Reviewerly/exHarmony huggingface dataset:
Discovering Topic Representative Terms for Short Text Clustering

Clustering short texts are one of the most important text analysis methods to help extract knowledge from online social media platforms, such as Twitter, Facebook, and Weibo.However, the instant features (such as abbreviation and informal expression) and the limited length of short texts challenge the clustering task.Fortunately, short texts about the same topic often share some common terms (or term stems), which can effectively represent a topic (i.e., supported by a cluster of short texts), and we also call them topic representative terms.Taking advantage of topic representative terms, it is much easier to cluster short texts by grouping short texts into the most similar topic representative term groups.This paper provides a novel topic representative term discovery (TRTD) method for short text clustering.In our TRTD method, we discover groups of closely bound up topic representative terms by exploiting the closeness and significance of terms.The closeness of the topic representative terms is measured by their interdependent co-occurrence, and the significance is measured by their global term occurrences throughout the whole short text corpus.The experimental results on real-world datasets demonstrate that TRTD achieves better accuracy and efficiency in short text clustering than the state-of-the-art methods.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification

Multi-label classification (MLC) aims to predict a set of labels for a given instance.Based on a pre-defined label order, the sequence-tosequence (Seq2Seq) model trained via maximum likelihood estimation method has been successfully applied to the MLC task and shows powerful ability to capture high-order correlations between labels.However, the output labels are essentially an unordered set rather than an ordered sequence.This inconsistency tends to result in some intractable problems, e.g., sensitivity to the label order.To remedy this, we propose a simple but effective sequence-to-set model.The proposed model is trained via reinforcement learning, where reward feedback is designed to be independent of the label order.In this way, we can reduce the dependence of the model on the label order, as well as capture high-order correlations between labels.Extensive experiments show that our approach can substantially outperform competitive baselines, as well as effectively reduce the sensitivity to the label order. 1
The effect of total quality management practices on employee performance: The moderating role of knowledge sharing

This study aimed to identify the effect of Total Quality Management (TQM) practices on their dimensions on employee performance who worked for Qatar Ministry of Interior, with the moderating share of knowledge.The components of TQM include customer focus, employee participation, continuous improvement, leadership & good vision and operations management.The study uses a quantitative research, where 280 questionnaires are collected from respondents to obtain the necessary information for testing the hypotheses of the study.Multiple regression and gradient regression are used to analyze the research data.The results indicate that TQM practices with its dimensions had effects on employee performance through knowledge sharing.The results of this study contribute to develop and implement different strategic directions that enhance the performance of employees through the adoption of TQM and the dissemination of a culture of knowledge sharing.
Chat and Learn: Effectiveness of Using WhatsApp as a Pedagogical Tool to Enhance EFL Learners Reading and Writing Skills

This study has examined the pedagogical role of WhatsApp as one of mobile-assisted language learning applications in developing motivational levels of Yemeni EFL learners to develop reading and writing skills. Twenty EFL undergraduate students of Aden University joined a WhatsApp English-medium chat group where they chatted, shared and commented on news articles in English for two months. Participants also took a pre-test and a post-test and responded to a questionnaire at the end of the study. Findings revealed that WhatsApp was a very effective application in developing students’ motivation to improve their reading and writing skills. It helped them develop vocabulary, grammar, reading comprehension and writing. The study also revealed that WhatsApp was positively perceived by students as a pedagogical tool for their learning of English. The study concluded that WhatsApp English-medium groups provide students with opportunities for practising a natural language, especially in written communication, outside the classroom and motivate them to learn mutually. The study therefore recommends EFL teachers to utilize WhatsApp as a pedagogical technique and to encourage their students to create and join WhatsApp English-medium groups for natural interactions and contextualized language use that cannot be easily provided in traditional classrooms.
Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine

Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate.The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population.Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018.The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer.We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.
Neural IR Meets Graph Embedding: A Ranking Model for Product Search

Recently, neural models for information retrieval are becoming increasingly popular. They provide effective approaches for product search due to their competitive advantages in semantic matching. However, it is challenging to use graph-based features, though proved very useful in IR literature, in these neural approaches. In this paper, we leverage the recent advances in graph embedding techniques to enable neural retrieval models to exploit graph-structured data for automatic feature extraction. The proposed approach can not only help to overcome the long-tail problem of click-through data, but also incorporate external heterogeneous information to improve search results. Extensive experiments on a real-world e-commerce dataset demonstrate significant improvement achieved by our proposed approach over multiple strong baselines both as an individual retrieval model and as a feature used in learning-to-rank frameworks.
Analysis of Skin Marks Through the Use of Intelligent Things

Melanoma is one of the more frequently encountered cancers, which quite often depends on exposure to ultraviolet radiation occurring in solar radiation.Of course, an additional factor is individual immunity and genetics.It is important to discover the possible spread of the cancer, which in this case depends on the observation of the skin, and above all the marks.The quickest possible detection of neoplastic changes can result in a chance to prolong life.In the era of mobility, where everyone has a camera built into the phone and access to the Internet, it is possible to analyze signs by an application that would analyze the skin.In the case of detected exceptions, the application would exchange information with a publicly available database and consult with a dermatologist about the possible need for urgent hispatological examination.In this paper, we propose a model of such a system based on intelligent things along with a detailed description of individual components.As a classification component, a convolutional classifier was proposed, which accepts not only image data, but also numerical one.The analysis of the proposed solution has been tested and discussed due to numerous advantages and disadvantages of such a solution nowadays. INDEX TERMS Feature extraction, image
A novel image encryption technique using hybrid method of discrete dynamical chaotic maps and Brownian motion

Information security is an important and critical subject of the current digital era. Nowadays, almost all information is digital in nature and security from hackers and eavesdroppers has become vital in civil (big giant corporations) as well as in defense organizations. One type of information in bit streams is in the form of digital images. In this article, an idea to combine Brownian motion with ternary unique orientation has been implemented which is related to random motion over time and spatial coordinates. Moreover, chaotic dynamical map has been used to add one more security level to the proposed encryption scheme. The proposed scheme is evaluated on different statistical tests and these results are compared with already existing benchmarks. The results show that the proposed algorithm has better security performances as compared to existing image enciphering schemes.
VisDrone-SOT2019: The Vision Meets Drone Single Object Tracking Challenge Results

The Vision Meets Drone (VisDrone2019) Single Object Tracking challenge is the second annual research activity focusing on evaluating single-object tracking algorithms on drones, held in conjunction with the International Conference on Computer Vision (ICCV 2019). The VisDrone-SOT2019 Challenge goes beyond its VisDrone-SOT2018 predecessor by introducing 25 more challenging sequences for long-term tracking. We evaluate and discuss the results of 22 participating algorithms and 19 state-of-the-art trackers on the collected dataset. The challenge results are publicly available at the website: http://www.aiskyeye.com/. WeexpecttheVisDrone-SOTchallenge to boost the research in single object tracking field.
JEC-QA: A Legal-Domain Question Answering Dataset

We present JEC-QA, the largest question answering dataset in the legal domain, collected from the National Judicial Examination of China. The examination is a comprehensive evaluation of professional skills for legal practitioners. College students are required to pass the examination to be certified as a lawyer or a judge. The dataset is challenging for existing question answering methods, because both retrieving relevant materials and answering questions require the ability of logic reasoning. Due to the high demand of multiple reasoning abilities to answer legal questions, the state-of-the-art models can only achieve about 28% accuracy on JEC-QA, while skilled humans and unskilled humans can reach 81% and 64% accuracy respectively, which indicates a huge gap between humans and machines on this task. We will release JEC-QA and our baselines to help improve the reasoning ability of machine comprehension models. You can access the dataset from http://jecqa.thunlp.org/.
Task Allocation for Multi-Agent Systems Based on Distributed Many-Objective Evolutionary Algorithm and Greedy Algorithm

Task allocation is a key issue in multi-agent systems, and finding the optimal strategy for task allocation has been proved to be an NP-hard problem.Existing task allocation methods for multi-agent systems mainly adopt distributed full search strategies or local search strategies.The former requires a lot of computation and communication costs, while the latter cannot ensure the diversity and quality of solutions.Therefore, in this paper, we combine a distributed many-objective evolutionary algorithm called D-NSGA3 with a greedy algorithm to search the task allocation solutions, and we comprehensively consider the constraints related to space, time, energy consumption and agent function in multi-agent systems.Specifically, D-NSGA3 is used to optimize multiple objectives simultaneously so as to ensure the search capability and the diversity of solutions.Alternate search between D-NSGA3 and the greedy algorithm is conducted to enhance the local optimizing ability.Experimental results show that the proposed method can effectively solve large-scale task allocation problems (e.g., the number of agents is not less than 250, and that of tasks is not less than 1000).Compared with the existing work called MSEA, the proposed method could achieve better and more diverse solutions.
Deep Supervised Hashing With Anchor Graph

Recently, a series of deep supervised hashing methods were proposed for binary code learning. However, due to the high computation cost and the limited hardware's memory, these methods will first select a subset from the training set, and then form a mini-batch data to update the network in each iteration. Therefore, the remaining labeled data cannot be fully utilized and the model cannot directly obtain the binary codes of the entire training set for retrieval. To address these problems, this paper proposes an interesting regularized deep model to seamlessly integrate the advantages of deep hashing and efficient binary code learning by using the anchor graph. As such, the deep features and label matrix can be jointly used to optimize the binary codes, and the network can obtain more discriminative feedback from the linear combinations of the learned bits. Moreover, we also reveal the algorithm mechanism and its computation essence. Experiments on three large-scale datasets indicate that the proposed method achieves better retrieval performance with less training time compared to previous deep hashing methods.
Transfer Learning for Arabic Named Entity Recognition With Deep Neural Networks

The vast amount of unstructured data spread on a daily basis rises the need for developing effective information retrieval and extraction methods. Named Entity Recognition is a challenging classification task for structuring data into pre-defined labels, and is even more complicated when being applied on the Arabic language due to its special traits and complex nature. This article presents a novel Deep Learning approach for Standard Arabic Named Entity Recognition that proved its out-performance when being compared to previous works. The main aim of building a new model is to provide better fine-grained results for use in the Natural Language Processing fields. In our proposed methodology we utilized transfer learning with deep neural networks to build a Pooled-GRU model combined with the Multilingual Universal Sentence Encoder. Our proposed model scored about 17% enhancement when being compared to previous work.
An affinity propagation clustering based particle swarm optimizer for dynamic optimization

Multipopulation methods, which can enhance the population diversity, are well suited for dynamic optimization. However, there are still some challenges need to be tackled when multipopulation methods are employed, namely, how to avoid sensitive parameters when creating sub-populations, and how to effectively adapt to the changing optima continuously during the search process. Therefore, a novel multipopulation algorithm based on the affinity propagation clustering is proposed to address the above challenges. In the proposed method, affinity propagation clustering is applied for automatically creating sub-populations by message-passing process, which can avoid some extra parameters. Moreover, a simple but effective strategy, denoted as optimal particles relocation, is proposed for responding to environmental changes. In this strategy, the best particles in each sub-population are first stored in a memory. Then, local search is applied for helping the memory to quickly locate new peaks, if the environmental change has occurred. To validate the performance of the proposed algorithm, a variety of experiments have been conducted. The experimental results have demonstrated that the proposed algorithm performs robustly and competitively under different environments.
A Lightweight and Formally Secure Certificate Based Signcryption With Proxy Re-Encryption (CBSRE) for Internet of Things Enabled Smart Grid

A smart grid is a new ecosystem, which is made by combining a number of smart Internet of Things (IoT) devices that manage wide energy sources by increasing the efficiency and reliability of the smart energy systems.As the IoT devices in the smart grid ecosystem generate a gigantic amount of data that needs to be stored and managed in the cloud server.On the other hand, the stored data in the cloud server can be accessible to a number of data users, therefore the data need authenticity and secrecy.Here, to fulfill the security requirements of such type of communication, signcryption with proxy re-encryption technique is the most suitable option where a semi-trusted third party can alter a ciphertext that has been encrypted for one user into another ciphertext without seeing the original content of the message.However, the existing signcryption with proxy re-encryption schemes for the smart grid environment is suffering from more bandwidth space and greater computational time requirements.Thus, in this paper, we propose a lightweight certificate-based signcryption with a proxy re-encryption (CBSRE) scheme for smart grid based-IoT devices with the intention of reducing the computational and communicational costs.For the security and efficiency of the proposed CBSRE scheme, we used a hyperelliptic curve cryptosystem that uses small parameters with a key size of 80-bits.Furthermore, the proposed scheme provides the security requirements of confidentiality (IND-CBSRE-CCA2-I and IND-CBSRE-CCA2-II), unforgeability (EUF-CBSRE-CMA-I and EUF-CBSRE-CMA-II) and forward secrecy.Additionally, we compared our proposed CBSRE scheme with the existing proxy signcryption with re-encryption schemes and the final results show that the new scheme provides strong security with the expanse of minimal computational and communications resources.
Determining Physical Activity Characteristics From Wristband Data for Use in Automated Insulin Delivery Systems

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.
Generalized Zero-Shot Text Classification for ICD Coding

The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses. Automatic ICD coding is a multi-label text classification problem with noisy clinical document inputs and long-tailed label distribution, making it difficult for fine-grained classification on both frequent and zero-shot codes at the same time, i.e. generalized zero-shot ICD coding. In this paper, we propose a latent feature generation framework to improve the prediction on unseen codes without compromising the performance on seen codes. Our framework generates semantically meaningful features for zero-shot codes by exploiting ICD code hierarchical structure and reconstructing the code-relevant keywords with a novel cycle architecture. To the best of our knowledge, this is the first adversarial generative model for generalized zero-shot learning on multi-label text classification. Extensive experiments demonstrate the effectiveness of our approach. On the public MIMIC-III dataset, our methods improve the F1 score from nearly 0 to 20.91% for the zero-shot codes, and increase the AUC score by 3% (absolute improvement) from previous state of the art. Code is available at https://github.com/csong27/gzsl_text.
Explicit Optimization of min max Steganographic Game

This article proposes an algorithm which allows Alice to simulate the game played between her and Eve. Under the condition that the set of detectors that Alice assumes Eve to have is sufficiently rich (e.g. CNNs), and that she has an algorithm enabling to avoid detection by a single classifier (e.g adversarial embedding, gibbs sampler, dynamic STCs), the proposed algorithm converges to an efficient steganographic algorithm. This is possible by using a min max strategy which consists at each iteration in selecting the least detectable stego image for the best classifier among the set of Eve's learned classifiers. The algorithm is extensively evaluated and compared to prior arts and results show the potential to increase the practical security of classical steganographic methods. For example the error probability Perr of XU-Net on detecting stego images with payload of 0.4 bpnzAC embedded by J-Uniward and QF 75 starts at 7.1% and is increased by +13.6% to reach 20.7% after eight iterations. For the same embedding rate and for QF 95, undetectability by XU-Net with J-Uniward embedding is 23.4%, and it jumps by +25.8% to reach 49.2% at iteration 3.