Reviewerly/exHarmony huggingface dataset:
Metode Weighted Product dalam Pemilihan Penerima Beasiswa Bagi Peserta Didik

Scholarships are one of the factors that can increase learning motivation for students. This scholarship is one of the school programs to help parents of students to ease the burden of education costs of the students. In determining the scholarship recipients who meet the requirements and eligibility at the MA Raudlatul Ulum a decision support system is using the Weighted Product method. Decision making in the Weighted Product method is done by multiplication to connect the rating of each attribute, where the rating of each attribute must be raised by the weight of the attribute in question. In this study there are several criteria used in decision making, namely Average Value, Behavior, Extracurricular, Parent Income, and Dependents of Parents. The research carried out begins with determining the weight of each criterion, then the ranking process is carried out which will produce the most optimal alternative. Based on the results of testing that has been done, it can be concluded that the system is able to provide accuracy of 90% if compared with the results of testing manually.&#x0D;
Self Multi-Head Attention for Speaker Recognition

Most state-of-the-art Deep Learning (DL) approaches for speaker recognition work on a short utterance level. Given the speech signal, these algorithms extract a sequence of speaker embeddings from short segments and those are averaged to obtain an utterance level speaker representation. In this work we propose the use of an attention mechanism to obtain a discriminative speaker embedding given non fixed length speech utterances. Our system is based on a Convolutional Neural Network (CNN) that encodes short-term speaker features from the spectrogram and a self multi-head attention model that maps these representations into a long-term speaker embedding. The attention model that we propose produces multiple alignments from different subsegments of the CNN encoded states over the sequence. Hence this mechanism works as a pooling layer which decides the most discriminative features over the sequence to obtain an utterance level representation. We have tested this approach for the verification task for the VoxCeleb1 dataset. The results show that self multi-head attention outperforms both temporal and statistical pooling methods with a 18\% of relative EER. Obtained results show a 58\% relative improvement in EER compared to i-vector+PLDA.
Generative adversarial networks with augmentation and penalty

Abstract In the original GANs model, there exist two drawbacks for image generation task. On the one hand, a generator cannot deal with a noise that causes an abrupt loss change and inputs of these noises also affect a discriminator, resulting in instability in training process. On the other hand, the discriminator’s discriminating ability is limited in the later stage of training. Eventually, generated images are blurred and targets are incomplete. In order to solve the above problems, a GANs model with hybrid augmented discriminator and fake sample penalty is firstly proposed. In this model, we design a hybrid augmented discriminator. We add real and fake samples into this discriminator. These hybrid samples are conducive to improve the discriminating ability of discriminator. Then, to stabilize the training process and achieve local convergence, we add a penalty to the generator on the basis of designed discriminator, which constrains fake samples with ill condition number. Secondly, we validate the generalization of proposed method on three different loss functions including Hinge, GANs and LSGANs loss. Finally, experimental results show that the proposed method is more effective than baseline models.
Minimization of Cable-Net Reflector Shape Error by Machine Learning

During assembly of a cable-net reflector, surface adjustments must be made to meet the stick accuracy requirement of a space mission by changing the lengths of adjustable cables. It is desirable to find the optimum adjustment amount based on the current state of the cable-net reflector. However, all data needed to establish an accurate simulation model for the cable-net reflector cannot be measured. Therefore, the cable-net reflector is treated as a black box, and a machine learning algorithm (support vector machine) is used to develop a prediction model to establish the relationship between cable length adjustments and surface accuracy. The -fold cross-validation method is used to estimate the generalization error. The simulated annealing and grid search are combined to get the optimum hyperparameters for the prediction model. Next, a surface adjustment method is developed to calculate the optimal cable lengths. A tension truss reflector example is used to demonstrate the support vector machine prediction model and the adjustment method. The machine learning algorithm identified the relationships between cable lengths and surface accuracy. Both surface accuracy and tension uniformity can be improved by adjusting the boundary cables and tension ties with this method using only information from the front-side nodes.
Patient Privacy and Ownership of Electronic Health Records on a Blockchain

Blockchain technology has found application outside of cryptocurrency in recent times. The development of patient-centric storage of medical records on a blockchain has recently gained momentum. However, there have been few developments in providing a solution towards giving up full control to the public so that the concentration of power by only a small group of validators can be avoided. In this paper, we discuss a solution that enables patient-driven interoperability of medical records on a public blockchain while maintaining privacy using new cryptographic constructs and truly giving up control to the general public.
A Non-prescriptive Environment to Scaffold High Quality and Privacy-aware Production of Open Data with AI

Data quality is strictly related to fitness for use. Therefore, data providers should improve the intrinsic quality of published data to prevent the diffusion of data sets practically impossible to use. Among all data providers, it is critical that governments and public agencies better assess and improve the quality of the produced data sets as early as possible - ideally during the production phase. Besides the quality aspect, data providers should also bear in mind that if they want to expose personal information, data must be compliant with EU General Data Protection Regulation (GDPR).
Recursive Subtree Composition in

Miryam de Lhoneux, Miguel Ballesteros, Joakim Nivre. 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.
Pulse-resolved measurement of continuous-variable Einstein-Podolsky-Rosen entanglement with shaped local oscillators

We report an experimental demonstration of the continuous-variable Einstein-Podolsky-Rosen (EPR) paradox and quantum steering using a pulsed light source and nonlinear optical waveguides. In this pulsed light source experiment, time-domain measurements were performed in which one measured value of the quadrature phase amplitude of the light field was independently obtained for each pulse. This independence is useful for application to quantum information processing and fundamental physics. To realize time-domain measurements, in-house built homodyne detectors were used to detect individual pulses. In addition, to improve the temporal-mode matching between the local oscillator (LO) and entangled pulse, the duration of the LO pulse was shortened by single-pass optical parametric amplification. The product of the conditional variances of the quadrature amplitudes was 0.82 ± 0.09 < 1, which satisfies the condition for realization of the EPR paradox and quantum steering.
Recognition of Robot Based on Attention Mechanism and Convolutional Neural Network

In order to accurately recognize the working targets in complex environments, an industrial robot vision model based on attention mechanism and deep learning is proposed. The model uses an improved attention mechanism to achieve fast focusing of the target, and employ a 10-layer-convolutional neural network (CNN) which combines local connection with full connection to accomplish target recognition. The local connection of CNN consists of three convolution layers and three sub-sampling layers, the convolution layers are used for feature extraction and the sub-sampling layers are used to reduce network nodes. The full connection layer of CNN is composed of input layer, hidden layer and output layer as a classifier for target recognition. More than 1000 target images are sampled for CNN network training. The effects of different CNN network structure parameters on the model are analyzed in order to satisfy the rapidity and reliability of robot vision. The test results show that the combination of improved attention mechanism and CNN model can achieve the fast focusing and accurate recognition of working targets in the robot system, the proposed model is can be applied to the visual navigation of industrial robots.
Supervised Machine Learning Classifier for Email Spam Filtering

Email is the omnipresent and persistent consequence applied to a regular base by a huge number of individuals worldwide. However, as a result of social support systems and advertisers, the majority of the emails hold unnecessary information called spam. This concern not just affects typical users of the net, but additionally causes an enormous setback for companies and organizations as it costs a massive amount of money in mislaid productivity, wastage of user’s time, and network bandwidth. In recent times, various parallel researchers have presented several email spam classification techniques, but it is extremely tough to eradicate the spam emails completely, while the spammers transform their techniques frequently. The proposed method is an efficient technique to classify the email spam messages using Support Vector Machines (SVM). Here, we present an SVM handling separation of nonlinear data using a Kernel function, which is an advanced machine learning technique in R to improve the accuracy of the model. Finally, we present a generic template for a working of kernel function in SVM that can be built in R.
Braille Translation System Using Neural Machine Translation Technology I - Code Conversion

We have constructed a translation system to use easily even for beginners. In order to translate Japanese into Braille, we programmed in Python and C language using MeCab, a morphological analysis engine. However, there are many exceptions in Braille grammar, and the programming has become complicated. In recent years, researches on the machine translation using a neural network are thriving, so we carried out braille translation using the technology of neural machine translation (NMT) this time. Tensorflow was used as a library of neural network. We also incorporated a transform which was a translation component in the open source library Tensor2Tensor. We used 100,000 words of Japanese as teacher data. By using NMT, the morphological engine and the complicated programming become unnecessary. Also, MNT can translate without worrying about the complex Braille grammar. Then, just by entering Japanese sentence, the Braille codes are output. There are many advantages. We describe the braille translation using NMT.
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation

Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization). Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. We propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. For training a plan-to-text generator, we present a method for matching reference texts to their corresponding text plans. For inference time, we describe a method for selecting high-quality text plans for new inputs. We implement and evaluate our approach on the WebNLG benchmark. Our results demonstrate that decoupling text planning from neural realization indeed improves the system's reliability and adequacy while maintaining fluent output. We observe improvements both in BLEU scores and in manual evaluations. Another benefit of our approach is the ability to output diverse realizations of the same input, paving the way to explicit control over the generated text structure.
The Future of the Banking System under the Dominance and Development of the Cryptocurrency Industry: <i>Empirical Evidence from Cointegration Analysis</i>

Bank stability and trust levels have been seriously affected by the 2007–2008 Global Financial Crisis, which led to the identification of a range of policies based on past experience intended to overcome the consequences of the crisis. The idea of cryptocurrency was introduced to handle the mistrust of financial intermediaries that led to the liquidity crisis. This study investigates whether the new cryptocurrencies have been able to perform the functions of financial intermediaries and offer the confidence level required by bank depositors, examining their long-run effect on banks’ deposit mobilization. The study applies a cointegration test analysis with a vector error correction model to examine this relationship. Overall results indicate that in the countries studied increases in the market capitalization of the cryptocurrency industry have a significant negative impact on banks’ deposit variability, while decreases have a positive impact, with a long-run equilibrium relationship. The outcomes of the study suggest that in order to regain the trust of depositors and avoid the effects of the cryptocurrency industry, banks should be encouraged to invest directly in cryptocurrency or should consider cryptocurrencies as an alternative investment asset for their portfolio investment diversification strategies during bullish market conditions and avoid them during bearish market conditions. Also, banks can incorporate blockchain technology into their operation system and compete with the cryptocurrency industry side by side in the financial market. If neither of these options is taken, however, the banking system may not be able to compete and sustain in the long term using the current operational model. The outcomes of this study can be used as policy guidance by central banks and the banking industry for the betterment of the industry. <b>TOPICS:</b>Currency, legal/regulatory/public policy, statistical methods, portfolio construction
DA-LD-Hildesheim at SemEval-2019 Task 6: Tracking Offensive Content with Deep Learning using Shallow Representation

This paper presents the participation of team DA-LD-Hildesheim of Information Retrieval and Language Processing lab at DA-IICT, India in Semeval-19 OffenEval track. The aim of this shared task is to identify offensive content at fined-grained level granularity. The task is divided into three sub-tasks. The system is required to check whether social media posts contain any offensive or profane content or not, targeted or untargeted towards any entity and classifying targeted posts into the individual, group or other categories. Social media posts suffer from data sparsity problem, Therefore, the distributed word representation technique is chosen over the Bag-of-Words for the text representation. Since limited labeled data was available for the training, pre-trained word vectors are used and fine-tuned on this classification task. Various deep learning models based on LSTM, Bidirectional LSTM, CNN, and Stacked CNN are used for the classification. It has been observed that labeled data was highly affected with class imbalance and our technique to handle the class-balance was not effective, in fact performance was degraded in some of the runs. Macro F1 score is used as a primary evaluation metric for the performance. Our System achieves Macro F1 score = 0.7833 in sub-task A, 0.6456 in the sub-task B and 0.5533 in the sub-task C.
Permission less Block chains and Secure Logging

The blockchain technology enables mutually untrusting participants to reach consensus on the state of a distributed and decentralized ledger (called a blockchain) in a permissionless setting. The consensus protocol of the blockchain imposes a unified view of the system state over the global network, and once a block is stable in the blockchain, its data is visible to all users and cannot be retrospectively modified or removed. Due to these properties, the blockchain technology is regarded as a general consensus infrastructure and based on which a variety of systems have been built.This article presents a study and survey of permissionless blockchain systems in the context of secure logging. We postulate the most essential properties required by a secure logging system and by considering a wide range of applications, we give insights into how the blockchain technology matches these requirements. Based on the survey, we motivate related research perspectives and challenges for blockchain-based secure logging systems, and we highlight potential solutions to some specific problems.
Future Security: Processes or Properties?—Research Directions in Cybersecurity

Security in critical infrastructures is a highly relevant topic and as the level of security of critical infrastructures needs to be increased the need for adequate methods and tools is apparent. “Processes and their properties” is the analysis perspective through which we revisit empirical data from our research on critical infrastructures to identify future research directions in security.
Multiple-Photon-Added and -Subtracted Two-Mode Binomial States: Nonclassicality and Entanglement*

Abstract We theoretically analyze the nonclassicality and entanglement of two new non-Gaussian entangled states generated by applying multiple-photon addition and subtraction to a two-mode binomial state. The nonclassical properties are investigated in terms of the partial negativity of the Wigner functions, whose results show that their nonclassicality can be enhanced via one-mode even-number photon operations and two-mode symmetrical operations for the initial two-mode binomial state. We also find that there exists some enhancement in the entanglement properties in certain parameter ranges via one-mode photon-addition and two-mode symmetrical operations .
Student experience on the development of transversal skills in university studies

The Bologna Process envisioned the idea of the development of transversal skills in the course of higher education. Recent research on education acknowledges the fact that higher education including university studies is already facing the turn towards inclusive transversal skills development. The current research focuses on the development of student transversal skills in university studies. It is aimed at the investigation of the phenomenon of the development of student transversal skills in university studies. The objective of higher education covers more than simply employability of graduates although employment outcomes are important. Higher education is a versatile process, which provides students with the possibilities to develop their knowledge, skills, attitudes in order to enable them to actively participate in the wider society by being involved in active citizenship and successful professional careers. Our research reveals that the development of transversal skills depends on both internal personal characteristics and external social factors including colleague influence, exchanging ideas and also institutional impact. The research participants also reveal their perception that their transversal skills are substantially improved on their university studies; however, they also stress their perceived needs for developing certain transversal skills which are necessary in professional work life, such as communication and foreign languages, ability to apply innovations and creativity, analytical skills and critical thinking, learning to learn and time management.
A Comparison of Visual Servoing from Features Velocity and Acceleration Interaction Models

Visual Servoing has been widely investigated in the last decades as it provides a powerful strategy for robot control. Thanks to the direct feed-back from a set of sensors, it allows to reduce the impact of some modeling errors and to perform tasks even in uncertain environments. The commonly exploited approach in this field is to use a model that expresses the rate of change of a set of features as a function of sensor twist. These schemes are commonly used to obtain a velocity command, which needs to be tracked by a low-level controller. Another approach that can be exploited consists in going one step further and to consider an acceleration model for the features. This strategy allows also to obtain a natural and direct link with the dynamic model of the controlled system. This study aims at comparing the use of velocity and acceleration-based models in feed-back linearization for Visual Servoing. We consider the case of a redundant manipulator and discuss what this implies for both control techniques. By means of simulations, we show that controllers based on features acceleration give better results than those based on velocity in presence of noisy feedback signals.
An Improved K-Means Algorithm Based on Kurtosis Test

Abstract Clustering is a process of classifying data into different classes and has become an important tool in data mining. Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simplicity and high efficiency. However, the traditional K-means algorithm can only find spherical clusters, and is also susceptible to noise points and isolated points, which makes the clustering results affected. To solve these problems, this paper proposes an improved K-means algorithm based on kurtosis test. The improved algorithm can improve the adaptability of clustering algorithm to complex shape datasets while reducing the impact of outlier data on clustering results, so that the algorithm results can be more accurate. The method used in our study is known as kurtosis test and Monte Carlo method. We validate our theoretical results in experiments on a variety of datasets. The experimental results show that the proposed algorithm has larger external indicators of clustering performance metrics, which means that the accuracy of clustering results is significantly improved.
Heatmap-Guided Balanced Deep Convolution Networks for Family Classification in the Wild

Automatic kinship recognition using Computer Vision, which aims to infer the blood relationship between individuals by only comparing their facial features, has started to gain attention recently. The introduction of large kinship datasets, such as Family In The Wild (FIW), has allowed large scale dataset modeling using state of the art deep learning models. Among other kinship recognition tasks, family classification task is lacking any significant progress due to its increasing difficulty in relation to the family member size. Furthermore, most current state of-the-art approaches do not perform any data pre-processing (which try to improve models accuracy) and are trained without a regularizer (which results in models susceptible to overfitting). In this paper, we present the Deep Family Classifier (DFC), a deep learning model for family classification in the wild. We build our model by combining two sub-networks: internal Image Feature Enhancer which operates by removing the image noise and provides an additional facial heatmap layer and Family Class Estimator trained with strong regularizers and a compound loss. We observe progressive improvement in accuracy during the validation phase, with a state of the art results of 16.89% is obtained for the track 2 in the RFIW2019 challenge and 17.08% of familly classification task on FIW dataset.