to extract the contours of overlapping cervical cells. Due to this new level set energy function, the segmentation of every individual cell worked well, especially in overlapping areas. We evaluate the proposed method by using the ISBI 2014 Challenge Dataset. The results demonstrate that our approach outperforms existing methods in extracting overlapping cervical cells and obtains accurate cell contours.
Srzoo: An Integrated Repository For Super-Resolution Using Deep Learning

Deep learning-based image processing algorithms, including image super-resolution methods, have been proposed with significant improvement in performance in recent years. However, their implementations and evaluations are dispersed in terms of various deep learning frameworks and various evaluation criteria. In this paper, we propose an integrated repository for the super-resolution tasks, named SRZoo, to provide state-of-the-art super-resolution models in a single place. Our repository offers not only converted versions of existing pre-trained models, but also documentation and toolkits for converting other models. In addition, SRZoo provides platform-agnostic image reconstruction tools to obtain super-resolved images and evaluate the performance in place. It also brings the opportunity of extension to advanced image-based researches and other image processing models. The software, documentation, and pre-trained models are publicly available on GitHub.
Visual Re-Ranking via Adaptive Collaborative Hypergraph Learning for Image Retrieval

Visual re-ranking has received considerable attention in recent years. It aims to enhance the performance of text-based image retrieval by boosting the rank of relevant images using visual information. Hypergraph has been widely used for relevance estimation, where textual results are taken as vertices and the re-ranking problem is formulated as a transductive learning on the hypergraph. The potential of the hypergraph learning is essentially determined by the hypergraph construction scheme. To this end, in this paper, we introduce a novel data representation technique named adaptive collaborative representation for hypergraph learning. Compared to the conventional collaborative representation, we consider the data locality to adaptively select relevant and close samples for a test sample and discard irrelevant and faraway ones. Moreover, at the feature level, we impose a weight matrix on the representation errors to adaptively highlight the important features and reduce the effect of redundant/noisy ones. Finally, we also add a nonnegativity constraint on the representation coefficients to enhance the hypergraph interpretability. These attractive properties allow constructing a more informative and quality hypergraph, thereby achieving better retrieval performance than other hypergraph models. Extensive experiments on the public MediaEval benchmarks demonstrate that our re-ranking method achieves consistently superior results, compared to state-of-the-art methods.
Prácticas de seguridad de información del Nivel Ejecutivo de la Policía Nacional de Colombia: Escuela de Policía Simón Bolívar (Tuluá, Colombia)

The use of different types of technology is increasingly common in a larger and more varied group of people. In that sense, the students of the Professional Technical Program in Police Service and professional police officers of the Simón Bolívar Police School are no exception. Therefore, it was decided to study, from the quantitative and qualitative approaches, the type of practices, from the point of view of information security, which both groups apply in the daily life of their activities. Thus, an instrument consisting of five variables was validated and applied to determine strengths and weaknesses in information security. As a result, it was found that on average the behaviors and knowledge about information security are medium level and, therefore, the risk of being associated with problems in this area is high, which implies a need to redesign continuous training programs and keep in the academic curriculum subjects that include units referring to good information security practices.
Attribution in Scale and Space

We study the attribution problem [28] for deep networks applied to perception tasks. For vision tasks, attribution techniques attribute the prediction of a network to the pixels of the input image. We propose a new technique called \emph{Blur Integrated Gradients}. This technique has several advantages over other methods. First, it can tell at what scale a network recognizes an object. It produces scores in the scale/frequency dimension, that we find captures interesting phenomena. Second, it satisfies the scale-space axioms [14], which imply that it employs perturbations that are free of artifact. We therefore produce explanations that are cleaner and consistent with the operation of deep networks. Third, it eliminates the need for a 'baseline' parameter for Integrated Gradients [31] for perception tasks. This is desirable because the choice of baseline has a significant effect on the explanations. We compare the proposed technique against previous techniques and demonstrate application on three tasks: ImageNet object recognition, Diabetic Retinopathy prediction, and AudioSet audio event identification.
Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction

Four kernel functions of support vector machines (SVM), namely, radial basis function, sigmoid function, linear function and polynomial function, were applied for the prediction of solar cell output power. Two types of SVM model such as epsilon-SRV and nu-SVR were chosen for each kernel function. Measured values of temperature T (°C) and irradiance E (〖kWh.m〗^(-2)) were used as inputs and solar cell output power P (kW) was used as output. The accuracy of each kernel function was evaluated using well known statistical parameters. Radial basis function using nu-SVR and polynomial function using epsilon-SVR provided similar and better results than other kernels. However, polynomial function has taken more analysis run time while radial basis function used more number of support vectors than other kernels. They may be more computationally expensive.
Distant Supervision for Extractive Question Summarization

Questions are often lengthy and difficult to understand because they tend to contain peripheral information. Previous work relies on costly human-annotated data or question-title pairs. In this work, we propose a distant supervision framework that can train a question summarizer without annotation costs or question-title pairs, where sentences are automatically annotated by means of heuristic rules. The key idea is that a single-sentence question tends to have a summary-like property. We empirically show that our models trained on the framework perform competitively with respect to supervised models without the requirement of a costly human-annotated dataset.
A concatenation construction for propelinear perfect codes from regular subgroups of $\mathrm {GA}(r,2)$

A code C is called propelinear if there is a subgroup of Aut(C) of order |C| acting transitively on the codewords of C. In the paper new propelinear perfect binary codes of any admissible length more than 7 are obtained by a particular case of the Solov'eva concatenation construction-1981 and the regular subgroups of the general affine group of the vector space over GF(2).
Multi-View Matching (MVM): Facilitating Multi-Person 3D Pose Estimation Learning with Action-Frozen People Video

To tackle the challeging problem of multi-person 3D pose estimation from a single image, we propose a multi-view matching (MVM) method in this work. The MVM method generates reliable 3D human poses from a large-scale video dataset, called the Mannequin dataset, that contains action-frozen people immitating mannequins. With a large amount of in-the-wild video data labeled by 3D supervisions automatically generated by MVM, we are able to train a neural network that takes a single image as the input for multi-person 3D pose estimation. The core technology of MVM lies in effective alignment of 2D poses obtained from multiple views of a static scene that has a strong geometric constraint. Our objective is to maximize mutual consistency of 2D poses estimated in multiple frames, where geometric constraints as well as appearance similarities are taken into account simultaneously. To demonstrate the effectiveness of 3D supervisions provided by the MVM method, we conduct experiments on the 3DPW and the MSCOCO datasets and show that our proposed solution offers the state-of-the-art performance.
EL USO DE LAS REDES SOCIALES Y EL DESEMPEÑO ACADÉMICO DE LOS ADOLESCENTES DE BÁSICA SECUNDARIA

Currently, ICTs are a powerful agent of change to redesign learning environments and teaching resources, allowing pedagogical models to be more flexible and qualified in terms of school functioning, the curriculum, the role of the teacher and the learning experiences that It is offered to the student.Therefore, the objective of the proposed research topic is the relationship between academic performance and the use of social networks by secondary school students.Morover, research reforms and educational policies located at current school as one that raises; the motivation, disinterest and apathy of the new generations compared to the training and education models that the traditional system has offered them, in this sense, the reality of the conceptual debate framed from the new information and communication technologies, such as a theoretical corpus, education.The study was empirical with a quantitative approach, of descriptive type, applied to the student population-major late-being a sample of 219 students of the San Vicente Educational Institution of the city of Guadalajara de Buga.The techniques and data collection instruments were the observation, the structured survey, the questionnaire and the online form among others.In relation to the latter, a statistical analysis of the data obtained was carried out, which allowed us to find a relationship between academic performance and time spent on social networks of secondary school adolescents.
NFC Based Intelligent Bus Ticketing System

As the India is one of the fastest growing economies in the world, the Indian urban population has a large dependency on buses for public transport, providing importance of transport within urban/rural area throughout the country. In the public transport various problems are faced like traffic jam, confusion and misunderstanding between the passengers regarding fares, having several security problems in public transport due to antisocial elements, etc. For making the life more convenient for the computer travelling in buses some new technologies can be used like Near Field Communication (NFC) and Radio Frequency Identification. The proposed system is based on ticketing of bus and tracking of bus. The system suggests a automated ticketing system which will automatically cost will deduct the passengers fare according to the distance travelled.
Requirements of Data Visualisation Tools to Analyse Big Data: A Structured Literature Review

The continual growth of big data necessitates efficient ways of analysing these large datasets. Data visualisation and visual analytics has been identified as a key tool in big data analysis because they draw on the human visual and cognitive capabilities to analyse data quickly, intuitively and interactively. However, current visualisation tools and visual analytical systems fall short of providing a seamless user experience and several improvements could be made to current commercially available visualisation tools. By conducting a systematic literature review, requirements of visualisation tools were identified and categorised into six groups: dimensionality reduction, data reduction, scalability and readability, interactivity, fast retrieval of results, and user assistance. The most common themes found in the literature were dimensionality reduction and interactive data exploration.
Multi-part and scale adaptive visual tracker based on kernel correlation filter

Accurate visual tracking is a challenging issue in computer vision. Correlation filter (CF) based methods are sought in visual tracking based on their efficiency and high performance. Nonetheless, CF-based trackers are sensitive to partial occlusion, which may reduce their overall performance and even lead to failure in tracking challenge. In this paper, we presented a very powerful tracker based on the kernelized correlation filter tracker (KCF). Firstly, we employ an intelligent multi-part tracking algorithm to improve the overall capability of correlation filter based tracker, especially in partial-occlusion challenges. Secondly, to cope with the problem of scale variation, we employ an effective scale adaptive scheme, which divided the target into four patches and computed the scale factor by finding the maximum response position of each patch via kernelized correlation filter. With this method, the scale computation was transformed into locating the centers of the patches. Thirdly, because the small deviation of the central function value will bring the problem of location ambiguity. To solve this problem, the new Gaussian kernel functions are introduced in this paper. Experiments on the default 51 video sequences in Visual Tracker Benchmark demonstrate that our proposed tracker provides significant improvement compared with the state-of-art trackers.
Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning

Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results. However, this comes with the cost of having a single, large model for each task, which is not ideal in low-memory/power scenarios (e.g., mobile). In this paper, we propose an effective way to fine-tune multiple down-stream generation tasks simultaneously using a single, large pre-trained model. The experiments on five diverse language generation tasks show that by just using an additional 2-3% parameters for each task, our model can maintain or even improve the performance of fine-tuning the whole model.
Blockchain and Smart Contracts in the Recording Industry

Purpose: A burgeoning body of research has described how the blockchain technology may affect the way firms operate within the recording industry which has undergone profound changes due to the dematerialisation of music and the emergence of now consumption habits. The purpose of the paper is to explore both the challenges and the opportunities related to the application of smart contracts and blockchain mechanisms to the recording industry. Approach/Methodology/Design: Based on a review of contributions made to the literature in various fields, we discuss recent developments, relying on several examples and use cases which bring an updated perspective to a topical question. While the blockchain brings interesting solutions in favour of an improved management of copyright data and fees collection, several barriers impede their uptake and large-scale adoption. Findings: We argue that the absence of both technological and regulatory standards, the resistance to change, and the necessary use of cryptocurrency, are all obstacles to a profound transformation of the sector. Practical Implications: To overcome these limitations, we suggest three recommendations that deal with technological standards, cooperative agreements, and international regulation around blockchain. Originality/Value: So far, the literature tends to focus either on blockchain technology or on smart contracts when discussing technological evolution within the recording industry. In this paper, we bring together these two elements which are definitely complementory to each other. Further research efforts are required to investigate in more details the feasibility and relevance of the recommendations we make.
The state of mobile learning supported by gamification and augmented reality in higher education institutions across three continents

The pervasive digitization is now a reality for organizations and society at large. In the last decade, for example, smartphones have been gradually transforming their scope from just voice communications providers to powerful media and computing devices. This transformation has led to a growth in business initiatives, namely in the services provision through mobile applications. In this context, higher education as an organization and part of society is compelled to change its operant mode in general and in particular in the teaching-learning process making it more digital and at the same time more motivating for students. The aim of this study is to investigate the perception that higher education professors in the area of technology, across Southern Europe, South America, and Asia have about the use of mobile technologies such as gamification and augmented reality and how they can be used to promote student’s engagement inside and outside of the classroom.
Development of research skills in the future foreign language teacher in the system of continuing education

Modern education, implemented in specially targeted institutions, in most cases cannot ensure high performance of graduates. A future teacher should possess not only a high level of qualifications but also creative thinking, the ability to use innovative methods and technologies. The improvement of the integrated abilities of future teachers is based on the appropriate competent combination of the Russian educational tradition with innovative types of education. University programs usually only follow the state educational standards, without contributing to the individual self-identification of young citizens. Today, education should be positioned as a daily intellectual work that reveals the potential of a schoolchild or student and contributes to building the required competencies. Consequently, non-formal education should become part of a teacher’s life, implying both personal cognitive activity and spontaneous education, carried out due to a personal activity in a rapidly developing educational space. The student should not only know a foreign language and master teaching methods, but also demonstrate an adequate intellectual level, the ability to reflect, to conduct scientific research, to predict learning outcomes. Unfortunately, at this stage, the potential of non-formal education in the field of foreign languages is not sufficiently used, which makes it difficult to improve the research skills of future school teachers.
The Impact of Zeroconf on the Education System of the Countries in Conflict: The Case of the Central African Republic

This article proposes a solution to extend the functionalities of zeroconf outside of a local network to enable young people in conflict areas to benefit from quality distance learning. This proposal, based on a judicious choice of a VPN mode, allows distant teachers and learners in conflict areas to work as if they were face to face. To support the e-learning service, in this article a broadband network built on the technology of free spaces of television (TVWS) transmission channels is set up. This e-learning solution, using zeroconf protocol, gives the various actors the opportunity to collaborate on practical work in real time; it also helps to encourage the resumption of educational activities of refugees young students in conflict areas to fight against their attempts to integrate armed groups for lack of future prospects.