Miracle-dz/UniFAR-FTUs huggingface dataset:
STORY SEGMENTATION USING DEEP NEURAL NETWORK

TV news programs usually contain multiple stories on different topics, and it is essential to locate the story boundaries for the purposes of video content indexing, search, and curation. With the exponential growth of video content, story segmentation will enable the consumers to view their favorite content effortlessly, and the service providers to provide their customers with personalized services. Given the dynamic range of topics, smooth story transitions in news, and varying duration of individual story, automated news story segmentation is a challenging task. This paper focuses on using linguistic information extracted from closed caption as the initial attempt to tackle this challenge, and our future work will integrate both audio and visual. A convolutional neural network framework with attention mechanism is proposed. The model is trained and tested on TDT2 data set, and it achieves an outstanding Fmeasure of 0.789 on the validation set and a reasonable Fmeasure of 0.707 on the testing set.
C HAPTER X A COMPLETE 3 D PERCEPTION AND PATH PLANNING ARCHITECTURE FOR A HUMANOID

If robots are going to share our living space, they should be able to move in clustered environments with reliability, avoiding fixed and moving obstacles. Smart environments are an easy solution to this matter (Pierro, 2012; Víctores, 2010; Coradeschi, 2006). The main objective of a smart environment is to reduce the complexity of a determined task and to help the robot to perform this task. First, by installing external cameras or sensors, a global and more accurate view of the environment can be perceived. It can help the robot to free computational resources by eliminating the need of doing SLAM. Also, a smarter high-level action planning can be performed by anticipating the location of fixed or moving obstacles. Furthermore, it reduces the dependence of the inner robot sensors.
Transferability of Sorghum Genic Microsatellite Markers to Peanut

Currently development of new marker types has shifted from anonymous DNA fragments to gene-based markers. Simple Sequence Repeats (SSRs) are useful DNA markers in plant genetic research including in peanut. However, de novo development of SSRs is expensive and time consuming. Gene-based DNA markers are transferable among related species owing to the conserved nature of genes. In this study transferability of sorghum EST-SSR (SbEST-SSR) markers to peanut was prospected. A set of 411 SbEST-SSR primer pairs were used to amplify peanut genomic DNA extracted from cultivated peanut where 39% of them successfully amplified. A comparison of amplification patterns between sorghum and peanut showed similar banding pattern with majority of transferable SbEST-SSRs. Among these transferable SSR markers, 14% have detected polymorphism among 4 resistant and 4 susceptible peanut lines for rust and late leaf spot diseases. These transferable markers will benefit peanut genome research by not only providing additional DNA markers for population genetic analyses, but also allowing comparative mapping to be possible between peanut and sorghum—a possible monocot-dicot comparison.
Keys-to-Sim Transferring Hand-Crafted Key-framed Animations to Simulated Figures using Wide Band Stochastic Trajectory Optimization

The vision of fully simulating characters and their environments has the potential to offer rich interactions between characters and objects in the virtual world. However, this introduces a challenging problem similar to controlling robotic ﬁgures: computing the necessary torques to perform a given task. In this paper, we address the problem of transferring hand-crafted kinematic motions to a fully simulated ﬁgure, by computing open-loop controls necessary to reproduce the target motion. One key ingredient to successful control is the mechanical feasibility of the target motion. While several methods have been successful at replicating human captured motion, there has not yet been a method capable of handling the case of artist-authored key-framed movements that can violate the laws of physics or go beyond the mechanical limits of the character. Due to the curse of dimensionality, sampling-based optimization methods typically restrict the search to a narrow band which limits exploration of feasible motions—resulting in a failure to reproduce the desired motion when a large deviation is required. In this paper, we solve this problem by combining a window-based breakdown of the controls on the temporal dimension, together with a global wide search strategy that keeps locally sub-optimal samples throughout the optimization.
Hand Gesture Recognition from Surveillance Video Using Surf Feature

— The Sign language is very important for people who have hearing and speaking deficiency. It is the only mode of communication for such people to convey their messages. In this paper, a feature detection scheme is introduced called SURF, which stands for Speeded Up Robust Features. The real time images will be captured first and then stored in directory and on recently captured image and feature extraction will take place to identify which sign has been articulated by the user through SURF algorithm. A Text to Speech conversion is also included in this project. An experimental result shows that the proposed approach performs well with low time.
Medication Non-Adherence in Chronic Mental Illness:Management Strategies

People who suffering from some mental problems can have some difficulty in treatment compliance. They can have difficulty remembering to take medication or appointment times. Unpleasant side effects of medication can also lead to people stopping medication, and a lack of insight into their illness can mean they do not see the need to follow treatments. Non-compliance with treatment can lead to poor health outcomes and even relapses and hospitalization. Several effective psychosocial interventions are currently available for the treatment of mental illness; these include family therapy, psychoeducational approaches, behavioral interventions, motivational interviewing, and cognitive approaches. There is no gold standard approach to the measurement of adherence as all methods have pros and cons. Successful forms of behavioral and cognitive interventions that improve medication adherence were those that: targeted and enhanced the therapeutic relationship; had a method for exploring the patient’s model of their disorder, including their beliefs and expectations; and employed concrete problem-solving techniques. These overlap, all have some evidence of effectiveness, and the intervention adopted should be tailored to the individual. Psychosocial interventions that utilize combined approaches seem more effective than unidimensional approaches.
arrow endemics to Mediterranean islands : Moderate genetic iversity but narrow climatic niche of the ancient , critically ndangered Naufraga ( Apiaceae )

Narrow endemics constitute the cornerstone of Mediterranean plant diversity. Naufraga balearica (Apiaceae) is a critically endangered, extremely narrow endemic plant from the western Mediterranean island of Majorca. Because the species belongs to a monotypic genus, N. balearica was hypothesized to be a palaeoendemism. Here we conducted phylogenetic dating, population genetic and climatic niche analyses in order to understand the evolutionary history and conservation perspectives of this flagship species. Phylogenetic dating analysis of nuclear and plastid DNA sequences revealed a late Miocene to early Pliocene divergence between Naufraga and its sister genus Apium, supporting the palaeoendemic status of the former. Amplified fragment length polymorphism (AFLP) markers and plastid DNA sequences of the five Naufraga populations revealed moderate genetic diversity. This diversity is in line with that of other palaeoendemisms from western Mediterranean islands, as revealed by a comparison with 22 other narrow endemic species from this region. Despite the fact that all Naufraga populations are located at a maximum distance of 10 km in a straight line, a strikingly strong population differentiation was found for AFLP markers, which is explained by long-term isolation likely related to short-range pollination and dispersal strategies of the species. While the species is not genetically impoverished, species distribution modelling and microclimatic monitoring revealed that narrow ecological requirements underlie the current extreme rarity of Naufraga and may jeopardize its long-term survival. Our results indicate that a multidisciplinary approach provides powerful tools to develop conservation strategies for evolutionarily singular lineages.
Sparse representation-based dictionary learning with CNN for image classification

In this paper, we propose a novel framework for image recognition based on a extended sparse model. First, inspired by the impressive results of CNN over different tasks in computer vision, we use the CNN models pre-trained on large datasets to extract features. Then we propose a extended sparse model which learns a dictionary for classification by incorporating the representation-constrained term and the coefficients incoherence term. With this learned dictionary, not only the representation residual but also the representation coefficients will be. Experiments on Caltech101 and PASCAL VOC 2012 datasets show the effectiveness of both our sparse model and our classification scheme on image classification.
Methodological Agreement between Body-Composition Methods in Young Soccer Players Stratified by Zinc Plasma Levels

Es factible pensar que la evaluacion de la composicion corporal puede estar influenciada por el estado de maduracion y los niveles plasmaticos de zinc, especialmente en atletas jovenes, que regularmente realizan un alto volumen de entrenamiento fisico. Por tanto, es importante aclarar el impacto de estos factores en la evaluacion de la composicion corporal de los atletas, ya que errores en su analisis pueden conducir al desarrollo de una equivocada prescripcion de entrenamiento, ademas de una dieta determinada, y por lo tanto afectar el rendimiento deportivo. El objetivo de esta investigacion consistio en: (1) comparar los diferentes metodos de evaluacion de la composicion corporal en futbolistas jovenes estratificados por los niveles plasmaticos de zinc; (2) comparar los dos metodos de referencia utilizando el espesor de los pliegues cutaneos en ninos (ecuaciones de Slaughter y Lohman), teniendo en cuenta el nivel de maduracion. En este estudio transversal, cincuenta futbolistas jovenes (13,3±0,7 anos) fueron sometidos a un perfil bioquimico de sangre, bioimpedancia electrica (BIA), absorciometria de rayos X de energia dual (DXA), medidas antropometricas (masa corporal, estatura y pliegues cutaneos de espesor (ST)) y radiografia de mano-muneca. La evaluacion de la composicion corporal se realizo por: DXA, ST (ecuaciones Lohman y Slaughter) y BIA (ecuacion Houtkooper). El nivel de zinc identifico dos grupos: deportistas normozincemicos e hipozincemicos, determinados por un punto de corte de 11,0 mmol/L. Se observo una diferencia significativa en los datos descriptivos de todos los participantes despues de la estratificacion del estado de zinc solo para la concentracion de zinc en plasma; se observaron correlaciones significativas entre los metodos de evaluacion (porcentaje de grasa: r= 0,34 a 0,98 y p <0,001 a 0,013; masa libre de grasa: r= desde 0,95 hasta 0,9998 y p <0,001), y disminuyeron las correlaciones al estar involucrada la impedancia electrica. Los resultados a traves de los metodos Bland y Altman mostraron un acuerdo mas cercano al comparar DXA y ST. El metodo ST fue mejor que el BIA para evaluar la composicion corporal (en los jugadores jovenes de futbol), cuando no estaban disponibles los escaneos DXA. La comparacion de los modelos basados en ST mostro que la mejor asociacion de valores DXA se obtuvieron para la ecuacion Slaughter, seguidos por la ecuacion Lohman utilizando la edad osea en lugar de la edad cronologica. Los niveles de zinc en plasma parecen no influir en la evaluacion de la composicion corporal, lo que amerita mas estudios.
VIRTUALIZATION-BASED MOBILE NETWORK ARCHITECTURE

The (r)evolution of wireless access infrastructure can be described as the convergence of the available radio communication systems towards a harmonized, more flexible and reconfigurable access system to match the current and upcoming demands. In recent years Softwarization and Virtualization technologies have moved from server and network domains to wireless domain and provides new perspectives of managing mobile networks functionalities. This paper provides evolution of the mobile network architecture in Software Defined Networking (SDN) and virtualization context and realizes it through the use of distribution of gateway function approach. Key improvements with proposed approach are to support efficient mobility management in heterogeneous access environments, remove the chains of IP preservation and optimal data path management according to application needs. A functional setup validates and assays the proposed evolution in terms of inter-system handover preparation, interruption and completion time relative to control plane delay requirements of the 5G networks.
ISPRS Journal of Photogrammetry and Remote Sensing

Supervoxels provide a more natural and compact representation of three dimensional point clouds, and enable the operations to be performed on regions rather than on the scattered points. Many state-of-the-art supervoxel segmentation methods adopt ﬁ xed resolution for each supervoxel, and rely on the initialization of seed points. As a result, they may not preserve well the boundaries of the point cloud with a non-uniform density. In this paper, we present a simple but e ﬀ ective supervoxel segmentation method for point clouds, which formalizes supervoxel segmentation as a subset selection problem. We develop an heuristic algorithm that utilizes local information to e ﬃ ciently solve the subset selection problem. The proposed method can produce supervoxels with adaptive resolutions, and dose not rely the selection of seed points. The method is fully tested on three publicly available point cloud segmentation benchmarks, which cover the major point cloud types. The experimental results show that compared with the state-of-the-art supervoxel segmentation methods, the supervoxels extracted using our method preserve the object boundaries and small structures more e ﬀ ectively, which is re ﬂ ected in a higher boundary recall and lower under-segmentation error.
Textbook Question Answering Under Instructor Guidance with Memory Networks

Textbook Question Answering (TQA) is a task to choose the most proper answers by reading a multi-modal context of abundant essays and images. TQA serves as a favorable test bed for visual and textual reasoning. However, most of the current methods are incapable of reasoning over the long contexts and images. To address this issue, we propose a novel approach of Instructor Guidance with Memory Networks (IGMN) which conducts the TQA task by finding contradictions between the candidate answers and their corresponding context. We build the Contradiction Entity-Relationship Graph (CERG) to extend the passage-level multi-modal contradictions to an essay level. The machine thus performs as an instructor to extract the essay-level contradictions as the Guidance. Afterwards, we exploit the memory networks to capture the information in the Guidance, and use the attention mechanisms to jointly reason over the global features of the multi-modal input. Extensive experiments demonstrate that our method outperforms the state-of-the-arts on the TQA dataset. The source code is available at https://github.com/freerailway/igmn.
A Review Paper on “Graphene Field Emission for Electron Microscopy”

Although good field emission from graphene has been demonstrated from a wide variety of different microfabricated structures, very few of them can be used to improve the design of cold field emitters for electron microscopy applications. Most of them consist of densely packed nano-emitters, which produce a large array of defocused overlapping electron beams, and therefore cannot be subsequently focused down to a single nanometer electron probe. This paper reviews the kind of single-tip cathode structures suitable in cold field emission guns for instruments such as scanning electron microscopy, transmission electron microscope or scanning transmission electron microscopy, and reviews progress in fabricating them from graphene-based materials.
Air flow humidification for air-assisted boom sprayer

The air-assisted ground spray is fairly widespread. However, due to the unpredictable weather conditions, the operational efficiency is impaired by stops on grounds of low humidity and high temperatures. The aim of this work was to assess an air humidification method and evaluate its impact on temperature and air humidity for the air curtain of the air-assisted sprayer. With respect to relative air humidity, it has increased in 6.59%, being the maximum change when inserting 1.92 L min -1 . So, it is concluded that the pipeline humidification might significantly reduce temperature and enhance air humidity. The treatments performed in this study consisted of a varied flow of a humidity device, related to weather conditions. Temperature and relative air humidity were measured at 1.0 m height from right to left of middle point of the machine, corresponding to the end of the spray boom, in the middle and end of right spray boom. The readings were also performed at three different distances from the end of the pipeline and at 0.25 and 0.50 m from that to the soil. The results show that 0.48 L min -1 in the humidification system has promoted a better efficiency in reducing air-temperature, on average 2.52 °C when compared to the non-humidified one.
OnQuerying for Safe Optimality in Factored Markov Decision Processes Extended Abstract

As it achieves a goal on behalf of its human user, an autonomous agent’s actions may have side effects that change features of its environment in ways that negatively surprise its user. An agent that can be trusted to operate safely should thus only change features the user has explicitly permitted. We formalize this problem, and develop a planning algorithm that avoids potentially negative side effects given what the agent knows about (un)changeable features. Further, we formulate a provably minimax-regret querying strategy for the agent to selectively ask the user about features that it hasn’t explicitly been told about. We empirically show how much faster it is than a more exhaustive approach and how much better its queries are than those found by the best known heuristic.
Subspace estimation and prediction methods for hidden Markov models

Hidden Markov models (HMMs) are probabilistic functions of finite Markov chains, or, put in other words, state space models with finite state space. In this paper, we examine subspace estimation methods for HMMs whose output lies a finite set as well. In particular, we study the geometric structure arising from the nonminimality of the linear state space representation of HMMs, and consistency of a subspace algorithm arising from a certain factorization of the singular value decomposition of the estimated linear prediction matrix. For this algorithm, we show that the estimates of the transition and emission probability matrices are consistent up to a similarity transformation, and that the m-step linear predictor computed from the estimated system matrices is consistent, i.e., converges to the true optimal linear m-step predictor.
An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor.
Semantically Rich, Context Aware Access Control for Openstack

In an open source cloud computing platform such as OpenStack, operators use Role-Based Access Control (RBAC) model to grant access to cloud resources. However, these user-level role-based access control techniques fail to include comprehensive user context. We believe a situational aware framework will improve security by bringing in user's context to such cloud systems. In this paper, we create a semantically rich context-sensitive access control system for OpenStack by incorporating the user's current context attributes like location, time, etc. In a proof-of-concept implementation, we integrate a knowledge graph with our own access control system to express and enforce the contextual-situation policies in OpenStack. The proposed system provides enhanced, flexible access control while minimizing the overhead of altering the existing access control framework. We also discuss various use cases, to highlight the benefits of our system and show enforcement results.
Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation

We propose PsiRec, a novel user preference propagation recommender that incorporates pseudo-implicit feedback for enriching the original sparse implicit feedback dataset. Three of the unique characteristics of PsiRec are: (i) it views user-item interactions as a bipartite graph and models pseudo-implicit feedback from this perspective; (ii) its random walks-based approach extracts graph structure information from this bipartite graph, toward estimating pseudo-implicit feedback; and (iii) it adopts a Skip-gram inspired measure of confidence in pseudo-implicit feedback that captures the pointwise mutual information between users and items. This pseudo-implicit feedback is ultimately incorporated into a new latent factor model to estimate user preference in cases of extreme sparsity. PsiRec results in improvements of 21.5% and 22.7% in terms of Precision@10 and Recall@10 over state-of-the-art Collaborative Denoising Auto-Encoders. Our implementation is available at https://github.com/heyunh2015/PsiRecICDM2018.
A New Approach for Creating Polymer Hydrogels with Regions of Distinct Chemical, Mechanical, and Optical Properties

We describe a new approach to creating hybrid polymer hydrogels that comprise two different gel types (gel 1 and gel 2) juxtaposed in predetermined zones or patterns and with the unique properties of each gel being retained. The key to our approach is to ensure that the viscosities of the pregel mixtures are high when they are brought into contact and subsequently polymerized; this limits the diffusion at gel/gel interfaces. The final gel appears as a single, homogeneous, transparent material with smooth, robust interfaces between the dissimilar zones. However, its hybrid nature is revealed by specific tests. In one example, we use the same monomer, N,N-dimethylacrylamide, for gels 1 and 2, but gel 1 is cross-linked by a chemical cross-linker while gel 2 is cross-linked by laponite nanoparticles. In this case, when the hybrid gel is immersed in a mixed solution of cationic and anionic dyes, gel 2 selectively absorbs the cationic dye due to the strong affinity of the nanoparticles in it for cationic specie.