Reviewerly/exHarmony huggingface dataset:
Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods

An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862.
ENTREPRENEURIAL ORIENTATION IN ROMANIAN HIGHER EDUCATION

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 9864-9872ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.2458Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Improved morphological filtering method for interferograms

This study proposes an improved interferogram filtering method with mathematical morphology. The algorithm firstly converts the interferogram in cosine and sine parts to preserve the continuity of phases, then adopts the improved adaptive line structure elements to run reconstruction process on two parts based on grey scale morphological opening and closing operations. In addition combine the two filtered parts to get the wrapped phases. Simulated and real-data experiments show that the proposed algorithm is more robust in filtering compared with other algorithms referred in this study.
The making of the Litkey Corpus, a richly annotated longitudinal corpus of German texts written by primary school children

To date, corpus and computational linguistic work on written language acquisition has mostly dealt with second language learners who have usually already mastered orthography acquisition in their first language.In this paper, we present the Litkey Corpus, a richly-annotated longitudinal corpus of written texts produced by primary school children in Germany from grades 2 to 4. The paper focuses on the (semi-)automatic annotation procedure at various linguistic levels, which include POS tags, features of the wordinternal structure (phonemes, syllables, morphemes) and key orthographic features of the target words as well as a categorization of spelling errors.Comprehensive evaluations show that high accuracy was achieved on all levels, making the Litkey Corpus a useful resource for corpus-based research on literacy acquisition of German primary school children and for developing NLP tools for educational purposes.
Researches on data encryption scheme based on CP-ASBE of cloud storage

The ciphertext-policy attribute-set based encryption (CP-ASBE) based on a single authorisation centre easily becomes the security bottleneck of the system. With the support of trusted measurement technology, a novel method of CP-ASBE based on multiple attribute authority (AA) is proposed to solve this problem, and an encryption scheme is designed for cloud storage, which includes data storage, data access, data encryption and trusted measurement scheme for AA. The security performance and time cost of the encryption scheme are simulated, and the results show that the scheme can improve the security of users' data in the cloud storage environment.
Exhaustive single bit fault analysis. A use case against Mbedtls and OpenSSL’s protection on ARM and Intel CPU

Faults in software implementations target both data and instructions at different locations. Bellcore attack is a well-known fault attack that is able to break CRT-RSA. In response, cryptographic libraries such as OpenSSL are designed with protection. In this paper, new faults locations are shown on OpenSSL implementation of the CRT-RSA signature running on Intel and ARM processors. Among those faults, one restores the Bellcore attack on the OpenSSL library despite a protection and another is a complete new fault that exploits the OpenSSL protection to get RSA private key. Quite surprisingly, one of the exhibited faults is, ironically enough, made possible because of the existence of such protection. Mbedtls library is also analyzed in this paper. A way to find all exploitable faults on monobit flip fault model is also detailed.
Adjusted Bounded Weighted Policy Learner

The Weighted Policy Learner (WPL) algorithm has been shown to converge to Nash Equilibria (NE) in several challenging environments with minimum knowledge. However, WPL has trouble converging to deterministic strategies, since the policy update rate approaches zero. We propose a new update rule that bounds this update rate such that, in pure NE games, the algorithm’s speed is not slowed down, while its behavior in stochastic NE games remains unchanged. We demonstrate our proposal’s behavior in several common game-theoretic environments (with stochastic and deterministic equilibrium policies), in complex maze-related games (where some actions dominate others in most states), against the original WPL as well as other state of the art algorithms. We draw conclusions over the benefits of our solution and its advantages.
CCGweb: a New Annotation Tool and a First Quadrilingual CCG Treebank

We present the first open-source graphical annotation tool for combinatory categorial grammar (CCG), and the first set of detailed guidelines for syntactic annotation with CCG, for four languages: English, German, Italian, and Dutch.We also release a parallel pilot CCG treebank based on these guidelines, with 4x100 adjudicated sentences, 10K singleannotator fully corrected sentences, and 82K single-annotator partially corrected sentences.
Boosting Causal Embeddings via Potential Verb-Mediated Causal Patterns

Existing approaches to causal embeddings rely heavily on hand-crafted high-precision causal patterns, leading to limited coverage. To solve this problem, this paper proposes a method to boost causal embeddings by exploring potential verb-mediated causal patterns. It first constructs a seed set of causal word pairs, then uses them as supervision to characterize the causal strengths of extracted verb-mediated patterns, and finally exploits the weighted extractions by those verb-mediated patterns in the construction of boosted causal embeddings. Experimental results have shown that the boosted causal embeddings outperform several state-of-the-arts significantly on both English and Chinese. As by-products, the top-ranked patterns coincide with human intuition about causality.
High-Resolution Driving Scene Synthesis Using Stacked Conditional Gans and Spectral Normalization

Large-scale dataset plays a key role in the driving scene understanding for deep learning based-autonomous driving tasks. Due to the fact that the annotation for a large number of images is extremely labor-intensive and time-consuming, many researchers turn to using image-synthesis techniques for automatic construction of training data. However, traditional methods often have difficulties in producing high-definition driving scene images. To tackle this problem, in this paper, we propose a novel deep model - hdCGAN - for high-definition image-to-image translation. The hdCGAN is built on a conditional GAN in combination with a spectral normalization. Moreover, we improve the hdCGAN by using a stacked network architecture and the enhanced model is called stack-hdCGAN. With the guidance of multi-scale discriminators and the constraint of spectral normalization in the training procedure, the learned models can generate high-resolution and high-quality driving scene images from corresponding semantic segmentation maps. Quantitative and qualitative evaluations on the Cityscapes dataset demonstrate the effectiveness of the proposed models.
An α-factor architecture for RS decoder implemented on 90 nm CMOS technology for computer computing applications devices

In Computing devices in the world wide, where number of computing and communication systems are increased rapidly. In the channel noise has greater impact on the messages or data transmitted through the wired/wireless network. The Reed–Solomon (RS) decoder plays a vital role in removing the error from the received messages or data. The RS decoder has four main blocks, namely, Syndrome Computation (SC) block, Key Equation Solver (KES) block, Chien Search (CS) block and Forney Algorithm (FA) block. The researchers have thrown a number of works on each and every block of RS decoder. Moreover, the parallel and pipelined RS decoder shows a greater improvement in terms of gate element, latency, coding gain and throughput. Although this architecture has higher performance gain, the computation complexity in the SC block is not addressed. This paper presents an efficient architecture to compute the α-factor of SC blocks in RS decoder. The proposed RS decoder is developed using Verilog HDL (Hardware Description Language) and synthesized in Synopsys Design Compiler (DC). The proposed architecture is implemented in 90 nm CMOS technology and the results are evaluated in terms of gate count, clock rate, latency and throughput. The evaluated results of the proposed decoder show a remarkable improvement when compared with a conventional RS decoder.
On Retrieval of Nearly Identical Video Clips with Query Frame

Video clips retrieval from a video database is a challenging problem for researchers because of voluminous data. Video Clips retrieval with query frame finds the video clips that have similar frames to query frame. Applying matching to all frames of any video clips is time consuming and computationally complex for database containing of several large size video clips. In the present article, we present a three level near identical video clips retrieval algorithm. In the first step, we identify keyframes of video clips in database through establishment of shot transition boundaries and then on keyframes of video clips we apply Integration of Curvelet transform and Simple Linear Iterative Clustering algorithm (SLIC) to get segmented query frames. In the second step, query frame will undergo Integration of Curvelet transform and Simple Linear Iterative Clustering algorithm to get segmented query frames. Now in the third step, the segmentation results of query frame are searched to obtain matched result in database. The method is experimented on 25 different video clips belonging to the categories-animations, serials, personal interviews, news and movies. Performance of the method is assessed with the parameters Precision, Recall, F-measure, Accuracy, Detection Percentage, Jaccard Criteria, and Missing Factor. The furnished performance results have shown sound performance of the proposed method over the other state-of-art methods.
Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images

In this paper, we explore the problem of training one-look regression models for counting objects in datasets comprising a small number of high-resolution, variable-shaped images. We illustrate that conventional global average pooling (GAP) based models are unreliable due to the patchwise cancellation of true overestimates and underestimates for patchwise inference. To overcome this limitation and reduce overfitting caused by the training on full-resolution images, we propose to employ global sum pooling (GSP) instead of GAP or fully connected (FC) layers at the backend of a convolutional network. Although computationally equivalent to GAP, we show through comprehensive experimentation that GSP allows convolutional networks to learn the counting task as a simple linear mapping problem generalized over the input shape and the number of objects present. This generalization capability allows GSP to avoid both patchwise cancellation and overfitting by training on small patches and inference on full-resolution images as a whole. We evaluate our approach on four different aerial image datasets - two car counting datasets (CARPK and COWC), one crowd counting dataset (ShanghaiTech; parts A and B) and one new challenging dataset for wheat spike counting. Our GSP models improve upon the state-of-the-art approaches on all four datasets with a simple architecture. Also, GSP architectures trained with smaller-sized image patches exhibit better localization property due to their focus on learning from smaller regions while training.
Dynamic Data Citation Service—Subset Tool for Operational Data Management

In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of “dynamic data citation”. The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution.
Mask Point R-CNN.

The attributes of object contours has great significance for instance segmentation task. However, most of the current popular deep neural networks do not pay much attention to the target edge information. Inspired by the human annotation process when making instance segmentation datasets, in this paper, we propose Mask Point RCNN aiming at promoting the neural networks attention to the target edge information, which can heighten the information propagates between multiple tasks by using different attributes features. Specifically, we present an auxiliary task to Mask RCNN, including utilizing keypoint detection technology to construct the target edge contour, and enhancing the sensitivity of the network to the object edge through multi task learning and feature fusion. These improvements are easy to implement and have a small amount of additional computing overhead. By extensive evaluations on the Cityscapes dataset, the results show that our approach outperforms vanilla Mask RCNN by 5.4% on the validation subset and 5.0% on the test subset.
Speeding Up Search-Based Motion Planning via Conservative Heuristics

Weighted A* search (wA*) is a popular tool for robot motionplanning. Its efficiency however depends on the quality of heuristic function used. In fact, it has been shown that the correlation between the heuristic function and the true costto-goal significantly affects the efficiency of the search, when used with a large weight on the heuristics. Motivated by this observation, we investigate the problem of computing heuristics that explicitly aim to minimize the amount of search efforts in finding a feasible plan. The key observation we exploit is that while heuristics tries to guide the search along what looks like an optimal path towards the goal, there are other paths that are clearly sub-optimal yet are much easier to compute. For example, in motion planning domains like footstep-planning for humanoids, a heuristic that guides the search along a path away from obstacles is less likely to encounter local minima compared with the heuristics that guides the search along an optimal but close-to-obstacles path. We utilize this observation to define the concept of conservative heuristics and propose a simple algorithm for computing such a heuristic function. Experimental analysis on (1) humanoid footstep planning (simulation), (2) path planning for a UAV (simulation), and a real-world experiment in footstep-planning for a NAO robot shows the utility of the approach.
Advances in Visualization Recommender Systems

As we take part in the revolution of big data, visualization has become an important mechanism for enabling human-in-the-loop data analytics and data-driven decision making. Since visual representations of data and information greatly appeal to the fast cognitive facilities of the human brain, they form a powerful interface to connect the human to the data and any advanced machine-learning algorithms that could be operating on them. This human–machine discourse is commonly referred to as visual analytics. This installment of Computer’s series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Visualization and Computer Graphics.
Face recognition of face images with hidden parts using Gabor wavelets and PCA

Face Recognition is one of the biometric methods that has recently gained significant attention at the level of research and the scientific community. However, in some special cases, face recognition methods can be sensitive to illumination, facial expressions, aging, face orientation, pose variation and hidden parts of face images, which make recognition very difficult. In this paper, we are interested in the hidden parts of a face image specifically those hidden by facial hair and/or a hair style. We first tested the Eigenfaces method for these modified images; but this approach failed to recognize them. So we have proposed an algorithm that combines Gabor magnitude and phase and PCA. To evaluate the efficiency of our algorithm, we used a variety of face images of FEI Brazilian database and hid the hairstyle and facial hair (barbs) to recognize them from the database images. The obtained results show that the proposed method (Gabor filter and PCA) attained high efficiency in the recognition for this type of problem.
Implementing Blockchain in Public Distribution System

In this paper we will describe a blockchain technology-based prototype that can be used in a small website.There are presently many fraud activities and corruptions taking place in the food supply schemes present as it sometimes does not reach the poor or the other sections of the society.This paper focuses on developing blockchain prototype that is used to record all the transactions/records and log all these transactions.A simple end-to-end web based app of this kind of the blockchain prototype can be built that has most of the features and functionalities to carry out all kinds of the transactions between the central government, state government, the district office, ration shop/and the customers, are recorded in the system.The user of the system can view the transactions of any part of the public distribution system.The project have some features that is guaranteed to provide the most important aspect that is, the security using the concept of blockchain.
Facial Expression Decoding based on fMRI Brain Signal

The analysis of facial expressions is a hot topic in brain-computer interface research. To determine the facial expressions of the subjects under the corresponding stimulation, we analyze the fMRI images acquired by the Magnetic Resonance. There are six kinds of facial expressions: "anger", "disgust", "sadness", "happiness", "joy" and "surprise". We demonstrate that brain decoding is achievable through the parsing of two facial expressions ("anger" and "joy"). Support vector machine and extreme learning machine are selected to classify these expressions based on time series features. Experimental results show that the classification performance of the extreme learning machine algorithm is better than support vector machine. Among the eight participants in the trials, the classification accuracy of three subjects reached 70-80%, and the remaining five subjects also achieved accuracy of 50-60%. Therefore, we can conclude that the brain decoding can be used to help analyzing human facial expressions.
Governing the Global Public Square

Social media platforms are the public square of our era – a reality that has been entrenched by the widespread closure of physical public spaces in response to the COVID-19 pandemic. And this online space is global in nature, with over 2.5 billion users worldwide. Its governance does not fall solely to governments. With the rise of social media, important decisions about what content does - and does not - stay online are made by private technology companies. Reflecting this reality, cutting-edge scholarship has converged on a triadic approach to understanding how the global public square operates - with states, users, and technology companies marking out three points on a “free speech triangle” that determines what content appears online. While offering valuable insights into the nature of online speech regulation, this scholarship—which has influenced public discussion—has been limited by drawing primarily on a recurring set of case studies arising from the U.S. and the European Union. As a result, the free speech triangle has locked in assumptions that make sense for the U.S. and the EU, but that regrettably lack broad applicability. This Essay focuses our attention on the global public square that actually exists, rather than the narrow U.S. and European-centric description that has commanded public attention. Drawing on interviews with civil society, public sources, and technology company transparency data, it introduces a new set of case studies from the Global South, which elucidate important dynamics that are sidelined in the current content moderation discussion. Drawing on this broader set of materials, I supplement the free speech triangle’s analysis of who is responsible for online content, with the question of what these actors do. In this way, activity within the global public square can be grouped into four categories: content production, content amplification, rule creation, and enforcement. Analyzing the governance of the global public square through this functional approach preserves important insights from the existing literature while also creating space to incorporate the plurality of regulatory arrangements around the world. I close with prescriptive insights that this functional approach offers to policymakers in a period of unprecedented frustration with how the global public square is governed.