Reviewerly/exHarmony huggingface dataset:
Classifier Probes May Just Learn from Linear Context Features

Classifiers trained on auxiliary probing tasks are a popular tool to analyze the representations learned by neural sentence encoders such as BERT and ELMo.While many authors are aware of the difficulty to distinguish between "extracting the linguistic structure encoded in the representations" and "learning the probing task," the validity of probing methods calls for further research.Using a neighboring word identity prediction task, we show that the token embeddings learned by neural sentence encoders contain a significant amount of information about the exact linear context of the token, and hypothesize that, with such information, learning standard probing tasks may be feasible even without additional linguistic structure.We develop this hypothesis into a framework in which analysis efforts can be scrutinized and argue that, with current models and baselines, conclusions that representations contain linguistic structure are not well-founded.Current probing methodology, such as restricting the classifier's expressiveness or using strong baselines, can help to better estimate the complexity of learning, but not build a foundation for speculations about the nature of the linguistic structure encoded in the learned representations.
MultiEmotions-It: a New Dataset for Opinion Polarity and Emotion Analysis for Italian

This paper1 presents a new linguistic resource for Italian, called MultiEmotions-It, containing comments to music videos and advertisements posted on YouTube and Facebook. These comments are manually annotated according to four different dimensions: i.e., relatedness, opinion polarity, emotions and sarcasm. For the annotation of emotions we adopted the Plutchik's model taking into account both basic and complex emotions, i.e. dyads.
Combined Deep Learning With Directed Acyclic Graph SVM for Local Adjustment of Age Estimation

In order to further improve the accuracy of age estimation, a locally adjusted age estimation algorithm based on deep learning and directed acyclic graph SVM is proposed.In the training phase, SE-ResNet-50 network pre-trained by the VGGFace2 dataset is first fine-tuned.Once the network converges, and the vector consisting of the parameters of the last fully connected layer is used as a representation and train multiple One-Versus-One SVMs.In the test phase, we first sent the face image to be estimated into SE-ResNet-50 to obtain a rough age estimation value, then set the specific neighborhood, and finally combined the trained SVM into a directed acyclic graph SVM and set specific neighborhood with the global estimate as the center for accurate age estimate.In order to show the universality of the proposed coarse-tofine or/and global-to-local method, experiments were carried out on MORPH and AFAD images of different races, and the results verified the effectiveness of the algorithm.
Interpretable Multi-headed Attention for Abstractive Summarization at Controllable Lengths

Abstractive summarization at controllable lengths is a challenging task in natural language processing.It is even more challenging for domains where limited training data is available or scenarios in which the length of the summary is not known beforehand.At the same time, when it comes to trusting machine-generated summaries, explaining how a summary was constructed in human-understandable terms may be critical.We propose Multi-level Summarizer (MLS), a supervised method to construct abstractive summaries of a text document at controllable lengths.The key enabler of our method is an interpretable multi-headed attention mechanism that computes attention distribution over an input document using an array of timestep independent semantic kernels.Each kernel optimizes a human-interpretable syntactic or semantic property.Exhaustive experiments on two low-resource datasets in English language show that MLS outperforms strong baselines by up to 14.70% in the METEOR score.Human evaluation of the summaries also suggests that they capture the key concepts of the document at various length-budgets.
If the Coronavirus Doesn’t Scare You, the Banners Will—A Case Study of Early COVID-19 Banners

As a crucial element of China’s political and cultural life, “banners”, or biāoyǔ, have been around for decades, in support of national-level policies such as family planning and the governing mottos of Presidents. The banners that have emerged during the Covid-19 pandemic which was also the subject of a national-level driven policy, have been involved in a nation-wide public debate over the language styles of banners used to urge people to stay indoors. Based on the analysis of the early COVID-19 banners and the related online comments, this article analyzes the language style patterns of the banners and the mode of banner circulation. The study found that the manner in which the banners are circulated goes beyond a unidirectional path of on-site instant communication. This process is facilitated by social networks and mass media, which, during circulation, twice created a banner upgrade. The upgrades created decontextualization and function extension of the banners, whereas audience feedback triggered an adaptive adjustment of the language style of the banners. This article suggests that the study of the use and spread of banners, especially the early COVID-19 banners, sheds light on the study of mass communication and discourse style, and also how measures to contain pandemics such as COVID-19 can be communicated.
A LOGISTIC REGRESSION BASED HYBRID MODEL FOR BREAST CANCER CLASSIFICATION

Data mining techniques are being used for breast cancer classification and good performance accuracy has been obtained while using the techniques individually or as ensembles.A notable problem is the skewed nature of the data which leads to imbalance among the output classes.The minority class being the negative class usually are smaller in number than the positive majority class.This usually leads to a moderate accuracy value for the classifier.The correct classification of minority cases is a significant problem.All classes need to be given equal importance during classification.In this study a hybrid model based on Logistic Regression is implemented with class balancing and ant search techniques and the performance is evaluated on the two class Wisconsin breast cancer dataset.A performance accuracy of 99.4% was obtained.
Forecasting future corn and soybean prices: an analysis of the use of textual information to enrich time-series.

The commodities corn and soybean are products consumed on a large scale in the world. Fluctuations in market prices have far-reaching effects on consumers, farmers, and grain processors. Thus, forecasting the prices of these grains has attracted significant attention from researchers. Forecasting models generally use quantitative time-series data. However, external qualitative factors can influence data in time-series, such as political events, economic crises, and the foreign exchange market. This information is not explicit in the time-series data, and these factors can influence the prediction of the variable values. Textual data extracted from news, forums, and social networks can be a source of knowledge about external factors and potentially useful for time-series forecasting models. Some studies present text mining techniques to combine textual data with time-series. However, the existing representations have some limitations, such as the curse of dimensionality and ineffective attributes. This work applies pre-processing methods in time-series and uses representations combined with textual data to predict the future price of corn and soybeans. The results indicate that the methods used can be an alternative to improve forecasting performance in regression tasks.
Robust Learning of Mixtures of Gaussians

We resolve one of the major outstanding problems in robust statistics. In particular, if X is an evenly weighted mixture of two arbitrary d-dimensional Gaussians, we devise a polynomial time algorithm that given access to samples from X an ∊-fraction of which have been adversarially corrupted, learns X to error poly(∊) in total variation distance.
Research Reproducibility as a Survival Analysis

There has been increasing concern within the machine learning community that we are in a reproducibility crisis. As many have begun to work on this problem, all work we are aware of treat the issue of reproducibility as an intrinsic binary property: a paper is or is not reproducible. Instead, we consider modeling the reproducibility of a paper as a survival analysis problem. We argue that this perspective represents a more accurate model of the underlying meta-science question of reproducible research, and we show how a survival analysis allows us to draw new insights that better explain prior longitudinal data. The data and code can be found at https://github.com/EdwardRaff/Research-Reproducibility-Survival-Analysis
Chinese Paragraph-level Discourse Parsing with Global Backward and Local Reverse Reading

Discourse structure tree construction is the fundamental task of discourse parsing and most previous work focused on English.Due to the cultural and linguistic differences, existing successful methods on English discourse parsing cannot be transformed into Chinese directly, especially in paragraph level suffering from longer discourse units and fewer explicit connectives.To alleviate the above issues, we propose two reading modes, i.e., the global backward reading and the local reverse reading, to construct Chinese paragraph level discourse trees.The former processes discourse units from the end to the beginning in a document to utilize the left-branching bias of discourse structure in Chinese, while the latter reverses the position of paragraphs in a discourse unit to enhance the differentiation of coherence between adjacent discourse units.The experimental results on Chinese MCDTB demonstrate that our model outperforms all strong baselines.
Research on Resource Scheduling of Cloud Computing Based on Improved Genetic Algorithm

In order to solve the problem that the resource scheduling time of cloud data center is too long, this paper analyzes the two-stage resource scheduling mechanism of cloud data center. Aiming at the minimum task completion time, a mathematical model of resource scheduling in cloud data center is established. The two-stage resource scheduling optimization simulation is realized by using the conventional genetic algorithm. On the technology of the conventional genetic algorithm, an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm. The experimental results show that the improved genetic algorithm can significantly reduce the total completion time of the task, and has good convergence and global optimization ability.
Analogy Models for Neural Word Inflection

Analogy is assumed to be the cognitive mechanism speakers resort to in order to inflect an unknown form of a lexeme based on knowledge of other words in a language.In this process, an analogy is formed between word forms within an inflectional paradigm but also across paradigms.As neural network models for inflection are typically trained only on lemma-target form pairs, we propose three new ways to provide neural models with additional source forms to strengthen analogy-formation, and compare our methods to other approaches in the literature.We show that the proposed methods of providing a Transformer sequence-to-sequence model with additional analogy sources in the input are consistently effective, and improve upon recent state-of-the-art results on 46 languages, particularly in low-resource settings.We also propose a method to combine the analogy-motivated approach with data hallucination or augmentation.We find that the two approaches are complementary to each other and combining the two approaches is especially helpful when the training data is extremely limited.
Translation, from Pen-and-Paper to Computer-Assisted Tools (CAT Tools) and Machine Translation (MT)

This paper reflects the technology-induced novelty of translation, which is perceived as a bridge between languages and cultures.We debate the extent to which the translation process maintains its specificity in the light of the new technology-enhanced working methods ensured by a large variety of Computer-Assisted Translation (CAT) and Machine Translation (MT) tools that aim to enhance the process, which includes the translation itself, the translator, the translation project manager, the linguist, the terminologist, the reviewer, and the client.This paper also hints at the topic from the perspective of the translation teacher, who needs to provide students with transversal competencies that are suitable for the digital area, supported by the ability to tackle Cloud-based translation tools, in view of Industry 4.0 requirements.
USER EXPERIENCE (UX) OF MULTIMEDIA; PROGRAM OF AUTO PLAY MEDIA STUDIO FOR TEACHER PROFESSIONAL EDUCATION (PPG)

A factor that can influence the success of learning process is the use of learning media. Therefore, this research aims to evaluate the user experience of multimedia using the program of Auto play media studio for Teacher Professional Education (PPG). The research problem is how the teachers use multimedia in learning and how the teachers develop the multimedia by themselves using the computer technology program of media studio auto play. The research method is the user experience (UX) using the instrument from User Experience Questionnaire (UEQ) of Indonesian version, because there are several language versions of the questionnaire built and validated (for example, English, Spanish, Portuguese, etc.) These versions are available for free and online at www.ueq-online.org. The research result from the test of UEQ is that there are five scales which are categorized as excellent; they are Attractiveness, Efficiency, Dependability, Stimulation and Novelty, whereas the scale of Perspicuity is categorized of Above Average according to the interval benchmark of UEQ. All scores are above 0,08 shows that the evaluation of UX is positive. It can be concluded that the multimedia learning using the program of is very good – satisfying to use for the Teacher Professional Education for Islamic Education subject (PAI).
Predicting Personal Opinion on Future Events with Fingerprints

Predicting users' opinions in their response to social events has important real-world applications, many of which with political and social impacts.Existing approaches derive a population's opinion on a going event from large scores of user generated content.In certain scenarios, we may not be able to acquire such content and thus cannot infer opinion on those emerging events.To address this problem, we propose to explore opinion on unseen articles based on an user's fingerprinting: the prior reading and commenting history.This work presents a focused study on modeling and leveraging fingerprinting techniques to predict a user's future opinion to an unseen event or topic.We introduce a recurrent neural network based model that integrates fingerprinting.We collect a large dataset that consists of event-comment pairs from six news websites.We evaluate the proposed model on this dataset.The results show substantial performance gains demonstrating the effectiveness of our approach.
De-correlated unbiased sequential filtering based on best unbiased linear estimation for target tracking in Doppler radar

In target tracking applications, the Doppler measurement contains information of the target range rate, which has the potential capability to improve the tracking performance.However, the nonlinear degree between the measurement and the target state increases with the introduction of the Doppler measurement.Therefore, target tracking in the Doppler radar is a nonlinear filtering problem.In order to handle this problem, the Kalman filter form of best linear unbiased estimation (BLUE) with position measurements is proposed, which is combined with the sequential filtering algorithm to handle the Doppler measurement further, where the statistic characteristic of the converted measurement error is calculated based on the predicted information in the sequential filter.Moreover, the algorithm is extended to the maneuvering target tracking case, where the interacting multiple model (IMM) algorithm is used as the basic framework and the model probabilities are updated according to the BLUE position filter and the sequential filter, and the final estimation is a weighted sum of the outputs from the sequential filters and the model probabilities.Simulation results show that compared with existing approaches, the proposed algorithm can realize target tracking with preferable tracking precision and the extended method can achieve effective maneuvering target tracking.
A Cryptography Core Tolerant to DFA Fault Attacks

This work describes a hardware approach for the concurrent fault detection and error correction in a cryptographic core. It has been shown in the literature that transient faults injected in a cryptographic core can lead to the revelation of the encryption key using quite inexpensive equipments. This kind of attack is a real threat to tamper resistant devices like Smart Cards. To tackle such attacks, the cryptographic core must be immune to transient faults. In this work the DES algorithm is taken as a vulnerable cryptosystem case study.We show how an attack against DES is performed through a fault injection campaign. Then, a countermeasure based on partial hardware replication is proposed and applied to DES. Experimental results show the efficiency of the proposed scheme to protect DES against DFA fault attacks. Furthermore, the proposed solution is independent of implementation, and can be applied to other cryptographic algorithms, such as AES.
Siamese Attentional Cascade Keypoints Network for Visual Object Tracking

Visual object tracking is urgent yet challenging work since it requires the simultaneous and effective classification and estimation of a target. Thus, research on tracking has been attracting a considerable amount of attention despite the limitations of existing trackers owing to deformation, occlusion and motion. For most current tracking methods, researchers have proposed various ways to adopt a multi-scale search or anchors for estimation, but these methods always need prior knowledge and too many hyperparameters. To address these issues, we proposed a novel Siamese Attentional Cascade Keypoints Tracking Network named SiamACN to exactly track the object by using keypoints prediction instead of anchors. Compared to complex target prediction, the anchor-free method is performed to avoid plaguy hyperparameters, and a simplified hourglass network with global attention is considered the backbone to improve the tracking efficiency. Further, our framework uses keypoints prediction around the target with cascade corner pooling to simplify the model. To certificate the superiority of our framework, extensive tests are conducted on five tracking benchmarks, including OTB-2015, VOT-2016, VOT-2018, LaSOT and UAV123. Our method achieves the leading performance with an accuracy of 61.2% on VOT2016 and favorably runs at 32 FPS against other competing algorithms, which confirms its effectiveness in real-time applications.
Recent Progress in Automated Code Generation from GUI Images Using Machine Learning Techniques

The manual transformation of a user interface design into code is a costly and time-consuming process. A solution can be the automation of the generation of code based on sketches or GUI design images. Recently, Machine Learning approaches have shown promising results in detecting GUI elements for such automation. Thus, to provide an overview of existing approaches, we performed a systematic mapping study. As a result, we identified and compared 20 approaches, that demonstrate good performance results being considered useful. These results can be used by researchers and practitioners in order to improve the efficiency of the GUI design process as well as continue to evolve and improve approaches for its support.
An efficient biometric-based continuous authentication scheme with HMM prehensile movements modeling

Biometric is an emerging technique for user authentication thanks to its efficiency compared to the traditional methods, such as passwords and access-cards. However, most existing biometric authentication systems require the cooperation of users and provide only a login time authentication. To address these drawbacks, we propose in this paper a new, efficient continuous authentication scheme based on the newly biometric trait that still under development: prehensile movements. In this work, we model the movements through Hidden Markov Model-Universal Background Model (HMM-UBM) with continuous observations based on Gaussian Mixture Model (GMM). Unlike the literature, the gravity signal is included. The results of the experiments conducted on a public database HMOG and on a proprietary database, collected under uncontrolled conditions, have shown that prehensile movements are very promising. This new biometric feature will allow users to be authenticated continuously, passively and in real time.
EFL Learners’ Perspectives on the use of Smartphones in Higher Education Settings in Slovakia

MALL (Mobile assisted language learning) affords new opportunities for EFL (English as a foreign language) learners and teachers. Research on MALL is still in its infancy in Slovakia, and this paper attempts to fill in this gap by examining students’ perception and attitudes towards the use of smartphones for the purposes of learning and practicing English. The target population of this study constituted of the Slovak university EFL learners whose major was Teacher Training of English Language and Literature (n = 77) at a Slovak university. The research method employed to achieve the objectives of this study was a 5‑point Likert scale questionnaire, comprising of two sets of statements: general and out‑of‑the‑classroom statements with a total of 29 items. The research results for both sets of statements imply that the participants display moderately positive attitudes towards smartphones in the context of EFL learning. However, the findings also reveal some issues surrounding the perception and potential use of smartphones such as the inability to plan students’ language learning appropriately and effectively, general underuse of smartphone apps, or problems related to practicing speaking skills. The results further suggest the immediate need to develop and enhance the awareness of smartphones and their potential in the process of teaching and learning English so that the EFL learners can utilize considerable opportunities these smart devices offer. Finally, the limitations of this study are recognized, and it is emphasized that conducting further research in this area is urgently needed.
H-Rotation: Secure Storage and Retrieval of Passphrases on the Authentication Process

Passwords/passphrases can be either system generated or user-selected. A combination of both approaches is also possible—encryption created by the system and assigned to the user by the information system meeting the policy requirements. Policy rules can be designed to increase security and usability factors, such as information storage and retrieval. This paper proposes an algorithm dedicated to the security of passphrases in an online authentication, so the passphrase entered will be stored in a remote database. Through an SHA-3 hash function, the system must hash the pass phase. Before storage, the system must apply random rotations on the already generated HASH while eliminating any traceability performed on the different transactions performed. To prevent the hacker from using them recurrently if he wants to attack our database. Then the system must recover the real HASH and then the passphrase based on the data provided by the user in the form of codes.