Reviewerly/exHarmony huggingface dataset:
Research on image recognition method of bank financing bill based on binary tree decision

Financial paper is a note without reason debt or consideration acceptance, issued for obtaining money financing. Financial paper identification system is a hot issue of the current file analysis and identification system, it covers paper classification, image processing, character segmentation and identification, file image compression and other series of processes. A research on multiple aspects of financial paper identification system is made in this paper. On which basis, a financial paper identification system with applied value is established. Through substantive experimental test and practical application, the method has better classification performance and higher processing efficiency, and has been used in bank bill identification processing system in a large scale.
Multilevel Model for Video Object Segmentation Based on Supervision Optimization

In this work, we present a supervised object segmentation algorithm for unconstrained video. Instead of arbitrarily picking a few frames for manual labeling, as in many existing supervised methods, the proposed method selects frames in a more reasonable manner, called supervision optimization. For this, we formulate a principled objective function by inferring the propagation error from appearance and motion clues. After this, we construct a multilevel segmentation model, which consists of low-level and high-level features. On the low level, image pixels are used for a more accurate estimation of motion and segmentation. On the high level, image segments are considered for a more semantic classification of the foreground and background. By integrating these in one segmentation graph, the result can be further improved by leveraging the knowledge from both levels. In experiments, the proposed approach is evaluated by different measures, and the results on a benchmark demonstrate the effectiveness in comparison with other state-of-the-art algorithms.
Mining direct acyclic graphs to find frequent substructures — An experimental analysis on educational data

The number of undergraduate students joining universities in Brazil has largely grown in the recent years. However, the number of students who actually graduate remains low. Some studies show that this is due to a phenomenon called retention, consisting of a student taking more time to graduate than the minimum required by the program, which may lead to late graduation. Hence, identifying retention patterns in an undergraduate program may assist the universities in anticipating the entrance of qualified professionals in the job market, while lessening the students’ dropout rate. Undergraduate programs and grade reports can be represented by DAGs, in which each course (as a task to be accomplished by each student) is represented as a vertex, and relations between courses are represented as edges. This article proposes methods for mining DAGs using statistical analysis and Apriori-based concepts, to identify retention patterns in undergraduate programs. This work also presents an experimental analysis using real data from Fluminense Federal University, a Brazilian public higher education institution, for evaluating the methods.
A Study on Smart Electronics Voting Machine Using Face Recognition and Aadhar Verification with IOT

Voting is an important process in which people can choose their own leader for the government. The device which we use for the voting process is an electronic voting machine with highly secured steps such as having unique number aadhar card, biometric of face recognition with IOT. This had a secured database saved system. Voting was an important role in our democratic system as we have the right to select our leader for the government.
Toward Self-Driving Bicycles Using State-of-the-Art Deep Reinforcement Learning Algorithms

In this paper, we propose a controller for a bicycle using the DDPG (Deep Deterministic Policy Gradient) algorithm, which is a state-of-the-art deep reinforcement learning algorithm. We use a reward function and a deep neural network to build the controller. By using the proposed controller, a bicycle can not only be stably balanced but also travel to any specified location. We confirm that the controller with DDPG shows better performance than the other baselines such as Normalized Advantage Function (NAF) and Proximal Policy Optimization (PPO). For the performance evaluation, we implemented the proposed algorithm in various settings such as fixed and random speed, start location, and destination location.
');DROP TABLE textbooks;--

In this position paper, we look at the representation of SQL injection within undergraduate database textbooks, and argue that both discussion of security issues and security of example code must be improved. SQL injection is a common database exploit which takes advantage of programs that incorrectly incorporate user input into SQL statements. Teaching students how to write parameterized SQL statements is key to preventing this wide-spread attack. We look at the current editions of seven textbooks used at the top 50 US CS programs, and analyze their coverage of SQL injection, use of parameterized queries, and correctness of examples. We find a wide variety in the amount of coverage given to the topic, from none at all to in-depth coverage of defenses. Additionally, we find cases of SQL injectable code given as examples of how to correctly write queries in two of seven textbooks.
Super-resolution imaging and field of view extension using a single camera with Risley prisms

A novel imaging method using Risley prisms is proposed to achieve super-resolution imaging and field of view (FOV) extension. The mathematical models are developed, and the solutions to sub-pixel imaging for super-resolution reconstruction are presented. Simulations show that the proposed method can enhance the image resolution up to optical diffraction limit of the optical system for imaging systems whose resolution is limited by pixel size. A prototype is developed. Experimental results show that the scene resolving capacity can be enhanced by 2.0 times with a resolution improvement factor of 4, and the FOV extension results accord with the simulations, providing a promising approach for super-resolution reconstruction, large FOV imaging, and foveated imaging with low cost and high efficiency.
Sequential and Parallel Solution-Biased Search for Subgraph Algorithms

The current state of the art in subgraph isomorphism solving involves using degree as a value-ordering heuristic to direct backtracking search. Such a search makes a heavy commitment to the first branching choice, which is often incorrect. To mitigate this, we introduce and evaluate a new approach, which we call “solution-biased search”. By combining a slightly-random value-ordering heuristic, rapid restarts, and nogood recording, we design an algorithm which instead uses degree to direct the proportion of search effort spent in different subproblems. This increases performance by two orders of magnitude on satisfiable instances, whilst not affecting performance on unsatisfiable instances. This algorithm can also be parallelised in a very simple but effective way: across both satisfiable and unsatisfiable instances, we get a further speedup of over thirty from thirty-six cores, and over one hundred from ten distributed-memory hosts. Finally, we show that solution-biased search is also suitable for optimisation problems, by using it to improve two maximum common induced subgraph algorithms.
A Hierarchical Mission Planning Method for Simultaneous Arrival of Multi-UAV Coalition

A hierarchical mission planning method was proposed to solve a simultaneous attack mission planning problem for multi-unmanned aerial vehicles (UAVs). The method consisted of three phases aiming to decouple and solve the mission planning problem. In the first phase, the Pythagorean hodograph (PH) curve was used in the path estimation process for each UAV, which also served as the input for the task allocation process. In the second phase, a task allocation algorithm based on a negotiation mechanism was proposed to assign the targets. Considering the resource requirement, time-dependent value of targets and resource consumption of UAVs, the proposed task allocation algorithm can generate a feasible allocation strategy and get the maximum system utility. In the last phase, a path planning method was proposed to generate a simultaneous arrival PH path for each UAV considering UAV’s kinematic constraint and collision avoidance with obstacles. The comparison simulations showed that the path estimation process using the PH curve and the proposed task allocation algorithm improved the system utility, and the hierarchical mission planning method has potential in a real mission.
Text Similarity Estimation Based on Word Embeddings and Matrix Norms for Targeted Marketing

Tim vor der Brück, Marc Pouly. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Context-Aware Text-Based Binary Image Stylization and Synthesis

In this work, we present a new framework for the stylization of text-based binary images. First, our method stylizes the stroke-based geometric shape like text, symbols and icons in the target binary image based on an input style image. Second, the composition of the stylized geometric shape and a background image is explored. To accomplish the task, we propose legibility-preserving structure and texture transfer algorithms, which progressively narrow the visual differences between the binary image and the style image. The stylization is then followed by a context-aware layout design algorithm, where cues for both seamlessness and aesthetics are employed to determine the optimal layout of the shape in the background. Given the layout, the binary image is seamlessly embedded into the background by texture synthesis under a context-aware boundary constraint. According to the contents of binary images, our method can be applied to many fields. We show that the proposed method is capable of addressing the unsupervised text stylization problem and is superior to state-of-the-art style transfer methods in automatic artistic typography creation. Besides, extensive experiments on various tasks, such as visual-textual presentation synthesis, icon/symbol rendering and structure-guided image inpainting, demonstrate the effectiveness of the proposed method.
Inferring Analyzable Models from Trajectories of Spatially-Distributed Internet of Things

Internet of things systems are increasingly common nowadays. They feature spatially-distributed, mobile entities with an arising collective behavior. Such entities bear radionavigation sensors that produce positioning information, then used by the (software-enabled) device to produce positioning information over time, referred to as trajectories. However, software applications built on top of this require composite models of space to be in place; such models can provide adaptive behaviors by observing, evaluating, and reacting to a constantly changing spatial environment. This is typically achieved by monitoring for changes, analyzing requirements violations and then planning and executing adequate countermeasures. We are concerned with the fact that model representations of space are highly pertinent to requirements reasoning of internet of things systems, and such spatial models can be very useful for engineering adaptation. To this end, we provide and implement a technique to infer analyzable models from general trajectories of spatially-distributed systems, which may be used for engineering analysis or planning facilities for the overall self-adaptive systems. Moreover, we illustrate how such spatial models are used for evaluation of requirements predicating about the structure of space, the spatial distribution of devices, temporal as well as quantitative aspects through formal spatio-temporal verification.
Deep Distribution Network

Existing recommendation methods mostly learn fixed vectors for users and items in a low-dimensional continuous space, and then calculate the popular dot-product to derive user-item distances. However, these methods suffer from two drawbacks: (1) the data sparsity issue prevents from learning high-quality representations; and (2) the dot-product violates the crucial triangular inequality and therefore, results in a sub-optimal performance. In this work, in order to overcome the two aforementioned drawbacks, we propose Deep Distribution Network (DDN) to model users and items via Gaussian distributions. We argue that, compared to fixed vectors, distribution-based representations are more powerful to characterize users' uncertain interests and items' distinct properties. In addition, we propose a Wasserstein-based loss, in which the critical triangular inequality can be satisfied. In experiments, we evaluate DDN and comparative models on standard datasets. It is shown that DDN significantly outperforms state-of-the-art models, demonstrating the advantages of the proposed distribution-based representations and wassertein loss.
STUFIIT at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter with MUSE and ELMo Embeddings

We evaluate the viability of multilingual learning for the task of hate speech detection.We also experiment with adversarial learning as a means of creating a multilingual model.Ultimately our multilingual models have had worse results than their monolignual counterparts.We find that the choice of word representations (word embeddings) is very crucial for deep learning as a simple switch between MUSE and ELMo embeddings has shown a 3-4% increase in accuracy.This also shows the importance of context when dealing with online content.
Joint Task-Recursive Learning for RGB-D Scene Understanding

RGB-D scene understanding under monocular camera is an emerging and challenging topic with many potential applications. In this paper, we propose a novel Task-Recursive Learning (TRL) framework to jointly and recurrently conduct three representative tasks therein containing depth estimation, surface normal prediction and semantic segmentation. TRL recursively refines the prediction results through a series of task-level interactions, where one-time cross-task interaction is abstracted as one network block of one time stage. In each stage, we serialize multiple tasks into a sequence and then recursively perform their interactions. To adaptively enhance counterpart patterns, we encapsulate interactions into a specific Task-Attentional Module (TAM) to mutually-boost the tasks from each other. Across stages, the historical experiences of previous states of tasks are selectively propagated into the next stages by using Feature-Selection unit (FS-Unit), which takes advantage of complementary information across tasks. The sequence of task-level interactions is also evolved along a coarse-to-fine scale space such that the required details may be refined progressively. Finally the task-abstracted sequence problem of multi-task prediction is framed into a recursive network. Extensive experiments on NYU-Depth v2 and SUN RGB-D datasets demonstrate that our method can recursively refines the results of the triple tasks and achieves state-of-the-art performance.
Content-based table retrieval for web queries

Understanding the connections between unstructured text and semi-structured table is an important yet neglected problem in natural language processing. In this work, we focus on content-based table retrieval. Given a query, the task is to find the most relevant table from a collection of tables. Further progress towards improving this area requires powerful models of semantic matching and richer training and evaluation resources. To remedy this, we present a ranking based approach, and implement both carefully designed features and neural network architectures to measure the relevance between a query and the content of a table. Furthermore, we release an open-domain dataset that includes 21,113 web queries for 273,816 tables. We conduct comprehensive experiments on both real world and synthetic datasets. Various evaluation criteria demonstrate that the proposed approach performs comparable or better than a carefully designed feature-based system. We show what depth of table and language understanding is required to do well on this task, and hope further interests from the community in exploring deeper connections between table and text.
Game Theory Meets Embeddings: a Unified Framework for Word Sense Disambiguation

Game-theoretic models, thanks to their intrinsic ability to exploit contextual information, have shown to be particularly suited for the Word Sense Disambiguation task.They represent ambiguous words as the players of a non-cooperative game and their senses as the strategies that the players can select in order to play the games.The interaction among the players is modeled with a weighted graph and the payoff as an embedding similarity function, which the players try to maximize.The impact of the word and sense embedding representations in the framework was tested and analyzed extensively: experiments on standard benchmarks show state-of-art performances and different tests hint at the usefulness of using disambiguation to obtain contextualized word representations.
Secure Observer-based Motion Control based on Controller Encryption

Disturbance observer-based motion control, one of effective schemes for force control and uncertainty compensation has been widely used in industrial systems such as manipulators and servo systems. Despite its effectiveness reported in the literature, security measures applicable to industrial equipment and motion control systems have not yet been established in comparison to those to information equipment. A lack of cybersecurity measures may result in critical incidents in the future. This study proposes a method to enhance the cybersecurity of an integral-type servo controller with a full-order state observer, or disturbance observer, for an industrial motion control system based on controller encryption. Feasibility of the controller encryption method was confirmed through position servo control experiments with a feed drive testbed. Influences on computational latency for real-time control were examined by varying the size of the key used for encryption. Performance degradation of the proposed encrypted control systems was millimeter order, and thus the controller encryption method can be applied as a security measure in motion control systems.
An expert system for predicting the velocity field in narrow open channel flows using self-adaptive extreme learning machines

This work investigates the ability of a new model based on powerful self-adaptive extreme learning machines to predict the velocity field in an open channel. A total of 363 velocity field data obtained in 8 different hydraulic conditions of a narrow open channel are used to develop the proposed model. The performance of the proposed model in predicting the velocity field is analysed for 3 different discharge rates that have no role in model development. According to the model prediction accuracy comparisons, the proposed model is more accurate than existing equations and can be employed successfully in velocity field predicting. Furthermore, the new model can more accurately predict the negative gradient of velocity near the free surface, which is the most significant/complex feature of a velocity distribution in narrow open channels. Moreover, a sensitivity analysis is done to surrey the effect of the proposed model on each input variable.
Evaluation of Pattern Recognition Methods for Head Gesture-Based Interface of a Virtual Reality Helmet Equipped with a Single IMU Sensor

The motivation of this paper is to examine the effectiveness of state-of-the-art and newly proposed motion capture pattern recognition methods in the task of head gesture classifications. The head gestures are designed for a user interface that utilizes a virtual reality helmet equipped with an.
Pre-trained Contextual Embedding of Source Code

The source code of a program not only serves as a formal description of anexecutable task, but it also serves to communicate developer intent in ahuman-readable form. To facilitate this, developers use meaningful identifiernames and natural-language documentation. This makes it possible tosuccessfully apply sequence-modeling approaches, shown to be effective innatural-language processing, to source code. A major advancement innatural-language understanding has been the use of pre-trained tokenembeddings; BERT and other works have further shown that pre-trained contextualembeddings can be extremely powerful and can be fine-tuned effectively for avariety of downstream supervised tasks. Inspired by these developments, wepresent the first attempt to replicate this success on source code. We curate amassive corpus of Python programs from GitHub to pre-train a BERT model, whichwe call Code Understanding BERT (CuBERT). We also pre-train Word2Vec embeddingson the same dataset. We create a benchmark of five classification tasks andcompare fine-tuned CuBERT against sequence models trained with and without theWord2Vec embeddings. Our results show that CuBERT outperforms the baselinemethods by a margin of 2.9-22%. We also show its superiority when fine-tunedwith smaller datasets, and over fewer epochs. We further evaluate CuBERT'seffectiveness on a joint classification, localization and repair task involvingprediction of two pointers.
Creating Meta Attack Language Instances using ArchiMate: Applied to Electric Power and Energy System Cases

Cyber-attacks on power assets can have disastrous consequences for individuals, regions, and whole nations. In order to respond to these threats, the assessment of power grids' and plants' cyber security can foster a higher degree of safety for the whole infrastructure dependent on power. Hitherto, we propose the use of attack simulations based on system architecture models. To reduce the effort of creating new attack graphs for each system of a given type, domain-specific attack languages may be employed. They codify common attack logics of the considered domain. Previously, MAL (the Meta Attack Language) was proposed, which serves as a framework to develop domain specific attack languages. We extend the tool set of MAL by developing an approach to model security domains in ArchiMate notation. Next, those models are used to create a MAL instance, which reflects the concepts modeled in ArchiMate. These instances serve as input to simulate attacks on certain systems. To show the applicability of our approach, we conduct two case studies in the power domain. On the one hand, we model a thermal power plant and possible attacks on it. On the other hand, we use the attack on the Ukrainian power grid for our case study.