Reviewerly/exHarmony huggingface dataset:
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection

We study the detection of propagandistic text fragments in news articles.Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques.Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language.The former refers to the logical consistency between coarse-and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions.The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters.We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection.Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.
s-Transformer: Segment-Transformer for Robust Neural Speech Synthesis

Neural end-to-end text-to-speech (TTS) , which adopts either a recurrent model, e.g. Tacotron, or an attention one, e.g. Transformer, to characterize a speech utterance, has achieved significant improvement of speech synthesis. However, it is still very challenging to deal with different sentence lengths, particularly, for long sentences where sequence model has limitation of the effective context length. We propose a novel segment-Transformer (s-Transformer), which models speech at segment level where recurrence is reused via cached memories for both the encoder and decoder. Long-range contexts can be captured by the extended memory, meanwhile, the encoder-decoder attention on segment which is much easier to handle. In addition, we employ a modified relative positional self attention to generalize sequence length beyond a period possibly unseen in the training data. By comparing the proposed s-Transformer with the standard Transformer, on short sentences, both achieve the same MOS scores of 4.29, which is very close to 4.32 by the recordings; similar scores of 4.22 vs 4.2 on long sentences, and significantly better for extra-long sentences with a gain of 0.2 in MOS. Since the cached memory is updated with time, the s-Transformer generates rather natural and coherent speech for a long period of time.
Investment, Financial, Trade Freedom and Risk-taking: Empirical Evidence from USA‎

This study aims to explore investment, financial, and trade freedom impact on banks’ risk-taking. A unique dataset of large commercial banks of the USA covering the period 2002-2018 is used. The findings prove that financial freedom reduces the bank’s risk-taking whereas investment and trade freedom increase the risk-taking of large commercial banks in the observed period. The behavior of risk-taking due to financial, trade and investment freedom of under-capitalized and low-liquid banks seems to be marginally less impacted as compared to well-capitalized and high-liquid banks. The findings are robust using loan loss reserves as a risk measure and subclassification of a sample. The results suggest that the intervention of the government is decisive in developing the degree of economic freedom for the stability of the financial system.
Assess The Effectiveness of Students' Online Learning in the Epidemic Disaster 19

Based on online survey data of 225 students at Ho Chi Minh City University of Technical Education (May 2020), men accounted for 74.7% and women accounted for 25.3%; the paper analyzes the effectiveness of students' online learning in the context of the covid epidemic 19. The results show that online teaching of students has somewhat met the expectations of learning, bringing the certain convenience in saving travel time, having more time to study documents,... However, online learning also shows that there are still many difficulties from the means of learning, learning space, the process of interacting with others. Collaboration with teachers leads to the effectiveness of online learning, which is not as effective as traditional methods. Therefore, research on online learning efficiency of students will contribute an objective view on the state of online learning in the context of the covid epidemic 19.
ArraMon: A Joint Navigation-Assembly Instruction Interpretation Task in Dynamic Environments

For embodied agents, navigation is an important ability but not an isolated goal. Agents are also expected to perform specific tasks after reaching the target location, such as picking up objects and assembling them into a particular arrangement. We combine Vision-and-Language Navigation, assembling of collected objects, and object referring expression comprehension, to create a novel joint navigation-and-assembly task, named ArraMon. During this task, the agent (similar to a PokeMON GO player) is asked to find and collect different target objects one-by-one by navigating based on natural language instructions in a complex, realistic outdoor environment, but then also ARRAnge the collected objects part-by-part in an egocentric grid-layout environment. To support this task, we implement a 3D dynamic environment simulator and collect a dataset (in English; and also extended to Hindi) with human-written navigation and assembling instructions, and the corresponding ground truth trajectories. We also filter the collected instructions via a verification stage, leading to a total of 7.7K task instances (30.8K instructions and paths). We present results for several baseline models (integrated and biased) and metrics (nDTW, CTC, rPOD, and PTC), and the large model-human performance gap demonstrates that our task is challenging and presents a wide scope for future work. Our dataset, simulator, and code are publicly available at: https://arramonunc.github.io
ANALISIS BEBAN DAN STATUS PENCEMARAN BOD DAN COD DI KALI ASIN, SEMARANG

Kali Asin is an important river for the drainage system in North Semarang. This river is often drained by domestic and industrial waste. The inclusion of these wastes can cause a decrease water quality marked by an increase Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) concentrations. The purpose of this study was to determine the load of BOD and COD pollution and assess the status of pollution using the Pollution Index Method based on Government Regulation Number 82 of 2001 class II. Sampling was carried on 17 December 2019-28 January 2020. Sampling was carried out three times, once in 3 weeks. The material used was water samples taken from three stations (estuary, middle of the settlement, and far from the settlement) based on regional characteristics, land use, and sources of pollution. The research method uses a survey method with the technique of determining the location of sampling using purposive sampling. Water quality variables analyzed were temperature, discharge, pH, DO, BOD, COD. Based on the research results, these variables mostly do not meet the quality standards such as BOD and COD variables. The results of the calculation of pollution load in terms of BOD and COD variables obtained an average number of 952.54 kg / day and 3,425.86 kg / day. The results of the pollution index calculation at stations A, B, and C are 3.88; 3.78 and 3.53. The level pollution of Kali Asin was categorized in mild contamination.Â Keywords: Pollution Load, BOD, COD, Pollution Index, Kali Asin
Forensic Browser of Twitter based on Web Services

Twitter is a social media that can be accessed through smartphones and desktops.The large number of users makes Twitter inseparable from crimes including pornography, online gambling and hate speech.In this study, the steps used are collection, examination and analysis.This study uses a laptop as an object that is scenario in a state of opening Twitter via the Google Chrome browser with two modes, namely public mode and private mode.The research used the help of forensic tools, namely ftk imager, dumpIT, belkasoft ram capturer, XhD, browser history viewer, browser history capturer, and cached video viewer.This research produces digital evidence with google chrome browser in public mode and google chrome browser in private mode.In the condition of using the browser in public mode with indicators in the form of text posts, link posts, images, and videos, the research succeeded in getting all the evidence that was sought.Meanwhile, in the private mode google chrome browser managed to get evidence with a success of 50%, namely in the form of text posts and link posts.While the remaining 50% is not found for private mode browsers, namely in image posts and video posts.
Proposal of Surrogate Model for Genetic Programming Based on Program Structure Similarity

This paper proposes a novel surrogate model for genetic programming that estimates the fitness of each individual by using the tree structure similarity. In particular, the fitness of each individual is estimated with the nearest neighbor method by comparing each individual with the evaluated population. We conduct an experiment to investigate the effectiveness of the proposed method. In the experiment, we compare genetic programming with and without the proposed surrogate model on the symbolic regression problem. We assess the convergence speed and the discovery ratio of the optimum program. The experimental result reveals that the proposed method improves the convergence speed of genetic programming while maintaining the discovery rate of the optimum program.
A Proposal for Virtual Laboratories in Learning Biology for Secondary School Curriculum

The study aimed to identify the extent of biology teachers' awareness regarding the importance of virtual laboratories in teaching biology for secondary school. It also intended to develop a proposal to enable biology teachers to use virtual laboratories in teaching secondary level. To achieve these aims, the descriptive-analytical approach was used. The research sample consisted of fifty female biology teachers at the secondary level in the city of Al-Kharj. The study applied the questionnaire as its main tool to gather data. The study concluded that the degree of awareness among female biology teachers regarding the importance of virtual laboratories in teaching biology for the secondary level was found at a moderate level. Besides, the ability of female biology teachers to use virtual laboratories in teaching biology for the secondary stage was low. Considering these results, a proposal was developed to enable female biology teachers to use virtual laboratories in teaching biology for secondary school.
A novel method for Causal Structure Discovery from EHR data: A demonstration on type-2 diabetes mellitus

Abstract Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.
Face Recognition Service Model for Student Identity Verification Using Deep Neural Network and Support Vector Machine (SVM)

Impersonation in the context of examination, is a situation where a candidate sits in an examination for another candidate pretending to the real candidate. In many institutions in Nigeria, to mitigate this act, students are expected to present a means of identification before entering the examination hall. However, this approach is not sufficient to determine the eligibility of a student for an examination as these means of identification can easily be falsified. This paper therefore, develops a face recognition web service model for student identity verification using Deep Neural Network (DNN) and Support Vector Machine (SVM). The aim is to mitigate examination impersonation by simple face scan using mobile phone and also to make such a model accessible and re-usable for seamless integration with any kind of student identity verification project.
Methods for calculating system parameters for electronic signature "Crystals-Dilithium" 128, 256, 384 and 512 bits of security levels

Globally, the efforts of a significant number of crypto-theorists, mathematicians and cryptologists-practitioners are focused on the NIST PQC open competition. One of the main tasks of the competition consists in development and adoption of a post-quantum ES standard or standards. The finalists of the second stage of the NIST competition were three ES mechanisms – CRYSTALS-DILITHIUM, Falcon and Rainbow. In addition, three alternative candidates were identified that require more detailed research. In general, a comprehensive analysis of the finalists is an important task for cryptologists in the global cryptocommunity. Moreover, security, i.e. bringing the cryptographic stability of two finalist candidates, to the ES standard – CRYSTALS-DILITHIUM and Falcon, is based on problems in the theory and practice of algebraic lattices. The EP Dilithium method (scheme) is based on the approach called "Fiat-Shamir Interruptions". The essence of the CRYSTALS-DILITHIUM ES algorithm is considered in the article. A detailed analysis of possible attacks on the algorithm and the mechanisms of their implementation is also carried out. Models of violator, threats and security are considered and analyzed. The main definitions of ES security models are provided. The main design elements of the mechanism of perspective post-quantum ES Dilithium are described in the generalized form. General estimations of the ES Dilithium security level are given. The substantiation and essence of models of threats, violator and security are given. The stability of the ES Dilithium algorithm is investigated. The purpose of the article is to substantiate a security model, classification, primary analysis and assessment of known attacks on the CRYSTALS-DILITHIUM EP cryptosystem, to establish restrictions and develop practical algorithms for calculating (generating) system-wide parameters to ensure 128, 256, 384 and 512 bits of security relative to classical and 64, 128, 192 and 256 bits relative to quantum cryptanalysis.
GradAug: A New Regularization Method for Deep Neural Networks

We propose a new regularization method to alleviate over-fitting in deep neural networks. The key idea is utilizing randomly transformed training samples to regularize a set of sub-networks, which are originated by sampling the width of the original network, in the training process. As such, the proposed method introduces self-guided disturbances to the raw gradients of the network and therefore is termed as Gradient Augmentation (GradAug). We demonstrate that GradAug can help the network learn well-generalized and more diverse representations. Moreover, it is easy to implement and can be applied to various structures and applications. GradAug improves ResNet-50 to 78.79% on ImageNet classification, which is a new state-of-the-art accuracy. By combining with CutMix, it further boosts the performance to 79.67%, which outperforms an ensemble of advanced training tricks. The generalization ability is evaluated on COCO object detection and instance segmentation where GradAug significantly surpasses other state-of-the-art methods. GradAug is also robust to image distortions and FGSM adversarial attacks and is highly effective in low data regimes. Code is available at this https URL
Simple means Faster: Real-Time Human Motion Forecasting in Monocular First Person Videos on CPU

We present a simple, fast, and light-weight RNN based framework for forecasting future locations of humans in first person monocular videos. The primary motivation for this work was to design a network which could accurately predict future trajectories at a very high rate on a CPU. Typical applications of such a system would be a social robot or a visual assistance system for all, as both cannot afford to have high compute power to avoid getting heavier, less power efficient, and costlier. In contrast to many previous methods which rely on multiple type of cues such as camera ego-motion or 2D pose of the human, we show that a carefully designed network model which relies solely on bounding boxes can not only perform better but also predicts trajectories at a very high rate while being quite low in size of approximately 17 MB. Specifically, we demonstrate that having an auto-encoder in the encoding phase of the past information and a regularizing layer in the end boosts the accuracy of predictions with negligible overhead. We experiment with three first person video datasets: CityWalks, FPL and JAAD. Our simple method trained on CityWalks surpasses the prediction accuracy of state-of-the-art method (STED) while being 9.6x faster on a CPU (STED runs on a GPU). We also demonstrate that our model can transfer zero-shot or after just 15% fine-tuning to other similar datasets and perform on par with the state-of-the-art methods on such datasets (FPL and DTP). To the best of our knowledge, we are the first to accurately forecast trajectories at a very high prediction rate of 78 trajectories per second on CPU.
Parameter Identification of Generator Excitation System Based on Improved Grey Wolf Optimization

Abstract In order to ensure the accuracy of power system modelling and the reliability of safety and stability analysis, it is important to confirm the true parameters of the excitation system. This paper proposes that using hunting group division strategy and convergence factor non-linear decreasing strategy to improve the standard grey wolf optimization algorithm, and applied it to the identification of the generator excitation system. The simulation results show that the identification of the excitation system based on the improved grey wolf optimization algorithm has higher identification accuracy and stability, which provides an effective new method for the parameter identification of the nonlinear.
Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling

Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks. On the other hand, traditional machine translation has a long history of leveraging unlabeled data through noisy channel modeling. The same idea has recently been shown to achieve strong improvements for neural machine translation. Unfortunately, naïve noisy channel modeling with modern sequence to sequence models is up to an order of magnitude slower than alternatives. We address this issue by introducing efficient approximations to make inference with the noisy channel approach as fast as strong ensembles while increasing accuracy. We also show that the noisy channel approach can outperform strong pre-training results by achieving a new state of the art on WMT Romanian-English translation.
Measuring weighting factor of eigenstates in quantum superposition by classical mechanical ‘quantum’ computer

We design classical apparatuses to measure the weighting factor of eigenstates without breaking the quantum mechanical superposition state. The apparatuses work as "quantum" computer even though they follow classical physics. To do quantum computation, we need to know what percentage of a particular state is included in a superposed quantum state. However, we face difficulties that large-scale equipment, such as cryogenic temperature, is required to maintain the overlap. Combining causal formulation of quantum mechanics and mediocre classical mechanism overcomes the situation. The weighting coefficients of the superposition can be determined by monitoring the trajectory of a classical particle under quantum flucutation force. Such monitoring is possible only in macroscopic physics. Efficacy of our macroscopic apparatus model is shown in one of typical quantum algorithms, Deutsch algorithm. This classical mechanical design opens the way for "quantum mechanical" artificial intelligence to be mounted on robots in usual macroscopic world.
Smoothing of Mammogram Using an Improved Gradient based Technique

The most common cancer among women is breast cancer. The survival prognosis and rate of breast cancer differ considerably according to the stage of cancer. Treatment is more efficient if detected early because the progression to a higher stage can be avoided. Digital mammograms are used to identify breast cancer in the early stage. Low contrast mammogram images are usually affected by Poisson noise. In the present work, an improved L0 gradient minimization smoothing method is proposed. Experiments performed on the DDSM (Digital Database for Screening Mammography) images show that the proposed method is better in sharpening major edges. Further the performance metrics such as peak signal to noise ratio (PSNR), mean square error (MSE) and structural similarity index measure (SSIM) values show that proposed method provides better performance as compared with different existing methods.
Quasar Detection using Linear Support Vector Machine with Learning From Mistakes Methodology

The field of Astronomy requires the collection and assimilation of vast volumes of data. The data handling and processing problem has become severe as the sheer volume of data produced by scientific instruments each night grows exponentially. This problem becomes extensive for conventional methods of processing the data, which was mostly manual, but is the perfect setting for the use of Machine Learning approaches. While building classifiers for Astronomy, the cost of losing a rare object like supernovae or quasars to detection losses is far more severe than having many false positives, given the rarity and scientific value of these objects. In this paper, a Linear Support Vector Machine (LSVM) is explored to detect Quasars, which are extremely bright objects in which a supermassive black hole is surrounded by a luminous accretion disk. In Astronomy, it is vital to correctly identify quasars, as they are very rare in nature. Their rarity creates a class-imbalance problem that needs to be taken into consideration. The class-imbalance problem and high cost of misclassification are taken into account while designing the classifier. To achieve this detection, a novel classifier is explored, and its performance is evaluated. It was observed that LSVM along with Ensemble Bagged Trees (EBT) achieved a 10x reduction in the False Negative Rate, using the Learning from Mistakes methodology.
Social media in Egypt

This chapter provides an overview of social media in Egypt with a special focus on recent development in infrastructure, legislation, and social and political aspects of the environment within which these media are expected to operate. The chapter argues that while the current government tries to catch the train of technological innovation by investing in the Internet and social media infrastructure, this investment is being undermined by the many laws imposed with the intention of monitoring the cyberspace and crippling netizens. Several consequences and technology-enabled circumvention precautions taken by civil society are also discussed.