Reviewerly/exHarmony huggingface dataset:
Automatically Extracting Challenge Sets for Non-Local Phenomena in Neural Machine Translation

We show that the state-of-the-art Transformer MT model is not biased towards monotonic reordering (unlike previous recurrent neural network models), but that nevertheless, longdistance dependencies remain a challenge for the model.Since most dependencies are shortdistance, common evaluation metrics will be little influenced by how well systems perform on them.We therefore propose an automatic approach for extracting challenge sets replete with long-distance dependencies, and argue that evaluation using this methodology provides a complementary perspective on system performance.To support our claim, we compile challenge sets for English-German and German-English, which are much larger than any previously released challenge set for MT.The extracted sets are large enough to allow reliable automatic evaluation, which makes the proposed approach a scalable and practical solution for evaluating MT performance on the long-tail of syntactic phenomena. 1
An ensemble CNN method for biomedical entity normalization

Different representations of the same concept could often be seen in scientific reports and publications.Entity normalization (or entity linking) is the task to match the different representations to their standard concepts.In this paper, we present a two-step ensemble CNN method that normalizes microbiology-related entities in free text to concepts in standard dictionaries 1 .The method is capable of linking entities when only a small microbiology-related biomedical corpus is available for training, and achieved reasonable performance in the online test of the BioNLP-OST19 shared task Bacteria Biotope.
A Context-Aware Conversational Agent in the Rehabilitation Domain

Conversational agents are reshaping our communication environment and have the potential to inform and persuade in new and effective ways. In this paper, we present the underlying technologies and the theoretical background behind a health-care platform dedicated to supporting medical stuff and individuals with movement disabilities and to providing advanced monitoring functionalities in hospital and home surroundings. The framework implements an intelligent combination of two research areas: (1) sensor- and camera-based monitoring to collect, analyse, and interpret people behaviour and (2) natural machine–human interaction through an apprehensive virtual assistant benefiting ailing patients. In addition, the framework serves as an important assistant to caregivers and clinical experts to obtain information about the patients in an intuitive manner. The proposed approach capitalises on latest breakthroughs in computer vision, sensor management, speech recognition, natural language processing, knowledge representation, dialogue management, semantic reasoning, and speech synthesis, combining medical expertise and patient history.
SAR Image Super-Resolution Based on Noise-Free Generative Adversarial Network

Deep learning has been successfully applied to the ordinary image super-resolution (SR). However, since the synthetic aperture radar (SAR) images are often disturbed by multiplicative noise known as speckle and more blurry than ordinary images, there are few deep learning methods for the SAR image SR. In this paper, a deep generative adversarial network (DGAN) is proposed to reconstruct the pseudo high-resolution (HR) SAR images. First, a generator network is constructed to remove the noise of low-resolution SAR image and generate HR SAR image. Second, a discriminator network is used to differentiate between the pseudo super-resolution images and the realistic HR images. The adversarial objective function is introduced to make the pseudo HR SAR images closer to real SAR images. The experimental results show that our method can maintain the SAR image content with high-level noise suppression. The performance evaluation based on peak signal-to-noise-ratio and structural similarity index shows the superiority of the proposed method to the conventional CNN baselines.
SC-BiCapsNet: A Sentiment Classification Model Based on Bi-Channel Capsule Network

Most text sentiment classification methods have the shortcomings of insufficient semantic understanding and low classification accuracy.To solve these problems, a sentiment classification model based on capsule network (SC-BiCapsNet) was proposed in this paper.In the stage of word vectorization, the G-Word model is presented to improve the ability of text information representation.In the stage of text semantic representation, the COL-Att model is presented to optimize the semantic coding structure and enhance the effect of text semantic expression.In the stage of text classification, the capsule network is devised, and its core dynamic routing algorithm can increase the weight value of important features, thus improving the accuracy of classification results.This paper evaluates the performance of SC-BiCapsNet model on two text datasets, IMDB and NLPCC 2014 dataset.The experimental results demonstrate that our method outperforms the results of other related models on IMDB dataset and gets the highest accuracy of 0.9238.Meanwhile, comparing with the best system in NLPCC2014-Task II, the F1-scsore of SC-BiCapsNet has increased by at least 1.5%, which proves the validity of SC-BiCapsNet.In the future work, we will try to reduce the time consumption and improve the generalization ability of the model.
Integrated Cognitive Architecture for Robot Learning of Action and Language

The manner in which humans learn, plan, and decide actions is a very compelling subject. Moreover, the mechanism behind high-level cognitive functions, such as action planning, language understanding, and logical thinking, has not yet been fully implemented in robotics. In this paper, we propose a framework for the simultaneously comprehension of concepts, actions, and language as a first step toward this goal. This can be achieved by integrating various cognitive modules and leveraging mainly multimodal categorization by using multilayered multimodal latent Dirichlet allocation (mMLDA). The integration of reinforcement learning and mMLDA enables actions based on understanding. Furthermore, the mMLDA, in conjunction with grammar learning and based on the Bayesian hidden Markov model, allows the robot to verbalize its own actions and understand user utterances. We verify the potential of the proposed architecture through experiments using a real robot.
Implementasi Media Pembelajaran Berbasis Teknologi Informasi dan Komunikasi dalam Distance Learning

The rapid development of science and advances in advanced technology have implications for the rhythms of human communication. A very striking implication is that people are increasingly free and easy (easy access) in obtaining information and knowledge, including in the field of education. The global education environment is seen in several ways to provide solutions to other gaps and problems through the soaring distributed learning opportunities. Learning process activities with the help of information and communication technology take place not only applied limited by space, location or level of education such as at school or college, but can be done in many different places and involve a lot of people. Learning methods from learners that are not limited to time and place are called information and communication technology based learning. Learning media that can be used in distance learning are: computers, television, radio, internet, voice recordings, and VCD tapes.
Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation

While contextualized embeddings have produced performance breakthroughs in many Natural Language Processing (NLP) tasks, Word Sense Disambiguation (WSD) has not benefited from them yet.In this paper, we introduce QBERT, a Transformerbased architecture for contextualized embeddings which makes use of a coattentive layer to produce more deeply bidirectional representations, better-fitting for the WSD task.As a result, we are able to train a WSD system that beats the state of the art on the concatenation of all evaluation datasets by over 3 points, also outperforming a comparable model using ELMo.
Interactive Multi-Head Attention Networks for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ASC) has received much attention these years. With the successful application of attention networks in many fields, attention-based ASC has aroused great interest. However, most of the previous methods did not analyze the contribution of words well and the context-aspect term interaction was not well implemented, which largely limit the efficacy of models. In this paper, we exploit a novel method that is efficient and mainly adopts Multi-head Attention (MHA) networks. First, the word embedding and aspect term embedding are pre-trained by Bidirectional Encoder Representations from Transformers (BERT). Second, we make full use of MHA and convolutional operation to obtain hidden states, which is superior to traditional neural networks. Then, the interaction between context and aspect term is further implemented through averaging pooling and MHA. We conduct extensive experiments on three benchmark datasets and the final results show that the Interactive Multi-head Attention Networks (IMAN) model consistently outperforms the state-of-the-art methods on ASC task.
High-Resolution Image Inpainting Based on Multi-Scale Neural Network

Although image inpainting based on the generated adversarial network (GAN) has made great breakthroughs in accuracy and speed in recent years, they can only process low-resolution images because of memory limitations and difficulty in training. For high-resolution images, the inpainted regions become blurred and the unpleasant boundaries become visible. Based on the current advanced image generation network, we proposed a novel high-resolution image inpainting method based on multi-scale neural network. This method is a two-stage network including content reconstruction and texture detail restoration. After holding the visually believable fuzzy texture, we further restore the finer details to produce a smoother, clearer, and more coherent inpainting result. Then we propose a special application scene of image inpainting, that is, to delete the redundant pedestrians in the image and ensure the reality of background restoration. It involves pedestrian detection, identifying redundant pedestrians and filling in them with the seemingly correct content. To improve the accuracy of image inpainting in the application scene, we proposed a new mask dataset, which collected the characters in COCO dataset as a mask. Finally, we evaluated our method on COCO and VOC dataset. the experimental results show that our method can produce clearer and more coherent inpainting results, especially for high-resolution images, and the proposed mask dataset can produce better inpainting results in the special application scene.
White Blood Cell Segmentation via Sparsity and Geometry Constraints

Digital pathology and microscopic image analysis play an important role in cell morphology research. In particular, the effective segmentation of White Blood Cells (WBCs) remains a challenging problem due to the blurring boundaries of WBCs under rapid staining, as well as the adhesion between leukocytes and other cells. In this paper, we propose a novel WBC (including nuclei and cells) segmentation algorithm based on both sparsity and geometry constraints. Specifically, we first construct a sparse image representation via combining the HSL color space and the RGB color channels, followed by the use of a sparsity constraint to only preserve useful information from the nuclei features. In addition, we introduce a robust model fitting strategy (i.e., the geometry constraint) to detect cells. Our model fitting strategy is able to significantly improve the robustness of the proposed segmentation algorithm against outliers that could seriously contaminate WBCs. The experimental results show that the proposed algorithm presents clear advantages over the state-of-the-art WBC segmentation algorithms in terms of accuracy.
Constructing Optimal Visualization Metaphor of Fuzzy Cognitive Maps on the Basis of Formalized Cognitive Clarity Criteria

The paper presents continuation of research in the field of constructing a visualization metaphor of cognitive models based on fuzzy cognitive maps.Development of this metaphor in the direction of automating the construction of a visual image of a fuzzy cognitive map, which is optimal from the point of view of cognitive clarity criteria defined in the previous part of the study, is proposed.To this end, methods have been developed to formalize several nontrivial criteria of cognitive clarity.Also, a decision rule has been introduced for choosing the optimal visual image.An example of applying the proposed metaphor, confirming its efficiency and effectiveness, is given.
Remote monitoring of a premature infants incubator

&lt;p class="MDPI17abstract"&gt;&lt;span&gt;In this paper, the air temperature and humidity levels in the infant' incubator are monitored remotely by means of Arduino microcontroller with different sensors and an open source internet of things (IoT) applications. The system is connected to a network via a wireless fidelity (Wi-Fi) connection to be linked to an application on the smart phone or to the computer. The system is designed using Arduino microcontroller, DHT11/DHT22 sensor for measuring the body parameters, such as the temperature and the humidity, LCD monitor, ESP8266 WiFi modules, and NodeMCU-v3.The results have shown that real time updated medical records can be transferred to the medical staff utilizing ThingSpeak IoT applications. &lt;/span&gt;&lt;/p&gt;
Digital Literacy Effect on the Academic Performance of Students at Higher Education Level in Pakistan

The purpose of current study was to explore the effect of digital literacy on academic performance of the students at higher education level. The study was mixed method and data were gathered with a questionnaire and semistructured interviews. The validity and reliability of the scales were ensured through experts' opinion, pilot testing and Croanbach Alpha score. The population comprised the students of M.S/M.Phil and Ph.D. A sample of 800 students was selected randomly from 10 Universities. The statistical tests like mean, standard deviation and correlation were used. Results revealed that digital literacy had significant effects on communication skills, research skills and confidence of the students and insignificant effect on students' CGPA.
On $\mathbb{Z}_{\text{8}}$ -Linear Hadamard Codes: Rank and Classification

The Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(s)</sub> -additive codes are subgroups of Z <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(s)</sub> , and can be seen as a generalization of linear codes over Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> . A Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(s)</sub> -linear Hadamard code is a binary Hadamard code which is the Gray map image of a Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(s)</sub> -additive code. It is known that either the rank or the dimension of the kernel can be used to give a complete classification for the Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -linear Hadamard codes. However, when s > 2, the dimension of the kernel of Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(s)</sub> -linear Hadamard codes of length 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> only provides a complete classification for some values of t and s. In this paper, the rank of these codes is computed for s = 3. Moreover, it is proved that this invariant, along with the dimension of the kernel, provides a complete classification, once t ≥ 3 is fixed. In this case, the number of nonequivalent such codes is also established.
EnclaveCache

With in-memory key-value caches such as Redis and Memcached being a key component for many systems to improve throughput and reduce latency, cloud caches have been widely adopted for small companies to deploy their own cache systems. However, data security is still a major concern, which affects the adoption of cloud caches. Tenant's data stored in a multi-tenant cloud environment faces threats from both co-located other tenants, as well as the untrusted cloud provider.
Data Augmentation for Deep Learning of Judgment Documents

With the increasing number of machine learning parameters, the requirements on data quantity are getting higher and higher to train a good model. The choice of methods and the optimization of parameters can improve the model while the quality and quantity of the data determine the upper limit of the model. However, in realistic scenarios, it is quite challenging to get a lot of tag data. Therefore, it is natural to realize data augmentation by transforming the original data. We use three methods for data augmentation on different scales of original data in solving the crime prediction problem based on the description of the cases, and find that the effects of data augmentation are different for different models and different fundamental data quantities.
Masalah Yang Dihadapi Dunia Pendidikan Dengan Tutorial Online: Sebuah Short Review

Despite the many benefits of virtual distance learning (online), in its course distance education still has many problems. Educators and managers try to continue to improve the quality of teaching and the struggle of online class facilitators to improve the quality of distance education, but it is discovered that online class data is not the student's first choice. The results show that one of the problems in online education comes from the drop out / failure rate of students which is more common in virtual classrooms compared to traditional classes. Other studies reveal several factors such as the time needed to complete the module, lack of understanding of the context in the subject matter, problems with software accessibility, availability of reliable teachers, lack of support from the government and other reasons can create a decrease in motivation and a less conducive learning atmosphere.
Anomaly Detection using Unsupervised Methods: Credit Card Fraud Case Study

The usage of credit card has increased dramatically due to a rapid development of credit cards.Consequently, credit card fraud and the loss to the credit card owners and credit cards companies have been increased dramatically.Credit card Supervised learning has been widely used to detect anomaly in credit card transaction records based on the assumption that the pattern of a fraud would depend on the past transaction.However, unsupervised learning does not ignore the fact that the fraudsters could change their approaches based on customers' behaviors and patterns.In this study, three unsupervised methods were presented including autoencoder, one-class support vector machine, and robust Mahalanobis outlier detection.The dataset used in this study is based on real-life data of credit card transaction.Due to the availability of the response, fraud labels, after training the models the performance of each model was evaluated.The performance of these three methods is discussed extensively in the manuscript.For one-class SVM and auto encoder, the normal transaction labels were used for training.However, the advantages of robust Mahalanobis method over these methods is that it does not need any label for its training.
An improved term weighting scheme for text classification

Summary Text representation is a necessary and primary procedure in performing text classification (TC), which first needs to be obtained through an information‐rich term weighting scheme to achieve higher TC performance. So far, term frequency‐inverse document frequency (TF‐IDF) is the most widely used term weighting scheme, but it suffers from two deficiencies. First, the global weighting factors IDF in TF‐IDF approaches infinity if a certain term does not occur in a text. Second, the IDF is equal to zero if a certain term appears in any text. To offset these drawbacks, we first conduct an in‐depth analysis of the current term weighting schemes, and subsequently, an improved term weighting scheme called term frequency‐inverse exponential frequency (TF‐IEF) and its various variants are proposed. The proposed method replaces IDF with the new global weighting factor IEF to characterize the global weighting factor log‐like IDF in the corpus, which can greatly reduce the effect of feature (term) with high local weighting factor TF in term weighting. As a result, a more representative feature can be generated. We carried out a series of experiments on two commonly used data sets (corpora) utilizing Naïve Bayes and support vector machine classifiers to validate the performance of our proposed schemes. Experimental results explicitly reveal that the proposed term weighting schemes come with better performance than the compared schemes.
Machine Learning Assisted Side-Channel-Attack Countermeasure and Its Application on a 28-nm AES Circuit

Hardware countermeasure of side channel attack (SCA) becomes necessary to protect crypto circuits. Many countermeasures endured large area and power consumption. We propose a SCA-resistant methodology based on machine learning, which compensates the Hamming distance (HD) probability of the intermediate data directly. By making the HD probabilities unable to be distinguished from correct and incorrect sub-keys, it provides resistance to SCA. Optimum HD redistribution is obtained by a machine learning algorithm and then sent to the compensation circuit. Applied in an Advanced Encryption Standard (AES)-128 circuit, the whole compensated circuit is implemented on a 28-nm CMOS process. The experimental results show that it resists correlation-based SCA with 1.5 million traces, corresponding to 446× improvements of measures to disclosure compared with a nonprotected AES circuit. In addition, it has no impact on the frequency and throughput rate, and its power overhead of 38% and area overhead of 36% are relatively low, making it suitable for resource-constrained encryption circuits.
Momental directional patterns for dynamic texture recognition

Understanding the chaotic motions of dynamic textures (DTs) is a challenging problem of video representation for different tasks in computer vision. This paper presents a new approach for an efficient DT representation by addressing the following novel concepts. First, a model of moment volumes is introduced as an effective pre-processing technique for enriching the robust and discriminative information of dynamic voxels with low computational cost. Second, two important extensions of Local Derivative Pattern operator are proposed to improve its performance in capturing directional features. Third, we present a new framework, called Momental Directional Patterns, taking into account the advantages of filtering and local-feature-based approaches to form effective DT descriptors. Furthermore, motivated by convolutional neural networks, the proposed framework is boosted by utilizing more global features extracted from max-pooling videos to improve the discrimination power of the descriptors. Our proposal is verified on benchmark datasets, i.e., UCLA, DynTex, and DynTex++, for DT classification issue. The experimental results substantiate the interest of our method.