Reviewerly/exHarmony huggingface dataset:
Quarantine Quibbles: A Sentiment Analysis of COVID-19 Tweets

The 3-grams would be "I went to", and "went to work"• The 4-gram would be "I went to work" 4) Prune the list of candidate aspects in order to create a more-relevant list of aspects 5) Cross compare the pruned aspects to the original data in order to find sentimental connections between the data.
TWEETSUM: Event oriented Social Summarization Dataset

With social media becoming popular, a vast of short and noisy messages are produced by millions of users when a hot event happens.Developing social summarization systems becomes more and more critical for people to quickly grasp core and essential information.However, the publicly available and high-quality large scale dataset under social media situation is rare.Constructing such corpus is not easy and very expensive since short texts have very complex social characteristics.Though there exist some datasets, they only consider the text on social media and ignore the potential user relations relevant signals on social network.In this paper, we construct TWEETSUM, a new event-oriented dataset for social summarization.The original data is collected from twitter and contains 12 real world hot events with a total of 44,034 tweets and 11,240 users.We create expert summaries for each event, and we also have the annotation quality evaluation.In addition, we collect additional social signals (i.e.user relations, hashtags and user profiles) and further establish user relation network for each event.To our knowledge, it is the first event-oriented social summarization dataset that contains social relationships.Besides the detailed dataset description, we show the performance of several typical extractive summarization methods on TWEETSUM to establish baselines.For further researches, we will release this dataset to the public.
Sentiment Forecasting in Dialog

Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation.However, the polarity of next utterance is normally hard to predict, due to the lack of content of next utterance (yet to come).In this study, we propose a Neural Sentiment Forecasting (NSF) model to address inherent challenges.In particular, we employ a neural simulation model to simulate the next utterance based on the context (previous utterances encountered).Moreover, we employ a sequence influence model to learn both pair-wise and seq-wise influence.Empirical studies illustrate the importance of proposed sentiment forecasting task, and justify the effectiveness of our NSF model over several strong baselines.
Fuzzy Q-Learning Traffic Light Control based on Traffic Flow and Pedestrian Number Estimated from Visual Information

A vision-based intelligent traffic control system is a robust framework that controls the traffic flow in real-time by estimating the traffic density near traffic lights. In this paper, a traffic light control system based on fuzzy Q-learning is proposed according to the vehicle density and the pedestrian number estimated from the visual information. The aim of proposed approach is to minimize the pedestrian and the car waiting time and maximize throughput for an isolated 4-way traffic intersection. Also, the pedestrian traffic light is controlled based on the fuzzy logic. The states and actions of the Q-learning variables are set by a fuzzy algorithm which can be learned through environmental interactions. The system can detect the number of pedestrians and vehicles using visual information from cameras and machine vision algorithms. The fuzzy control system can adjust the sequence of green phases to decrease the total waiting time and the mean of the queue length. The proposed algorithm was simulated for one hour for each of 14 different traffic conditions and was assessed and compared with the preset cycle time and vehicle actuated approaches. The results showed the proposed algorithm could decrease the total waiting time and the mean of the queue length effectively.
CoNAN: A Complementary Neighboring-based Attention Network for Referring Expression Generation

Daily scenes are complex in the real world due to occlusion, undesired lighting conditions, etc.Although humans handle those complicated environments well, they evoke challenges for machine learning systems to identify and describe the target without ambiguity.Most previous research focuses on mining discriminating features within the same category for the target object.One the other hand, as the scene becomes more complicated, human frequently uses the neighbor objects as complementary information to describe the target one.Motivated by that, we propose a novel Complementary Neighboring-based Attention Network (CoNAN) that explicitly utilizes the visual differences between the target object and its highly-related neighbors.These highly-related neighbors are determined by an attentional ranking module, as complementary features, highlighting the discriminating aspects for the target object.The speaker module then takes the visual difference features as an additional input to generate the expression.Our qualitative and quantitative results on the dataset RefCOCO, RefCOCO+, and RefCOCOg demonstrate that our generated expressions outperform other state-of-the-art models by a clear margin.
Retro-Reflective-Marker-Aided Target Pose Estimation in a Safety-Critical Environment

Visual technologies have an indispensable role in safety-critical applications, where tasks must often be performed through teleoperation. Due to the lack of stereoscopic and motion parallax depth cues in conventional images, alignment tasks pose a significant challenge to remote operation. In this context, machine vision can provide mission-critical information to augment the operator’s perception. In this paper, we propose a retro-reflector marker-based teleoperation aid to be used in hostile remote handling environments. The system computes the remote manipulator’s position with respect to the target using a set of one or two low-resolution cameras attached to its wrist. We develop an end-to-end pipeline of calibration, marker detection, and pose estimation, and extensively study the performance of the overall system. The results demonstrate that we have successfully engineered a retro-reflective marker from materials that can withstand the extreme temperature and radiation levels of the environment. Furthermore, we demonstrate that the proposed maker-based approach provides robust and reliable estimates and significantly outperforms a previous stereo-matching-based approach, even with a single camera.
On quantum bit coding by Gaussian beam modes for the quantum key distribution

This paper deals with possibility of implementation of quantum key distribution algorithm through turbulent atmosphere.Beam wandering is considered as the main perturbation.For description of the density matrix, the Glauber-Sudarshan P-function technique is used.The probabilities of detectors triggering are determined.
Incremental Learning of Infrared Vehicle Detection Method Based on SSD

An incremental Learning of infrared vehicle detection method based on SSD(Single Shot MultiBox Detector) was proposed to solve the problems such as lack of detail in infrared vehicle images, difficulty in extracting feature information and low detection accuracy. Through incremental learning and training, the CNN detection model based on SSD was fine-tuning, and the Pascal VOC data set was pre-trained. Through this detection method, the infrared vehicle could be effectively identified and located effectively. Finally, the results of infrared vehicle detection using incremental data set and non-incremental data set are compared. The experimental results show that the error detection and missing detection of infrared vehicle detection are obviously improved by using the incremental data set, and the mAP is improved by 10.61%.
Sistem Pendukung Keputusan Seleksi Calon Karyawan Baru PT. Dawam Prima Perkasa Menggunakan Metode Aras Berbasis Web

Employees are one of the supporting factors for a company, because by having qualified employees, according to the qualifications and criteria needed by the company, then the company can develop and move forward in the future. No exception, PT. Dawam Prima Perkasa who is in need of new employees who are suitable to work in the company. However, PT. Dawam Prima Perkasa is faced with a problem where the large number of prospective employees who take the selection test will cause the number of incoming files to be adjusted to the criteria owned by the company and require a lot of time, making it prone to file errors. To minimize the occurrence of errors and the length of time used, a Decision Support System was created to determine the best prospective employees who will work in the company. The Decision Support System Method used is the Additive Ratio Assessment (ARAS). This research has gone through the process of validating the Spearman rank correlation and the value is 0.950. Based on these results, the ARAS method can be used in selecting prospective new employees at PT. Dawam prima Perkasa.
Cryptocurrency: A Mine of Controversies

Cryptocurrency is attracting the attention of many disciplines. Based on a systematic literature review, the state of art of academic research on cryptocurrency was investigated, demonstrating its complexity and the lack of consensus about several issues, as its definition, its operation without a financial institution, the impacts on economy and its future developments. Considering these issues, Actor-Network Theory was selected as a theory that can provide methods, such as controversy mapping, to understand this complex subject. The article analyzes this scenario, presenting a set of research topics that can be considered to study controversies related to cryptocurrency.
New generalized almost perfect nonlinear functions

APN (almost perfect non-linear) functions over finite fields of even characteristic are widely studied due to their applications to the design of symmetric ciphers resistant to differential attacks. This notion was recently generalized to GAPN (generalized APN) functions by Kuroda and Tsujie to odd characteristic p. They presented some constructions of GAPN functions, and other constructions were given by Zha et al. We present new constructions of GAPN functions both in the case of monomial and multinomial functions. Our monomial GAPN functions can be viewed as a further generalization of the Gold APN functions. We show that a certain technique used by Hou to construct permutations over finite fields also yields monomial GAPN functions. We also present several new constructions of GAPN functions which are sums of monomial GAPN functions, as well as new GAPN functions of degree p which can be written as the product of two powers of linearized polynomials. For this latter construction we describe some interesting differences between even and odd characteristic and also obtain a classification in certain cases.
rmassidda @ DaDoEval: Document Dating Using Sentence Embeddings at EVALITA 2020

This report describes an approach to solve the DaDoEval document dating subtasks for the EVALITA 2020 competition. The dating problem is tackled as a classification problem, where the significant length of the documents in the provided dataset is addressed by using sentence embeddings in a hierarchical architecture. Three different pre-trained models to generate sentence embeddings have been evaluated and compared: USE, LaBSE and SBERT. Other than sentence embeddings the classifier exploits a bag-of-entities representation of the document, generated using a pre-trained named entity recognizer. The final model is able to simultaneously produce the required date for each subtask.
Evaluation of Sustainable Use of Water Resources Based on Random Forest: A Case Study in the Lishui River Basin, Central China

Xie, C.; Chao, L.; Shi, D., and Ni, Z., 2020. Evaluation of sustainable use of water resources based on random forest: A case study in the Lishui River basin, Central China. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 134–136. Coconut Creek (Florida), ISSN 0749-0208.The evaluation of the sustainable use of water resources is an important part in the study of the ideal allocation of water resources. Selecting relevant evaluation indices with diverse scientific characteristics directly affects the accuracy of the results of water resource assessment. A machine learning method, random forest (RF), was applied to evaluate the sustainable use of water resources in the Lishui River basin of Hunan province. Python software was used to analyze eight correlating indices of the data set, create the visual matrix diagram of the original data collection and distribution points, and then analyze the evaluation results of this method. Based on a comparison with artificial neural networks (ANN) and support vector machines (SVM), the RF method evaluation results were considered reasonable and reliable with greater stability, accuracy, and practicability, and a classification evaluation standard rate of 99.6%.
Model-agnostic Methods for Text Classification with Inherent Noise

Text classification is a fundamental problem, and recently, deep neural networks (DNN) have shown promising results in many natural language tasks. However, their human-level performance relies on high-quality annotations, which are time-consuming and expensive to collect. As we move towards large inexpensive datasets, the inherent label noise degrades the generalization of DNN. While most machine learning literature focuses on building complex networks to handle noise, in this work, we evaluate model-agnostic methods to handle inherent noise in large scale text classification that can be easily incorporated into existing machine learning workflows with minimal interruption. Specifically, we conduct a point-by-point comparative study between several noise-robust methods on three datasets encompassing three popular classification models. To our knowledge, this is the first time such a comprehensive study in text classification encircling popular models and model-agnostic loss methods has been conducted. In this study, we describe our learning and demonstrate the application of our approach, which outperformed baselines by up to 10 % in classification accuracy while requiring no network modifications.
Analogies and Disanalogies Between Machine-Driven and Human-Driven Legal Judgement

Are there certain desirable properties from text-driven law, which have parallels in data-driven law? As a preliminary exercise, this article explores a range of analogies and disanalogies between text-driven normativity and its data-driven counterparts. Ultimately, the conclusion is that the analogies are weaker than the disanalogies. But the hope is that, in the process of drawing them, we learn something more about the comparison between text and data-driven normativities and the (im?)possibility of data-driven law.Reply by Emily M. Bender, Professor of Computational Linguistics, University of Washington.
XplaiNLI: Explainable Natural Language Inference through Visual Analytics

Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is.Recent research has revealed some heuristics and biases of these models.However, to date, there is no systematic effort to capitalize on those insights through a system that uses these to explain the NLI decisions.To this end, we propose XplaiNLI, an eXplainable, interactive, visualization interface that computes NLI with different methods and provides explanations for the decisions made by the different approaches.
A New Random Forest Method for Longitudinal Data Classification Using a Lexicographic Bi-Objective Approach

Standard supervised machine learning methods often ignore the temporal information represented in longitudinal data, but that information can lead to more precise predictions in classification tasks. Data preprocessing techniques and classification algorithms can be adapted to cope directly with longitudinal data inputs, making use of temporal information such as the time-index of features and previous measurements of the class variable. In this article, we propose two changes to the classification task of predicting age-related diseases in a real-world dataset created from the English Longitudinal Study of Ageing. First, we explore the addition of previous measurements of the class variable, and estimating the missing data in those added features using intermediate classifiers. Second, we propose a new split-feature selection procedure for a random forest's decision trees, which considers the candidate features' time-indexes, in addition to the information gain ratio. Our experiments compared the proposed approaches to baseline approaches, in 3 prediction scenarios, varying the “time gap” for the prediction - how many years in advance the class (occurrence of an age-related disease) is predicted. The experiments were performed on 10 datasets varying the class variable, and showed that the proposed approaches increased the random forest's predictive accuracy.
Anomaly Detection for Consortium Blockchains Based on Machine Learning Classification Algorithm

Although the consortium blockchains commonly adopt consensus algorithms with Byzantine fault tolerance (such as practical Byzantine fault tolerance (PBFT)), its consensus efficiency will be degraded by the existence of malicious nodes or behaviors. However, the existing researches mainly focus on the detection of malicious behaviors for public blockchains, but are rare about consortium blockchains. In this paper, an anomaly detection model based on machine learning (ML) classification algorithm is proposed for consortium blockchains that adopt PBFT. Besides, a two-stage process is proposed to reduce the resource consumption for anomaly detection. The data needed for proposed model only has two dimensions and is convenient to obtain. The results of experiment show that ML is very effective in anomaly detection for consortium blockchains. Specifically, the algorithms with the highest accuracy are convolutional neural networks (CNN), k-nearest neighbor (KNN) and support vector machines (SVM) in turn. However, KNN and SVM are more suitable because resource consumption of both algorithms are one third of CNN, and the accuracy rates are above 0.9 which is 0.9% lower than CNN.
Contact Relatedness can help improve multilingual NMT: Microsoft STCI-MT @ WMT20

We describe our submission for the English→Tamil and Tamil→English news translation shared task. In this submission, we focus on exploring if a low-resource language (Tamil) can benefit from a high-resource language (Hindi) with which it shares contact relatedness. We show utilizing contact relatedness via multilingual NMT can significantly improve translation quality for English-Tamil translation.
Convolutional neural network simplification with progressive retraining

Kernel pruning methods have been proposed to speed up, simplify, and improve explanation of convolutional neural network (CNN) models. However, the effectiveness of a simplified model is often below the original one. In this letter, we present new methods based on objective and subjective relevance criteria for kernel elimination in a layer-by-layer fashion. During the process, a CNN model is retrained only when the current layer is entirely simplified, by adjusting the weights from the next layer to the first one and preserving weights of subsequent layers not involved in the process. We call this strategy \emph{progressive retraining}, differently from kernel pruning methods that usually retrain the entire model after each simplification action -- e.g., the elimination of one or a few kernels. Our subjective relevance criterion exploits the ability of humans in recognizing visual patterns and improves the designer's understanding of the simplification process. The combination of suitable relevance criteria and progressive retraining shows that our methods can increase effectiveness with considerable model simplification. We also demonstrate that our methods can provide better results than two popular ones and another one from the state-of-the-art using four challenging image datasets.
Solar Radiation Climatology in Camagüey, Cuba (1981–2016)

The transition to renewable energies is an unavoidable step to guarantee a peaceful and sustainable future for humankind. Although solar radiation is one of the main sources of renewable energy, there are broad regions of the planet where it has not been characterized appropriately to provide the necessary information for regional and local planning and design of the different solar powered systems. The Caribbean, and Cuba in particular, lacked until very recently at least one long-term series of surface solar radiation measurements. Here we present the first long-term records of solar radiation for this region. Solar radiation measurements manually conducted and recorded on paper were rescued, reprocessed and quality controlled to develop the solar radiation climatology at the Actinometrical Station of Camagüey, in Cuba (21.422°N; 77.850°W; 122 m a.s.l.) for the period 1981–2016. The diurnal cycle based on the average hourly values of the global, direct and diffuse horizontal variables for the entire period have been determined and analyzed showing the dependence on solar zenith angle (SZA) and clouds. The annual cycle of global solar component given by the mean monthly daily values presents two maxima, one in April and another one in July with values of 5.06 and 4.91 kWh m−2, respectively (18.23 and 17.67 MJ m−2 per day for insolation), and the minimum in December (3.15 kWh m−2 or 11.33 MJ m−2). The maxima are governed by the direct solar components and are modulated by cloudiness. Both, diurnal and annual cycles of the diffuse solar component show a smoothed bell shaped behavior. In general solar radiation at this station presents a strong influence of clouds, with little seasonal variation but with higher values during the rainy season. Daily global radiation annual averages showed its maximum value in the year 1983, with 17.45 MJ m−2 explained by very low cloudiness this year, and the minimum value was reported in 2009 with a value of 12.43 MJ m−2 that could not explained by the cloud coverage or the aerosols optical depths registered that year. The effects of the 1982 El Chichón and 1991 Mount Pinatubo volcanic eruptions on the solar radiation variables at Camagüey are also shown and discussed. The results achieved in this study shown the characteristics of solar radiation in this area and their potential for solar power applications.
Ensemble BERT for Classifying Medication-mentioning Tweets

Twitter is a valuable source of patient-generated data that has been used in various population health studies. The first step in many of these studies is to identify and capture Twitter messages (tweets) containing medication mentions. In this article, we describe our submission to Task 1 of the Social Media Mining for Health Applications (SMM4H) Shared Task 2020. This task challenged participants to detect tweets that mention medications or dietary supplements in a natural, highly imbalance dataset. Our system combined a handcrafted preprocessing step with an ensemble of 20 BERT-based classifiers generated by dividing the training dataset into subsets using 10-fold cross validation and exploiting two BERT embedding models. Our system ranked first in this task, and improved the average F1 score across all participating teams by 19.07% with a precision, recall, and F1 on the test set of 83.75%, 87.01%, and 85.35% respectively.