Reviewerly/exHarmony huggingface dataset:
Self-Adaptive Embedding For Few-Shot Classification By Hierarchical Attention

Few-shot classification aims to learn a model that can generalize well to new classes-that are unseen in the training phase-with a small number of labeled instances. Many existing approaches learn a shared embedding function across various tasks to measure the similarities between support (train) and query (test) samples. However, the embeddings generated by these approaches fail to take into account the feature importance of different instances and the feature correlation between support and query samples in each task. To tackle this problem, we propose a novel Self-Adaptive Embedding approach (SAE) by introducing a hierarchical attention scheme. The major novelty of SAE lies in two folds. First, SAE can effectively capture the most discriminative features at the instance level, which significantly improves its performance on downstream classification tasks. Second, SAE can adaptively adjust the representations of support and query samples by considering the feature structures shared by them at the task level. Experiments demonstrate that SAE significantly outperforms existing state-of-the-art methods.
Design And Implementation Of Domestic News Collection System Based On Python

In the era of rapid development of the Internet, network media has become a new window for people to understand the outside world due to its fast speed and wide spread. News is a channel for people to know about Surrounding Information, but thousands of news are produced every day on the Internet. These news are needed or not in inside. How to efficiently and accurately obtain the news content we need from the website is a great need in people’s life. This system aims to collect news on specific websites and return it to users with concise and clear pages. Users can search specific keywords to select news that they are interested in so as to realize personalization for users. This system crawls and processes the domestic financial news content, which is convenient for people to process the information. In order to avoid duplication of information, the system has also implemented a self-defined deduplication rule. In the specific implementation, the system is written using Python in conjunction with the Scrapy framework and Django framework, which can simplify the system code to a certain extent. The practical value of this system lies in the timely, efficient and convenient access to domestic financial news that people care about, need and are interested in.
Reconstructing Part-Level 3D Models From a Single Image

Understanding an image with 3D representations has been an increasingly attractive topic in computer vision. The state-of the-art 3D reconstruction methods usually focus on the reconstruction of the holistic object, while missing important part information, which is crucial in robotic interaction and virtual reality applications. To solve this issue, we make the first attempt to reconstruct the 3D models with part-level representations in a unified framework. With the input of the singleview images, we first develop a feature enhancement encoder to incorporate discriminative local features into the feature representation. The local features are selected adaptively by a learnable local awareness module. Then the enhanced local features are fused with the global branch to form the 3D representations. We then develop a 3D part generator to decode the image priors to 3D parts with a 3D focal loss, which enables the representations of small parts. Experimental results indicate that our model generates reliable part-level structures while achieving state-of-the-art performance in object-level recovering.
E-Learning: Experience and New Challenges

The article is devoted to the e-learning development in Russian Federation. E-learning is seen as a product influenced by market regulatory mechanisms. The requirements for the structure and content of high-quality electronic courses, as well as to university professors as the creators of these products, are determined. Based on the experience of the National Research University "Moscow Power Engineering Institute", the authors analyzed the new challenges that students of educational programs using distance technologies face.
A history of the Groovy programming language

This paper describes the history of the Groovy programming language. At the time of Groovy’s inception, Java was a dominant programming language with a wealth of useful libraries. Despite this, it was perceived by some to be evolving slowing and to have shortcomings for scripting, rapid prototyping and when trying to write minimalistic code. Other languages seemed to be innovating faster than Java and, while overcoming some of Java’s shortcomings, used syntax that was less familiar to Java developers. Integration with Java libraries was also non-optimal. Groovy was created as a complementary language to Java—its dynamic counterpart. It would look and feel like Java but focus on extensibility and rapid innovation. Groovy would borrow ideas from dynamic languages like Ruby, Python and Smalltalk where needed to provide compelling JVM solutions for some of Java’s shortcomings. Groovy supported innovation through its runtime and compile-time metaprogramming capabilities. It supported simple operator overloading, had a flexible grammar and was extensible. These characteristics made it suitable for growing the language to have new commands (verbs) and properties (nouns) specific to a particular domain, a so called Domain Specific Language (DSL). While still intrinsically linked with Java, over time Groovy has evolved from a niche dynamic scripting language into a compelling mainstream language. After many years as a principally dynamically-typed language, a static nature was added to Groovy. Code could be statically type checked or when dynamic features weren’t needed, they could be turned off entirely for Java-like performance. A number of nuances to the static nature came about to support the style of coding used by Groovy developers. Many choices made by Groovy in its design, later appeared in other languages (Swift, C#, Kotlin, Ceylon, PHP, Ruby, Coffeescript, Scala, Frege, TypeScript and Java itself). This includes Groovy’s dangling closure, Groovy builders, null-safe navigation, the Elvis operator, ranges, the spaceship operator, and flow typing. For most languages, we don’t know to what extent Groovy played a part in their choices. We do know that Kotlin took inspiration from Groovy’s dangling closures, builder concept, default it parameter for closures, templates and interpolated strings, null-safe navigation and the Elvis operator. The leadership, governance and sponsorship arrangements of Groovy have evolved over time, but Groovy has always been a successful highly collaborative open source project driven more by the needs of the community than by a vision of a particular company or person.
Opinion Mining based complex polarity shift pattern handling for improved sentiment classification

The availability of rich volume of information on the web makes sentiment analysis an interesting, informative and impressive task. There is now an increasing desire to mine this information to gain new insights. But how well we extract value from this wealth of information is much important because identifying, accurately analyzing and extracting subjective information from the available text is not an easy task. Hence the purpose of this research work is to effectively handle complex polarity shift patterns such as negations, intensifiers, diminishers and contrast transitions that affect (sometimes, reverse) the polarity of the text for better classification of sentiments and nine classification techniques in supervised learning were used for binary sentiment classification. The most commonly used standard labelled dataset with movie reviews was used. The implementation was done in Python using the libraries of Natural Language Toolkit (NLTK) and Scikit-Learn (sklearn). Grid Search cross validation technique was also used to choose best parameters for each model while training so that the classifier could accurately predict the testing data. The accuracy of sentiment classification task of the original dataset was compared with the one after complex polarity shift pattern handling. The results of this study show an increase in the accuracy up to 3.5% for various classifiers after polarity shift pattern handling. Random Forest classifier which is an ensemble of classifiers tops the list with high accuracy among the other classifiers used.
Hail the Closest Driver on Roads: Privacy-Preserving Ride Matching in Online Ride Hailing Services

Online ride hailing (ORH) services enable a rider to request a driver to take him wherever he wants through a smartphone app on short notice. To use ORH services, users have to submit their ride information to the ORH service provider to make ride matching, such as pick-up/drop-off location. However, the submission of ride information may lead to the leakages of users’ privacy. In this paper, we focus on the issue of protecting the location information of both riders and drivers during ride matching and propose a privacy-preserving online ride matching scheme, called pRMatch. It enables an ORH service provider to find the closest available driver for an incoming rider over a city-scale road network, while protecting the location privacy of both riders and drivers against the ORH service provider and other unauthorized participants. In pRMatch, we compute the shortest road distance over encrypted data by using road network embedding and partially homomorphic encryption and further efficiently compare encrypted distances by using ciphertext packing and shuffling. The theoretical analysis and experimental results demonstrate that pRMatch is accurate and efficient, yet preserving users’ location privacy.
A Genetic Programming-Driven Data Fitting Method

Data fitting is the process of constructing a curve, or a set of mathematical functions, that has the best fit to a series of data points. Different with constructing a fitting model from same type of function, such as the polynomial model, we notice that a hybrid fitting model with multiple types of function may have a better fitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid fitting model depends on a reasonable combination of multiple functions and a set of effective parameters. That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fitting model construction approach. In this approach, the model is expressed by an improved tree coding expression and constructed through an evolution search process driven by the genetic programming. In order to verify the validity of generated hybrid fitting model, 6 prediction problems are chosen for experiment studies. The experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction accuracy and interpretability.
Comparison of Nonlinear Filtering Methods for Terrain Referenced Aircraft Navigation

Inertial Navigation Systems (INS) are the main part of the integrated navigation for most of the aerial vehicles. However, the accuracy of an inertial navigation solution decreases with time as the inertial instrument (e.g., gyroscope and accelerometer) errors are integrated through the navigation equations. Therefore, different aiding techniques are used to bound the drift in these systems. One of the commonly used techniques is the integration of INS with Global Navigation Satellite System (GNSS) signals. By means of this integration, the advantages of both technologies are combined to give a complete navigation solution. The need for Terrain Referenced Navigation (TRN) arises when these satellite based radio signals are unavailable. In recent years, research on the application of TRN to aerial vehicles has been increased rapidly with the developments in the accuracy of digital terrain elevation database (DTED). Since the land profile is inherently nonlinear, TRN becomes a nonlinear estimation problem. Because of the highly nonlinear problem, linear or linearized estimation techniques such as Kalman or Extended Kalman Filter (EKF) do not work properly for many terrain profiles. Hence, this paper focuses on nonlinear filtering techniques and presents the main principles of two different TRN methods. These methods will be compared and advantages of both methods will be presented. The first method is the Sequential Monte Carlo (SMC) technique namely the particle filter (PF) for dealing with nonlinearities and different types of probability distributions even multi-modal. PF is an approximate optimal filter on correct model and based on particle representation of probability density function. The second method is the Unscented Kalman Filter (UKF) based on the Unscented Transform (UT) of sigma points. The basic idea is to approximate the probability density function with deterministically selected and weighted small number of sigma points. Simulations with different inertial measurement units (IMUs), with different initial errors, over maps with various resolutions are performed and investigated. The performance of both nonlinear filtering algorithms will be presented through Monte Carlo simulations.
Moflowgan: Video Generation With Flow Guidance

In recent years, video generation has attracted a lot of attention in the computer vision community. Unlike image generation which only focuses on appearance, video generation requires modeling both content information and motion dynamics. In this work, we propose MoFlowGAN, which explicitly models motion dynamics by a content-motion decomposition architecture with an additional flow generator. The decomposition architecture models content and motion separately and is instantiated by a compact variant of BigGAN [1]. Besides, the flow generator generates optical flow directly based on high-level feature maps of adjacent frames as a strong supervision, hence the searching space of motion patterns is highly reduced. Our proposed MoFlowGAN achieves the state-of-the-art results on both MUG facial expression and UCF-101 datasets.
On the structure of cube tiling codes

Let S be a set of arbitrary objects, and let Sd={v1...vd:vi∈S}. A polybox code is a set V⊂Sd with the property that for every two words v,w∈V there is i∈[d] with vi′=wi, where a permutation s↦s′ of S is such that s′′=(s′)′=s and s′≠s. If |V|=2d, then V is called a cube tiling code. Cube tiling codes determine 2-periodic cube tilings of Rd or, equivalently, tilings of the flat torus Td={(x1,…,xd)(mod2):(x1,…,xd)∈Rd} by translates of the unit cube as well as r-perfect codes in Z4r+2d in the maximum metric. By a structural result, cube tiling codes for d=4 are enumerated. It is computed that there are 27,385 non-isomorphic cube tiling codes in dimension four, and the total number of such codes is equal to 17,794,836,080,455,680. Moreover, some procedure of passing from a cube tiling code to a cube tiling code in dimensions d≤5 is given.
Computing at the Edge: But, what Edge?

The traditional telecommunications business is evolving towards offering a richer set of services beyond basic connectivity, leveraging on network programmability and virtualization. A versatile execution environment is required, capable of running different workloads in different locations in the network. Cloud computing is the key paradigm that allows fostering this trending change. One interesting question to solve is to what extent those computing environments have to move towards the network edge. Some services can be enabled by environments with increased capillarity, while others can be implemented in environments with more relaxed constraints (e.g., in terms of latency). This paper explores this topic by differentiating service edge from physical network edge and proposing an architecture based on the ALTO server for assisting orchestration systems in discriminating the suitable environments for each service. We present a network-flow strategy for assigning services to infrastructure elements following those precepts, together with an initial scalability evaluation of the proposed assignment solution to show its feasibility.
Globally Optimal Relative Pose Estimation for Camera on a Selfie Stick

Taking selfies has become a photographic trend nowadays. We envision the emergence of the "video selfie" capturing a short continuous video clip (or burst photography) of the user, themselves. A selfie stick is usually used, whereby a camera is mounted on a stick for taking selfie photos. In this scenario, we observe that the camera typically goes through a special trajectory along a sphere surface. Motivated by this observation, in this work, we propose an efficient and globally optimal relative camera pose estimation between a pair of two images captured by a camera mounted on a selfie stick. We exploit the special geometric structure of the camera motion constrained by a selfie stick and define its motion as spherical joint motion. By the new parametrization and calibration scheme, we show that the pose estimation problem can be reduced to a 3-DoF (degrees of freedom) search problem, instead of a generic 6-DoF problem. This allows us to derive a fast branch-and-bound global optimization, which guarantees a global optimum. Thereby, we achieve efficient and robust estimation even in the presence of outliers. By experiments on both synthetic and real-world data, we validate the performance as well as the guaranteed optimality of the proposed method.
Securing IoT with Blockchain

In the course of the most recent decade Internet of Things has accelerated impacts in our day-to-day life by improving the traditional approaches. Blockchain provides a decentralized methodology which would dispense the centralized system, making a stronger system for gadgets to run on. The cryptographic calculations utilized by blockchain would make the information more private. This innovation is the missing connection to settle versatility, protection, and unwavering quality worries in the Internet-of-Things. The volume of data being collected by various IoT applications may vary and the amount of data a single block can hold is limited, which can be overcome by using a secure file system. IPFS provides a decentralized, secure file storage mechanism. In this the hash acts as an identifier for an individual file which in turn provides confidentiality for data. Blockchain along with IPFS gives a progressively secure approach for conveying information between the IoT gadgets that makes the communication much more stronger against threats.
Deep Self-Taught Graph Embedding Hashing With Pseudo Labels For Image Retrieval

It has always been a tricky task to generate image hashing function via deep learning without labels and allocate the relative distance between data through their features. Existing methods can complete this task and prevent the overfitting problem using shallow graph embedding technique. However, they only capture the first-order proximity. To address this problem, we design DSTGeH, a deep self-taught graph embedding hashing framework which learns hash function without labels for image retrieval. DSTGeH introduces deep graph embedding means to capture more complex topological relationships (the second-order proximity) on the graph and maps these relationships into pseudo labels, which enables an end-to-end hash model and helps recognize the samples outside the graph. We present the ablation studies and compare DSTGeH with the state-of-the-art label-free hashing algorithms. Extensive experiments show DSTGeH can achieve the best performances and produce an overwhelming advantage on multi-object datasets.
Decision Support for Mission-Centric Network Security Management

In this paper, we propose a decision support process that is designed to help network and security operators in understanding the complexity of a current security situation and decision making concerning ongoing cyber-attacks and threats. The process focuses on enterprise missions and uses a graph-based mission decomposition model that captures the missions, underlying hosts and services in the network, and functional and security requirements between them. Knowing the vulnerabilities and attacker’s position in the network, the process employs logical attack graphs and Bayesian network to infer the probability of the disruption of the confidentiality, integrity, and availability of the missions. Based on the probabilities of disruptions, the process suggests the most resilient mission configuration that would withstand the current security situation.
The ReCAP Project

Abstract Argumentation Machines search for arguments in natural language from information sources on the Web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. The recap project is part of the Priority Program ratio and aims at novel contributions to and confluence of methods from information retrieval, knowledge representation, as well as case-based reasoning for the development of future argumentation machines. In this paper we summarise recent research results from the project. In particular, a new German corpus of 100 semantically annotated argument graphs from the domain of education politics has been created and is made available to the argumentation research community. Further, we discuss a comprehensive investigation in finding arguments and argument graphs. We introduce a probabilistic ranking framework for argument retrieval, i.e. for finding good premises for a designated claim. For finding argument graphs, we developed methods for case-based argument retrieval considering the graph structure of an argument together with textual and ontology-based similarity measures applied to claims, premises, and argument schemes.
High-Speed And Accurate Scale Estimation For Visual Tracking With Gaussian Process Regression

Recent years have seen remarkable progress in the visual tracking domain. However, it remains a challenging task to estimate the scale of target efficiently and accurately. In this paper, we present a novel and high-performance scale estimation approach for tracking-by-detection framework. The proposed approach, named GPAS, formulates the scale estimation as a Gaussian process regression problem based on scale pyramid representation. In general, it enjoys the following there advantages. (i) Efficient. It only takes 2ms to estimate the scale of a target on a single CPU. (ii) Accurate. Without bells and whistles, its accuracy surpasses all previous hand-crafted features based scale estimation methods by large margins. (iii) Generic. It can be incorporated into any tracking-by-detection framework based trackers easily. Experiment results show that compared to the latest and classical scale estimation method, fDSST, our GPAS significantly improves the performance by 6.2% in mean distance precision, 8.9% in mean overlap precision, and 5.5% in mean AUC on 28 sequences of OTB2013 with significant scale variations.