Miracle-dz/UniFAR-FTUs huggingface dataset:
cs . C L ] 1 9 Ju l 2 01 9 Exploring sentence informativeness

This study is a preliminary exploration of the concept of informativeness –how much information a sentence gives about a word it contains– and its potential benefits to building quality word representations from scarce data. We propose several sentence-level classifiers to predict informativeness, and we perform a manual annotation on a set of sentences. We conclude that these two measures correspond to different notions of informativeness. However, our experiments show that using the classifiers’ predictions to train word embeddings has an impact on embedding quality. MOTS-CLÉS : Informativité, Plongements de mots, Classification de phrases, Labellisation.
MEDIA RELATION BASED AUTOMATIC CONTENT GENERATION METHOD ON SOCIAL P 2 P NETWORK

Existing social media services are aimed at text based messages and information sharing. Recently, changes in the trend of sharing visual content using a social media service. Users may utilize various visual contents in the social group they belong, can create a new content for their interests. In this paper, we propose a method to generate a media content automatically using information of media relation. We also design the MC (Media Container) for describing relationship among the media contents, and present a mechanism for combing media contents on social P2P network. The characteristics of social media, new content will continue to be generated, deleted and modified. Thus, the automatically generated media content does not reflect the newly added content for the same interest. In order to supplement these problems, we define the MC scheme to describe information of relationship among the contents using XML format, present the method for dynamically generating media contents with the latest contents which are reflected the user's interest. And through the development of the proto-type application, verify the method for automatic content generation using the MC is defined in this paper.
LTRT : An Ef fi cient and Reliable Topology Control Algorithm for Ad-Hoc Networks

Broadcasting, in the context of ad-hoc networks, is a costly operation, and thus topology control has been proposed to achieve efficient broadcasting with low interference and low energy consumption. By topology control, each node optimizes its transmission power by maintaining network connectivity in a localized manner. Local Minimum Spanning Tree (LMST) is the state-of-the-art topology control algorithm, which has been proven to provide satisfactory performance. However, LMST almost always results in a 1-connected network, without redundancy to tolerate external factors. In this paper, we propose Local Tree-based Reliable Topology (LTRT), which is mathematically proven to guarantee k-edge connectivity while preserving the features of LMST. LTRT can be easily constructed with a low computational complexity of O(k(m + n log n)), where k is the connectivity of the resulting topology, n is the number of neighboring nodes, and m is the number of edges. Simulation results have demonstrated the efficiency of LTRT and its superiority over other localized algorithms.
Mathematical Model based on Game Theory and Markov Chains for Analysing the Transmission Cost in SA-ZD Mechanism

In Slotted ALOHA (SA), mobile nodes tend to be more aggressive when they compete to get access to the common channel, which leads to a dramatic decreases of the Quality of Service (QoS). In this paper, we analyze the Slotted ALOHA and Slotted ALOHA combined with ZigZag Decoding (SA-ZD) within a stochastic game framework. First, we start with the cooperative model of the proposed mechanism in which we propose several approach to optimize the system. Then, we model the system by a non-cooperative game using a bi-dimensional Markov chain that integrates the effect of ZigZag Decoding (ZD). We then propose an adjustable transmission cost to compromise between throughput and the delay of backlogged packets. All found results show that our approach improves significantly the QoS of the system.
PENDETEKSIAN OBJEK MENGGUNAKAN ALGORITMA HSV BERBASIS RASPBERRY PI 3B

Many technologies using digital image processing (digital image processing) developed lately, this is due to ease of use and supported with adequate computer skills to perform computing. In this research, we made an object detector using HSV alogarithm using Raspberry PI 3B as a data processor. From our research, it was found that HSV method can detect and track objects in any distance, as long as it is still visible to the camera, so the resolution or quality of the camera plays an important role in digital image processing using this HSV method.
A Convolutional Neural Networks Approach to Devise Controller

PID controller is widely used in many fields. The input sequence and output sequence of a well-parameterized PID controller are transformed into a matrix presented in images in this paper, and a Every-Time step data augmentation algorithm is operated. This paper propose a image to image Convolutional Neural Networks(CNN) structure to learn the PID controller, which incorporates the successful methods in computer vision and deep learning to control field. The simulation is performed using Matlab/Simulink and Keras[1]. The simulation demonstrates that the CNN controller is capable in performance.
Strategies for Seed Placement and Streamline Selection

Flow visualization is a vital component in the workflow of studying computational fluid dynamic simulations. Integral curves or streamlines are one of the most commonly used techniques to visualize flow fields and selecting a good set of streamlines is viewed as a challenge. Identifying a representative set of streamlines that captures the flow behavior can be achieved by either strategically placing seed points or selecting a subset of precomputed streamlines that exhibit desired properties. Over the past two decades, as desired characteristics and hardware resources have evolved, the approaches to solving the challenge have greatly varied. Primarily automatic techniques, these algorithms can be classified into density-based, feature-based, and similarity-based methods. In this report, we further subdivide each category to identify the strategies adopted by various researchers. We identified a total of 18 automatic technique strategies and 2 manual technique strategies. In addition to the qualitative and quantitative comparisons established by these works, we evaluate the strategies based on criteria specifically relevant to us, i.e., redundancy, regions of interest, and computation. We believe our classification and categorization reporting benefits future research endeavors with regard to in situ flow visualization techniques where seed placement and streamline selection will be key to successful flow exploration.
Light funneling from a photonic crystal laser cavity to an optical nano-antenna: toward antenna-based laser nano-emission

We show that the near-field coupling between a photonic crystal microlaser and a nanoantenna can enable hybrid photonic systems that are both physically compact and highly efficient at transferring optical energy into the nano-antenna. Up to 19.4% of the laser power from a micron-scale photonic crystal laser cavity is experimentally transferred to a bowtie aperture nano-antenna (BNA) whose area is 400-fold smaller than the overall emission area of the microlaser. Instead of a direct deposition of the nano-antenna onto the photonic crystal, it is fabricated at the apex of a fiber tip to be accurately placed in the near-field volume of the microlaser. Such light funneling within a hybrid structure provides a path for overcoming the diffraction limit in optical energy transfer to the nanoscale and should thus open promising avenues in the nanoscale enhancement
Gbps optical soliton transmission link using in-line SOA on standard SMF at 1 . 3 l m

In this paper, 10Gbps optical soliton transmission link using in-line semiconductor optical amplifiers (SOAs) for already installed standard single mode fibers (SMF) at 1.3 mm wavelength has been reported. The pattern effect and the impact of chirp on pulse propagation after amplification have been investigated. The observations are based on modeling and simulation optical soliton transmission link. Optical soliton pulse transmission over distances of the order of several hundreds of kilometers has been shown with and without initial chirp. r 2006 Elsevier GmbH. All rights reserved.
MY PC HAS 32,539 ERRORS: HOW TELEPHONE SUPPORT SCAMS REALLY WORK

Fake security products, pushed by variations on black hat SEO and social media spam, constitute a highly adaptive, longstanding and well-documented area of cybercriminal activity. By comparison, lo-tech Windows support scams receive far less attention from the security industry, probably because they’re seen as primarily social engineering and not really susceptible to a technical ‘anti-scammer’ solution. Yet they’ve been a consistent source of fraudulent income for some time, and have quietly increased in sophistication. In this paper, we consider: 1. The evolution of the FUD and Blunder approach to cold-calling support scams, from ‘Microsoft told us you have a virus’ to more technically sophisticated hooks such as deliberate misinterpretation of output from system utilities such as Event Viewer and ASSOC.
Supplementary Materials for Incremental Object Learning from Contiguous Views — Appendix

Lighting: Light sources available in CRIB are three rod shaped light sources and four point light sources. These are implemented using Blender’s built in Lamp object, using type Point for point sources and Area for rod sources. After the choice of rod or point is made, the specified location and rotation (if rod light source) are applied. The color temperature of the light is implemented by using Blender’s Blackbody shader and is applied. Object motion: The user specifies the total number of frames, n (length of the sequence) and number of points on the viewing sphere p. A set of p key frames of specified or random (azimuth, elevation, tilt, scale) are inserted at random points between 0 and n. Note that scale here is changed from the object’s initial scale. Once these keyframes are specified, linear interpolation is done using Blender’s animation tools to generate the object motion, rotating around its estimated center of mass. Preprocessing: For the collected Toys-200 automatically preprocessing the geometry is straightforward since their geometry was manually cleaned up and joined when they were collected. Manual processing of a model downloaded from Blendswap often involves removing irrelevant geometry from the Blender scene (anchor points, other objects) and joining all the separate geometry forming an object into a single Blender object using tools built into Blender. Importing ShapeNet objects in Blender objects results in importing all the edges and faces as individual objects. We implement code that can appropriately join this geometry into a single object for ShapeNet automatically. For all objects the center of mass is estimated using built in tools based on the shape of the object. Since the object is rotating and changing in scale, we need to make sure that during the motion it will not leave the camera field of view.
Modeling Solar Irradiance for Short-term Dynamic Analysis of Power Systems

This paper presents and compares four solar irradiance models for short-term power system analysis. Three of these models can be found in the literature but are based on approximations and shortcomings, which are duly discussed. The fourth model is novel and is formulated through stochastic differential equations with jumps. The case study illustrates the ability of the proposed solar irradiance model to generate synthetic processes that reproduce the stochastic properties of flickering events taken from measurement data.
ffi cient Parallel and Incremental Graph Connectivity

On an evolving graph that is continuously updated by a high-velocity stream of edges, how can one efficiently maintain if two vertices are connected? This is the connectivity problem, a fundamental and widely studied problem on graphs. We present the first shared-memory parallel algorithm for incremental graph connectivity that is both provably work-efficient and has polylogarithmic parallel depth. We also present a simpler algorithm with slightly worse theoretical properties, but which is easier to implement and has good practical performance. Our experiments show a throughput of hundreds of millions of edges per second on a 20-core machine.
PROTECTED RELIABLE ROUTING FOR MANET USING BEES ALGORITHM

To maintain the information that is required to properly route the traffic is a key issue in MANET. Among the available routing techniques trust based routing techniques seem to be better. In trust based routing technique, security threats that occur during routing as well as network initialization is a left out problem. In this proposed scheme Protected Reliable Routing (PRR) ensures security in all expected parameters. Node formation is done by two way secured encrypted techniques which double checks for false node by ECDSA in case of multicast communication and HMAC embedded with MD5 in case of unicast communication and then allocates IP for new node. In routing to avoid delay,Bees algorithm is being used to choose the optimized path from source to sink based on the higher objective value and then source node gathers recommendation from neighbour node, analyse them by calculating confidence value and response value. The result thus obtained is again handpicked based on high communication rate to have a better knowledge about destination. Efficiency of PRR is shown by comparing it with existing techniques in terms of overhead, packet delivery ratio, packet loss and throughput. Analysis graph highlights that PRR is highly optimized with better results.
End-To-End Interpretable Neural Motion Planner

In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. We then sample a set of diverse physically possible trajectories and choose the one with the minimum learned cost. Importantly, our cost volume is able to naturally capture multi-modality. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America. Our experiments show that the learned cost volume can generate safer planning than all the baselines.
Homelessness in Rural Pennsylvania EXECUTIVE SUMMARY

This research analyzed existing data on rural homelessness, surveyed professionals who work with the homeless, and summarized information gathered at two homelessness summits to develop a better understanding of homelessness and provide an overview of existing patterns of rural homelessness. During their analyses, the researchers identified concerns about data quality, particularly data on rural communities, and indicated that existing data likely significantly undercount the rural homeless. From the data that were available, the research showed clear differences related to the homeless in rural and urban areas and an increase in the number of homeless counted in rural areas. The research also indicated that the rural homeless exhibit high rates of health and substance abuse problems and receive services that emphasize prevention and nonresidential interventions. The research also found regional differences between the rural parts of the state both overall and in regard to the rates of homelessness among veterans and the chronically homeless. The survey results indicated a rise in homelessness in the past 5 years, particularly in rural counties. These results corresponded with the data patterns at the state and regional levels. The survey results also indicated slightly higher rates of mental health and substance abuse issues among rural clients. The survey respondents were concerned about the inconsistencies in the definitions of homelessness used by different agencies, organizations, and programs, and with the definitions that exclude clients who are living temporarily with family or friends. Finally, the survey respondents overwhelmingly reported that their own organizations keep accurate records on the homeless they encounter; however, the respondents also reported a wide range of record keeping formats and procedures. The research underscores the point that inconsistency in data collection methods represents a lost opportunity to use these large amounts of data to help increase the understanding of and the ability to track homelessness at the state and local levels. Information from the homelessness summits confirmed the data weaknesses identified in the research. This information also supported evidence in the rural homelessness literature about problems associated with the lack of awareness of homelessness and the challenges associated with service delivery in rural areas. Attendees suggested that increased dialogue among service providers could be helpful in developing realistic solutions to service delivery that take into account the specific challenges associated with rural communities. Based on the research findings, the researchers provided a number of recommendations that include the following: • Develop a standard definition of homelessness that includes those who are doubled up; • Consolidate state-level data collection under one methodology and Homeless Management Information System; and • Develop data collection strategies specifically designed for rural areas.
Meta-Teaching : Curriculum Generation for Lifelong Learning

Meta-learning will be crucial to creating lifelong, generalizable AI. In practice, however, it is hard to define the meta-training task distribution that is used to train meta-learners. If made too small, tasks are too similar for a model to meaningfully generalize. If made too large, generalization becomes incredibly difficult. We argue that both problems can be alleviated by introducing a teacher model that controls the sequence of tasks that a meta-learner is trained on. This teacher model is incentivized to start the student metalearner on simple tasks then adaptively increase task difficulty in response to student progress. While this approach has been previously studied in curriculum generation, our main contribution is in extending it to meta-learning.
Unsupervised learning of cross-modal mappings between speech and text

Deep learning is one of the most prominent machine learning techniques nowadays, being the state-of-the-art on a broad range of applications in computer vision, natural language processing, and speech and audio processing. Current deep learning models, however, rely on significant amounts of supervision for training to achieve exceptional performance. For example, commercial speech recognition systems are usually trained on tens of thousands of hours of annotated data, which take the form of audio paired with transcriptions for training acoustic models, collections of text for training language models, and (possibly) linguist-crafted lexicons mapping words to their pronunciations. The immense cost of collecting these resources makes applying state-of-the-art speech recognition algorithm to under-resourced languages infeasible. In this thesis, we propose a general framework for mapping sequences between speech and text. Each component in this framework can be trained without any labeled data so the entire framework is unsupervised. We first propose a novel neural architecture that learns to represent a spoken word in an unlabeled speech corpus as an embedding vector in a latent space, in which word semantics and relationships between words are captured. In parallel, we train another latent space that captures similar information about written words using a corpus of unannotated text. By exploiting the geometrical properties exhibited in the speech and text embedding spaces, we develop an unsupervised learning algorithm that learns a cross-modal alignment between speech and text. As an example application of the learned alignment, we develop a unsupervised speech-to-text translation system using only unlabeled speech and text corpora. Thesis Supervisor: James R. Glass Title: Senior Research Scientist in Computer Science
KOMPLEXNÍ LÉâBA KARCINOMU JÍCNU COMPLEX THERAPY OF ESOPHAGEAL CANCER

Úvod Incidence karcinomu jícnu stoupá, v âRje v souãasné dobû pfiibliÏnû 400 nov ̆ch pfiípadÛ roãnû, a za posledních 20 let se zdvojnásobila (1). Podíl muÏÛ a Ïen je 5:1. Úmrtnost je pfiibliÏnû 90% roãní incidence, dlouhodobû pfieÏívá jen 5% nemocn ̆ch, hor‰í prognózu má jen karcinom jater, pleury a pankreatu (2). 50% nemocn ̆ch má vzdálené metastázy jiÏ v dobû stanovení diagnózy a u zb ̆vajících 50% s lokalizovan ̆m onemocnûním se metastázy objeví u velké vût‰iny v dal‰ím prÛbûhu choroby. U muÏÛ v âR je karcinom jícnu na desátém místû v úmrtnosti na zhoubn ̆ nádor (1). V posledních letech se zvy‰uje podíl adenokarcinomÛ a lokalizací v distálním jícnu, v západní Evropû a USA pfiesahuje podíl adenokarcinomÛ v publikovan ̆ch souborech 50%. U nás je tento trend zatím ménû nápadn ̆ a statistické údaje nejsou zvefiejÀovány v roãence ZN, ale podle zdrojÛ onkologického registru také v âR podíl adenokarcinomu v incidenci nádorÛ jícnu vzrostl – z 5% v roce 1985 na 17 % v roce 2000 (3). Identifikované rizikové faktory pro dlaÏdicové karcinomy tabák, alkohol jsou zcela jiné, neÏ rizikové faktory pro rozvoj adenokarcinomÛ. Se zv ̆‰en ̆m rizikem vzniku adenokarcinomu je spojena pfiítomnost Barretova jícnu a pfievaÏující pfiíãinou Barretovy metaplasie je chronick ̆ reflux, jehoÏ vznik je spojován se zv ̆‰enou aciditou po eradikaci H.pylori a téÏ s narÛstající obesitou populace (4, 59). Chirurgická léãba Resekce jícnu je standardním léãebn ̆m postupem pro nemocné s operabilním nálezem, s dlouhodob ̆m pfieÏíváním do 20%. V ̆sledky chirurgické léãby se v posledních letech zlep‰ily, ale tento pokrok je ovlivnûn více neÏ zlep‰ením úãinností chirurgické terapie samotné spí‰e pokroky v pfiedoperaãním stagingu, v ̆bûrem pacientÛ a pooperaãní péãí s poklesem pooperaãní úmrtnosti pod 5% (60). Druh operace (transtorakální versus transhiátová resekce) nebo její rozsah ve smyslu radikální lymfadenektomie mediastinálních, krãních a celiakálních uzlin nemá vliv na délku pfieÏití, i kdyÏ v ̆znam roz‰ífiené lymfadenektomie se stále studuje (5). Jednoznaãn ̆ vliv na v ̆sledky chirurgické léãby má zku‰enost chirurga – nejdel‰í pfieÏívání a nejniÏ‰í pooperaãní mortalita je zji‰Èována v centrech s vysok ̆m poãtem proveden ̆ch resekcí (61). Omezená úspû‰nost chirurgické léãby je dána povahou onemocnûní, zejména tûsnou souvislostí jícnu s mediastinálními orgány, která limituje radikalitu zdánlivû resekabilních pfiípadÛ hlavnû v krãním a v horním hrudním úseku (62). Bohaté lymfatické zásobení vede k ãasnému metastazování do regionálních uzlin, v dobû operace je pozitivita uzlin aÏ 80%, a velmi ãasto je pfiítomn ̆ mikrometastatick ̆ rozsev. To v‰e je pfiíãinou recidiv, aÈ lokoregionálních nebo vzdálen ̆ch. Snaha zlep‰it léãebné v ̆sledky vede k zavádûní dal‰ích léãebn ̆ch modalit kromû operace. KOMPLEXNÍ LÉâBA KARCINOMU JÍCNU