Reviewerly/exHarmony huggingface dataset:
Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces

In extreme classification settings, embedding-based neural network models are currently not competitive with sparse linear and tree-based methods in terms of accuracy. Most prior works attribute this poor performance to the low-dimensional bottleneck in embedding-based methods. In this paper, we demonstrate that theoretically there is no limitation to using low-dimensional embedding-based methods, and provide experimental evidence that overfitting is the root cause of the poor performance of embedding-based methods. These findings motivate us to investigate novel data augmentation and regularization techniques to mitigate overfitting. To this end, we propose GLaS, a new regularizer for embedding-based neural network approaches. It is a natural generalization from the graph Laplacian and spread-out regularizers, and empirically it addresses the drawback of each regularizer alone when applied to the extreme classification setup. With the proposed techniques, we attain or improve upon the state-of-the-art on most widely tested public extreme classification datasets with hundreds of thousands of labels.
Towards Realistic Practices In Low-Resource Natural Language Processing: The Development Set

Development sets are impractical to obtain for real low-resource languages, since using all available data for training is often more effective.However, development sets are widely used in research papers that purport to deal with low-resource natural language processing (NLP).Here, we aim to answer the following questions: Does using a development set for early stopping in the low-resource setting influence results as compared to a more realistic alternative, where the number of training epochs is tuned on development languages?And does it lead to overestimation or underestimation of performance?We repeat multiple experiments from recent work on neural models for low-resource NLP and compare results for models obtained by training with and without development sets.On average over languages, absolute accuracy differs by up to 1.4%.However, for some languages and tasks, differences are as big as 18.0% accuracy.Our results highlight the importance of realistic experimental setups in the publication of lowresource NLP research results.
Searchable Public Key Encryption Supporting Semantic Multi-Keywords Search

Searchable public key encryption (SPE) that supports multi-keywords search, allows data users to retrieve encrypted files of interest efficiently, and thus it has been intensively studied during recent years.However, most existing SPE solutions focus on the exact keyword matching, which fails to capture the semantic information of documents.In this paper, we develop a novel SPE scheme supporting semantic multi-keywords search over the encrypted data.Our solution is mainly built on two techniques: one is a shallow neural network model called ''word2vec'' for capturing the semantic keywords from documents; the other is a keywords conversion method which can convert keywords into a set of vectors.We then utilize an efficient inner product encryption scheme to encrypt these converted vectors and develop the target SPE scheme, which is proven to be secure against chosen keywords attacks.Moreover, we also present both theoretical and experimental analysis to verify the efficiency and accuracy of this scheme.The experiments over a real-world dataset demonstrate that our scheme can obtain a practical performance in terms of time and space complexities.To the best of our knowledge, it is the first time to construct semantic keywords search scheme over encrypted data in the public key setting.
Towards Controllable and Personalized Review Generation

In this paper, we propose a novel model RevGAN that automatically generates controllable and personalized user reviews based on the arbitrarily given sentimental and stylistic information.RevGAN utilizes the combination of three novel components, including selfattentive recursive autoencoders, conditional discriminators, and personalized decoders.We test its performance on the several real-world datasets, where our model significantly outperforms state-of-the-art generation models in terms of sentence quality, coherence, personalization and human evaluations.We also empirically show that the generated reviews could not be easily distinguished from the organically produced reviews and that they follow the same statistical linguistics laws.
The Future of Online Learning and Teaching in Higher Education

In recent years, rapid developments in the field of science and technology have profoundly affected social lifestyles and have led to significant changes in the process of producing, sharing, and communicating information. Acceleration in the growth rate of knowledge as a result of developments in science and technology has made it necessary to constantly update existing information. Parallel to this, the necessity of the people to follow the information constantly and to continuously update it to keep up with the speed of daily changes tops the agenda. This situation increased the use of information and communication technologies and the internet in the field of education, resulting in the spread of online environments.
Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems

The gravitational search algorithm (GSA) is a population-based meta-heuristic optimization algorithm which finds the optimal solution by the law of gravity and attraction between objects. However, as the number of iterations increases, the increase of the quality of the agents makes GSA fall into the local optimal solution more easily, which greatly reduces the exploration capability of the algorithm. Although the chaotic gravitational search algorithm (CGSA) uses chaotic maps for improving diversity to solve this problem, it still has problems with the balance of exploration and exploitation. This paper proposes the balance adjustment based chaotic gravitational search algorithm (BA-CGSA), which introduces the sine randomness function and the balance mechanism to solve the above problem. 30 benchmark functions of IEEE CEC 2014 are adopted to evaluate the performance of the proposed algorithm in terms of exploration and exploitation. Meanwhile, a real engineering design problem is used to illustrate the ability of the algorithm to solve practical application problems. The experimental results demonstrate its good performance in continuous optimization problems.
A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers

Accuracy and diversity are considered to be the two deriving factors when it comes to generating an ensemble classifier. Focusing only on accuracy causes the ensemble classifier to suffer from “diminishing returns” and the ensemble accuracy tends to plateau; whereas focusing only on diversity causes the ensemble classifier to suffer in accuracy. Therefore, a balance must be maintained between the two for the ensemble classifier to achieve high classification accuracy. In this paper, we propose a novel diversity measure known as Misclassification Diversity (MD) and an Incremental Layered Classifier Selection (ILCS) approach to generate an ensemble classifier. The proposed approach ILCS-MD generates an ensemble classifier by incrementally selecting classifiers from the base classifier pool based on increasing accuracy and diversity. The benefits are in two folds 1) the generated ensemble classifier contains only those classifiers from the pool which can either maximize accuracy whilst maintaining or increasing the diversity, and 2) the generated ensemble classifier selects only a few classifiers from the base classifier pool thus reducing ensemble component size as well. The proposed approach is evaluated on 55 benchmark datasets taken from UCI and KEEL dataset repositories. The results are compared with five existing pairwise diversity measures, and existing state of the art ensemble classifier approaches. A significance test is also conducted to verify the significance of the results.
GraphM

With the rapidly growing demand of graph processing in the real world, a large number of iterative graph processing jobs run concurrently on the same underlying graph. However, the storage engines of existing graph processing frameworks are mainly designed for running an individual job. Our studies show that they are inefficient when running concurrent jobs due to the redundant data storage and access overhead. To cope with this issue, we develop an efficient storage system, called GraphM. It can be integrated into the existing graph processing systems to efficiently support concurrent iterative graph processing jobs for higher throughput by fully exploiting the similarities of the data accesses between these concurrent jobs. GraphM regularizes the traversing order of the graph partitions for concurrent graph processing jobs by streaming the partitions into the main memory and the Last-Level Cache (LLC) in a common order, and then processes the related jobs concurrently in a novel fine-grained synchronization. In this way, the concurrent jobs share the same graph structure data in the LLC/memory and also the data accesses to the graph, so as to amortize the storage consumption and the data access overhead. To demonstrate the efficiency of GraphM, we plug it into state-of-the-art graph processing systems, including GridGraph, GraphChi, PowerGraph, and Chaos. Experiments results show that GraphM improves the throughput by 1.73~13 times.
Global Voices: Crossing Borders in Automatic News Summarization

We construct Global Voices, a multilingual dataset for evaluating cross-lingual summarization methods.We extract social-network descriptions of Global Voices news articles to cheaply collect evaluation data for into-English and from-English summarization in 15 languages.Especially, for the into-English summarization task, we crowd-source a highquality evaluation dataset based on guidelines that emphasize accuracy, coverage, and understandability.To ensure the quality of this dataset, we collect human ratings to filter out bad summaries, and conduct a survey on humans, which shows that the remaining summaries are preferred over the social-network summaries.We study the effect of translation quality in cross-lingual summarization, comparing a translate-then-summarize approach with several baselines.Our results highlight the limitations of the ROUGE metric that are overlooked in monolingual summarization.
High-Resolution Remote Sensing Imagery Classification of Imbalanced Data Using Multistage Sampling Method and Deep Neural Networks

Class imbalance is a key issue for the application of deep learning for remote sensing image classification because a model generated by imbalanced samples training has low classification accuracy for minority classes. In this study, an accurate classification approach using the multistage sampling method and deep neural networks was proposed to classify imbalanced data. We first balance samples by multistage sampling to obtain the training sets. Then, a state-of-the-art model is adopted by combining the advantages of atrous spatial pyramid pooling (ASPP) and Encoder-Decoder for pixel-wise classification, which are two different types of fully convolutional networks (FCNs) that can obtain contextual information of multiple levels in the Encoder stage. The details and spatial dimensions of targets are restored using such information during the Decoder stage. We employ four deep learning-based classification algorithms (basic FCN, FCN-8S, ASPP, and Encoder-Decoder with ASPP of our approach) on multistage training sets (original, MUS1, and MUS2) of WorldView-3 images in southeastern Qinghai-Tibet Plateau and GF-2 images in northeastern Beijing for comparison. The experiments show that, compared with existing sets (original, MUS1, and identical) and existing method (cost weighting), the MUS2 training set of multistage sampling significantly enhance the classification performance for minority classes. Our approach shows distinct advantages for imbalanced data.
Decentralised Applications Using Ethereum Blockchain

The concept of blockchain without a doubt is a revolutionary concept. It is the underlying Technology behind bitcoin and many more cryptocurrencies. Although the people's focus being only at blockchain as cryptocurrencies in everyday services to do payments online without the interference of a third party will try to replace the current method of cash which is a really slow and ancient method. Blockchain is a zero trust network and this makes it a very powerful tool for various services provided that people are ready to believe and invest in it. In the Ethereum world, the blockchain runs on smart contracts which are self-executing applications that come at a cost of security. This zero-trust network is capable of replacing many of the debated process or activities in our day to day life. One of our biggest concerns is an E-voting system which must be secure. Blockchain being an immutable and append only ledger will not allow for any tampering while also being fully transparent. In this paper, we have implemented and tested a sample e-voting app running as a smart contract for ethereum network using E-Wallets. After an election is held, eventually, the ethereum blockchain will hold the records of ballots and voters thus giving us a clear and trusty network where mishandling is to a minimum.
Artificial Intelligence-Based Security Authentication: Applications in Wireless Multimedia Networks

Security provisioning has become a key issue in wireless multimedia networks due to their vital roles in supporting various services.Conventional security solutions have deficiencies in computing efficiency and addressing emerging security challenges.In addition, traditional upper-layer authentication ignores the protection of the physical layer, leading to severe privacy data leakage.In this paper, we envision a new deep learning (DL)-enabled security authentication scheme to overcome these issues, while implementing Blind Feature Learning (BFL) and Lightweight Physical Layer Authentication (LPLA).Specifically, an intelligent authentication method is developed by exploring neural networks at the data collection unit to learn the characteristics of data.Then we propose a holistic authentication scheme based on machine learning to identify malicious multimedia devices.Experimental analysis verifies that the proposed scheme can guarantee the privacy of wireless multimedia sensors and achieve lightweight authentication.Performance results indicate that artificial intelligence-enabled authentication scheme improves the overall security of multimedia networks.
Maximizing Stylistic Control and Semantic Accuracy in NLG: Personality Variation and Discourse Contrast

Neural generation methods for task-oriented dialogue typically generate from a meaning representation that is populated using a database of domain information, such as a table of data describing a restaurant.While earlier work focused solely on the semantic fidelity of outputs, recent work has started to explore methods for controlling the style of the generated text while simultaneously achieving semantic accuracy.Here we experiment with two stylistic benchmark tasks, generating language that exhibits variation in personality, and generating discourse contrast.We report a huge performance improvement in both stylistic control and semantic accuracy over the state of the art on both of these benchmarks.We test several different models and show that putting stylistic conditioning in the decoder and eliminating the semantic re-ranker used in earlier models results in more than 15 points higher BLEU for Personality, with a reduction of semantic error to near zero.We also report an improvement from .75 to .81 in controlling contrast and a reduction in semantic error from 16% to 2%.
Gaze-Driven Adaptive Interventions for Magazine-Style Narrative Visualizations

In this paper we investigate the value of gaze-driven adaptive interventions to support processing of textual documents with embedded visualizations, i.e., Magazine Style Narrative Visualizations (MSNVs). These interventions are provided dynamically by highlighting relevant data points in the visualization when the user reads related sentences in the MNSV text, as detected by an eye-tracker. We conducted a user study during which participants read a set of MSNVs with our interventions, and compared their performance and experience with participants who received no interventions. Our work extends previous findings by showing that dynamic, gaze-driven interventions can be delivered based on reading behaviors in MSNVs, a widespread form of documents that have never been considered for gaze-driven adaptation so far. Next, we found that the interventions significantly improved the performance of users with low levels of visualization literacy, i.e., those users who need help the most due to their lower ability to process and understand data visualizations. However, high literacy users were not impacted by the interventions, providing initial evidence that gaze-driven interventions can be further improved by personalizing them to the levels of visualization literacy of their users.
Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event

Abstract Background In the hospital setting, it is crucial to identify patients at risk for deterioration before it fully develops, so providers can respond rapidly to reverse the deterioration. Rapid response (RR) activation criteria include a subjective component (“worried about the patient”) that is often documented in nurses' notes and is hard to capture and quantify, hindering active screening for deteriorating patients. Objectives We used unsupervised machine learning to automatically discover RR event risk/protective factors from unstructured nursing notes. Methods In this retrospective cohort study, we obtained nursing notes of hospitalized, nonintensive care unit patients, documented from 2015 through 2018 from Partners HealthCare databases. We applied topic modeling to those notes to reveal topics (clusters of associated words) documented by nurses. Two nursing experts named each topic with a representative Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) concept. We used the concepts along with vital signs and demographics in a time-dependent covariates extended Cox model to identify risk/protective factors for RR event risk. Results From a total of 776,849 notes of 45,299 patients, we generated 95 stable topics, of which 80 were mapped to 72 distinct SNOMED CT concepts. Compared with a model containing only demographics and vital signs, the latent topics improved the model's predictive ability from a concordance index of 0.657 to 0.720. Thirty topics were found significantly associated with RR event risk at a 0.05 level, and 11 remained significant after Bonferroni correction of the significance level to 6.94E-04, including physical examination (hazard ratio [HR] = 1.07, 95% confidence interval [CI], 1.03–1.12), informing doctor (HR = 1.05, 95% CI, 1.03–1.08), and seizure precautions (HR = 1.08, 95% CI, 1.04–1.12). Conclusion Unsupervised machine learning methods can automatically reveal interpretable and informative signals from free-text and may support early identification of patients at risk for RR events.
Importance of Technology Leadership for Technology Integration: Gender and Professional Development Perspective

Technology leadership is being redesigned to accelerate technology integration in schools to develop and sustain the skills needed to produce skilled manpower. Previous studies showed that the factors that prevented leaders and teachers from integrating information and communication technologies (ICT) in schools are lack of ICT training, teacher ICT competency, and access to ICT resources. This shows that there is a gap in technology integration in schools. This study aims to identify the importance of technology leadership for technology integration based on gender and professional development. This is a quantitative study using cross-sectional design. A total of 90 respondents were selected by systematic random sampling from 190 national secondary schools. The importance and performance matrix analysis revealed that professional development had high performance and was an important variable in the prediction of teachers’ technology integration, but gender was not. Thus, school management should give priority to digital age learning culture and digital citizenship constructs to accelerate teachers’ technology integration in schools.
A Stable Variational Autoencoder for Text Modelling

Variational Autoencoder (VAE) is a powerful method for learning representations of highdimensional data.However, VAEs can suffer from an issue known as latent variable collapse (or KL loss vanishing), where the posterior collapses to the prior and the model will ignore the latent codes in generative tasks.Such an issue is particularly prevalent when employing VAE-RNN architectures for text modelling (Bowman et al., 2016).In this paper, we present a simple architecture called holistic regularisation VAE (HR-VAE), which can effectively avoid latent variable collapse.Compared to existing VAE-RNN architectures, we show that our model can achieve much more stable training process and can generate text with significantly better quality.
DES encryption and decryption algorithm implementation based on FPGA

Nowadays there is a lot of importance given to data security on the internet. The DES is one of the most preferred block cipher encryption/decryption procedures used at present. This paper presents a high throughput reconfigurable hardware implementation of DES Encryption algorithm. This achieved by using a new proposed implementation of the DES algorithm using pipelined concept. The implementation of the proposed design is presented by using Spartan-3E (XC3S500E) family FPGAs and is one of the fastest hardware implementations with much greater security. At a clock frequency of 167.448MHz for encryption and 167.870MHz for decryption, it can encrypt or decrypt data blocks at a rate of 10688Mbps.
Less Is Better: Unweighted Data Subsampling via Influence Function

In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling. Most previous works in subsampling are weighted methods designed to help the performance of subset-model approach the full-set-model, hence the weighted methods have no chance to acquire a subset-model that is better than the full-set-model. However, we question that how can we achieve better model with less data? In this work, we propose a novel Unweighted Influence Data Subsampling (UIDS) method, and prove that the subset-model acquired through our method can outperform the full-set-model. Besides, we show that overly confident on a given test set for sampling is common in Influence-based subsampling methods, which can eventually cause our subset-model's failure in out-of-sample test. To mitigate it, we develop a probabilistic sampling scheme to control the worst-case risk over all distributions close to the empirical distribution. The experiment results demonstrate our methods superiority over existed subsampling methods in diverse tasks, such as text classification, image classification, click-through prediction, etc.