Reviewerly/exHarmony huggingface dataset:
Discriminative Sentence Modeling for Story Ending Prediction

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge. To tackle this task, we propose a new neural network called Diff-Net for better modeling the differences of each ending in this task. The proposed model could discriminate two endings in three semantic levels: contextual representation, story-aware representation, and discriminative representation. Experimental results on the Story Cloze Test dataset show that the proposed model siginificantly outperforms various systems by a large margin, and detailed ablation studies are given for better understanding our model. We also carefully examine the traditional and BERT-based models on both SCT v1.0 and v1.5 with interesting findings that may potentially help future studies.
Click-through rate prediction with the user memory network

Click-through rate (CTR) prediction is a critical task in online advertising systems. Models like Deep Neural Networks (DNNs) are simple but stateless. They consider each target ad independently and cannot directly extract useful information contained in users' historical ad impressions and clicks. In contrast, models like Recurrent Neural Networks (RNNs) are stateful but complex. They model temporal dependency between users' sequential behaviors and can achieve improved prediction performance than DNNs. However, both the offline training and online prediction process of RNNs are much more complex and time-consuming. In this paper, we propose Memory Augmented DNN (MA-DNN) for practical CTR prediction services. In particular, we create two external memory vectors for each user, memorizing high-level abstractions of what a user possibly likes and dislikes. The proposed MA-DNN achieves a good compromise between DNN and RNN. It is as simple as DNN, but has certain ability to exploit useful information contained in users' historical behaviors as RNN. Both offline and online experiments demonstrate the effectiveness of MA-DNN for practical CTR prediction services. Actually, the memory component can be augmented to other models as well (e.g., the Wide&Deep model).
Reading Habits and Their Influence on Academic Achievement Among Students at Asia Pacific International University

In this study reading habits have been correlated with academic achievement. Students whohave developed the habits of reading academic and non-academic materials develop theircomprehension of concepts, critical thinking skills, and verbal fluency and ultimately havebetter academic outcomes. This quantitative study examined reading habits and theirinfluence on the academic achievements of students at Asia-Pacific International University,Mauk Lek, Thailand. A convenience sampling method was utilized to select 250 individualswho responded to a survey questionnaire, A Statistical Package for Social Science (SPSS)was used to analyze the data. A multiple regression and Correlation Matrix analysis checkedthe influence of reading habits on academic achievement. Among the five variables, the studyconfirmed that the purpose of reading has a significant relationship with academicperformance. Furthermore, the findings indicated that the majority of the respondents valuethe importance of reading. However, the respondents had low reading habits. The studyrecommends that policymakers in universities should develop plans and for increasingstudents‘ reading habits. This includes providing resources to encourage students to take upreading. In addition, administrators should develop strategies for teachers to create activitiesthat can promote reading habits, which would, in turn, improve students‘ academicperformance.
An evidence synthesis of strategies, enablers and barriers for keeping secrets online regarding the procurement and supply of illicit drugs

This systematic review attempts to understand how people keep secrets online, and in particular how people use the internet when engaging in covert behaviours and activities regarding the procurement and supply of illicit drugs. With the Internet and social media being part of everyday life for most people in western and non-western countries, there are ever-growing opportunities for individuals to engage in covert behaviours and activities online that may be considered illegal or unethical. A search strategy using Medical Subject Headings terms and relevant key words was developed. A comprehensive literature search of published and unpublished studies in electronic databases was conducted. Additional studies were identified from reference lists of previous studies and (systematic) reviews that had similar objectives as this search, and were included if they fulfilled our inclusion criteria. Two researchers independently screened abstracts and full-texts for study eligibility and evaluated the quality of included studies. Disagreements were resolved by a consensus procedure. The systematic review includes 33 qualitative studies and one cross-sectional study, published between 2006 and 2018. Five covert behaviours were identified: the use of communication channels; anonymity; visibility reduction; limited posts in public; following forum rules and recommendations. The same technologies that provide individuals with easy access to information, such as social networking sites and forums, digital devices, digital tools and services, also increase the prevalence of inaccurate information, loss of privacy, identity theft and disinhibited communication. This review takes a rigorous interdisciplinary approach to synthesising knowledge on the strategies adopted by people in keeping secrets online. Whilst the focus is on the procurement and supply of illicit drugs, this knowledge is transferrable to a range of contexts where people keep secrets online. It has particular significance for those who design online/social media applications, and for law enforcement and security agencies.
The power of modal separation logics

Abstract We introduce a modal separation logic MSL whose models are memory states from separation logic and the logical connectives include modal operators as well as separating conjunction and implication from separation logic. With such a combination of operators, some fragments of MSL can be seen as genuine modal logics whereas some others capture standard separation logics, leading to an original language to speak about memory states. We analyse the decidability status and the computational complexity of several fragments of MSL, obtaining surprising results by design of proof methods that take into account the modal and separation features of MSL. For example, the satisfiability problem for the fragment of MSL with $\Diamond $, the difference modality $\langle \neq \rangle $ and separating conjunction $\ast $ is shown Tower-complete whereas the restriction either to $\Diamond $ and $\ast $ or to $\langle \neq \rangle $ and $\ast $ is only NP-complete. We establish that the full logic MSL admits an undecidable satisfiability problem. Furthermore, we investigate variants of MSL with alternative semantics and we build bridges with interval temporal logics and with logics equipped with sabotage operators.
Blockchain technology applications: a literature review

Goal: The objective of this article is to raise the state of the art of the applications of blockchain described in the scientific literature.&#x0D; Design/methodology/approach: A bibliographic research on the Scopus database was carried out from November, 2017 to May, 2018, supported by the scientific research technique called Bibliometry. The compilation of 102 articles found in the scientific literature were presented in the Blockchain context, providing a reference for the fields of blockchain applications.&#x0D; Results: The results indicate a large increase in the number of articles published since 2017, the largest number of publications were written by authors affiliated with institutions located in China, as well as the most referenced applications are related to cryptocurrencies, smart contracts and IoT.&#x0D; Limitations of the investigation: The limitation lies in using only the Scopus database for bibliometry, despite being a renowned scientific base.&#x0D; Practical implications: This study can serve as a bibliographic reference with a global view of the main Blockchain applications that can be applied by different types of organizations&#x0D; Originality/value: Blockchain is a technology that aims to securely store data, such as financial data, contracts, proof of identity, documents of possession, medical information, etc. To do so, a framework based on laws of mathematics and cryptography is used to ensure the security of transactions of different applications, such as Bitcoin, which is a digital currency that guarantees the safe transfer of financial data. However, being a new technology, there is still little research on the subject. On the other hand, there are different types of applications that are already benefiting from the blockchain structure.
Back to the Future – Temporal Adaptation of Text Representations

Language evolves over time in many ways relevant to natural language processing tasks. For example, recent occurrences of tokens 'BERT' and 'ELMO' in publications refer to neural network architectures rather than persons. This type of temporal signal is typically overlooked, but is important if one aims to deploy a machine learning model over an extended period of time. In particular, language evolution causes data drift between time-steps in sequential decision-making tasks. Examples of such tasks include prediction of paper acceptance for yearly conferences (regular intervals) or author stance prediction for rumours on Twitter (irregular intervals). Inspired by successes in computer vision, we tackle data drift by sequentially aligning learned representations. We evaluate on three challenging tasks varying in terms of time-scales, linguistic units, and domains. These tasks show our method outperforming several strong baselines, including using all available data. We argue that, due to its low computational expense, sequential alignment is a practical solution to dealing with language evolution.
DEVELOPING A FASTER PATTERN MATCHING ALGORITHMS FOR INTRUSION DETECTION SYSTEM

Fast pattern matching algorithms mostly used by IDS, which are considered one of the important systems used to monitor and analyze host and network traffic. Their main function is to detect various types of malicious and malware files by examining incoming and outgoing data through the network. As the network speed growing, the malicious behavior and malware files are increasing; the pattern matching algorithms must be faster. In this research paper we are presenting a new method of pattern matching, which could be a platform for enhancement in the future. In this field, researchers spared no efforts to introduce fast algorithms for pattern matching. The Most popular algorithms are Boyer-Moore, Aho–Corasick, Naïve String search, Rabin Karp String Search and Knuth–Morris–Pratt. Based on studying these techniques we are developing algorithms that process the text data, using different algorithm technique and then we’ll test the performance and compare the processing time with the fastest proven pattern matching algorithms available. Document the result and draw the overall conclusion.
Region Based Adversarial Synthesis of Facial Action Units

Facial expression synthesis or editing has recently received increasing attention in the field of affective computing and facial expression modeling. However, most existing facial expression synthesis works are limited in paired training data, low resolution, identity information damaging, and so on. To address those limitations, this paper introduces a novel Action Unit (AU) level facial expression synthesis method called Local Attentive Conditional Generative Adversarial Network (LAC-GAN) based on face action units annotations. Given desired AU labels, LAC-GAN utilizes local AU regional rules to control the status of each AU and attentive mechanism to combine several of them into the whole photo-realistic facial expressions or arbitrary facial expressions. In addition, unpaired training data is utilized in our proposed method to train the manipulation module with the corresponding AU labels, which learns a mapping between a facial expression manifold. Extensive qualitative and quantitative evaluations are conducted on commonly used BP4D dataset to verify the effectiveness of our proposed AU synthesis method.
Discontinuous Constituent Parsing with Pointer Networks

One of the most complex syntactic representations used in computational linguistics and NLP are discontinuous constituent trees, crucial for representing all grammatical phenomena of languages such as German. Recent advances in dependency parsing have shown that Pointer Networks excel in efficiently parsing syntactic relations between words in a sentence. This kind of sequence-to-sequence models achieve outstanding accuracies in building non-projective dependency trees, but its potential has not been proved yet on a more difficult task. We propose a novel neural network architecture that, by means of Pointer Networks, is able to generate the most accurate discontinuous constituent representations to date, even without the need of Part-of-Speech tagging information. To do so, we internally model discontinuous constituent structures as augmented non-projective dependency structures. The proposed approach achieves state-of-the-art results on the two widely-used NEGRA and TIGER benchmarks, outperforming previous work by a wide margin.
A study of code change patterns for adaptive maintenance with AST analysis

Example-based transformational approaches to automate adaptive maintenance changes plays an important role in software research. One primary concern of those approaches is that a set of good qualified real examples of adaptive changes previously made in the history must be identified, or otherwise the adoption of such approaches will be put in question. Unfortunately, there is rarely enough detail to clearly direct transformation rule developers to overcome the barrier of finding qualified examples for adaptive changes. This work explores the histories of several open source systems to study the repetitiveness of adaptive changes in software evolution, and hence recognizing the source code change patterns that are strongly related with the adaptive maintenance. We collected the adaptive commits from the history of numerous open source systems, then we obtained the repetitiveness frequencies of source code changes based on the analysis of Abstract Syntax Tree (AST) edit actions within an adaptive commit. Using the prevalence of the most common adaptive changes, we suggested a set of change patterns that seem correlated with adaptive maintenance. It is observed that 76.93% of the undertaken adaptive changes were represented by 12 AST code differences. Moreover, only 9 change patterns covered 64.69% to 76.58% of the total adaptive change hunks in the examined projects. The most common individual patterns are related to initializing objects and method calls changes. A correlation analysis on examined projects shows that they have very similar frequencies of the patterns correlated with adaptive changes. The observed repeated adaptive changes could be useful examples for the construction of transformation approaches
Collaborative Sampling in Generative Adversarial Networks

The standard practice in Generative Adversarial Networks (GANs) discards the discriminator during sampling. However, this sampling method loses valuable information learned by the discriminator regarding the data distribution. In this work, we propose a collaborative sampling scheme between the generator and the discriminator for improved data generation. Guided by the discriminator, our approach refines the generated samples through gradient-based updates at a particular layer of the generator, shifting the generator distribution closer to the real data distribution. Additionally, we present a practical discriminator shaping method that can smoothen the loss landscape provided by the discriminator for effective sample refinement. Through extensive experiments on synthetic and image datasets, we demonstrate that our proposed method can improve generated samples both quantitatively and qualitatively, offering a new degree of freedom in GAN sampling.
A Skip-Connected Evolving Recurrent Neural Network for Data Stream Classification under Label Latency Scenario

Stream classification models for non-stationary environments often assume the immediate availability of data labels. However, in a practical scenario, it is quite natural that the data labels are available only after some temporal lag. This paper explores how a stream classifier model can be made adaptive to such label latency scenario. We propose SkipE-RNN, a self-evolutionary recurrent neural network with dynamically evolving skipped-recurrent-connection for the best utilization of previously observed label information while classifying the current data. When the data label is unavailable, SkipE-RNN uses an auto-learned mapping function to find the best match from the already known data labels and updates the network parameter accordingly. Later, upon availability of true data label, if the previously mapped label is found to be incorrect, SkipE-RNN employs a regularization technique along with the parameter updating process, so as to penalize the model. In addition, SkipE-RNN has inborn power of self-adjusting the network capacity by growing/pruning hidden nodes to cope with the evolving nature of data stream. Rigorous empirical evaluations using synthetic as well as real-world datasets reveal effectiveness of SkipE-RNN in both finitely delayed and infinitely delayed data label scenarios.
Balancing Quality and Human Involvement: An Effective Approach to Interactive Neural Machine Translation

Conventional interactive machine translation typically requires a human translator to validate every generated target word, even though most of them are correct in the advanced neural machine translation (NMT) scenario. Previous studies have exploited confidence approaches to address the intensive human involvement issue, which request human guidance only for a few number of words with low confidences. However, such approaches do not take the history of human involvement into account, and optimize the models only for the translation quality while ignoring the cost of human involvement. In response to these pitfalls, we propose a novel interactive NMT model, which explicitly accounts the history of human involvements and particularly is optimized towards two objectives corresponding to the translation quality and the cost of human involvement, respectively. Specifically, the model jointly predicts a target word and a decision on whether to request human guidance, which is based on both the partial translation and the history of human involvements. Since there is no explicit signals on the decisions of requesting human guidance in the bilingual corpus, we optimize the model with the reinforcement learning technique which enables our model to accurately predict when to request human guidance. Simulated and real experiments show that the proposed model can achieve higher translation quality with similar or less human involvement over the confidence-based baseline.
Rethinking Temporal Fusion for Video-Based Person Re-Identification on Semantic and Time Aspect

Recently, the research interest of person re-identification (ReID) has gradually turned to video-based methods, which acquire a person representation by aggregating frame features of an entire video. However, existing video-based ReID methods do not consider the semantic difference brought by the outputs of different network stages, which potentially compromises the information richness of the person features. Furthermore, traditional methods ignore important relationship among frames, which causes information redundancy in fusion along the time axis. To address these issues, we propose a novel general temporal fusion framework to aggregate frame features on both semantic aspect and time aspect. As for the semantic aspect, a multi-stage fusion network is explored to fuse richer frame features at multiple semantic levels, which can effectively reduce the information loss caused by the traditional single-stage fusion. While, for the time axis, the existing intra-frame attention method is improved by adding a novel inter-frame attention module, which effectively reduces the information redundancy in temporal fusion by taking the relationship among frames into consideration. The experimental results show that our approach can effectively improve the video-based re-identification accuracy, achieving the state-of-the-art performance.
Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation

Training generative models that can generate high-quality text with sufficient diversity is an important open problem for Natural Language Generation (NLG) community. Recently, generative adversarial models have been applied extensively on text generation tasks, where the adversarially trained generators alleviate the exposure bias experienced by conventional maximum likelihood approaches and result in promising generation quality. However, due to the notorious defect of mode collapse for adversarial training, the adversarially trained generators face a quality-diversity trade-off, i.e., the generator models tend to sacrifice generation diversity severely for increasing generation quality. In this paper, we propose a novel approach which aims to improve the performance of adversarial text generation via efficiently decelerating mode collapse of the adversarial training. To this end, we introduce a cooperative training paradigm, where a language model is cooperatively trained with the generator and we utilize the language model to efficiently shape the data distribution of the generator against mode collapse. Moreover, instead of engaging the cooperative update for the generator in a principled way, we formulate a meta learning mechanism, where the cooperative update to the generator serves as a high level meta task, with an intuition of ensuring the parameters of the generator after the adversarial update would stay resistant against mode collapse. In the experiment, we demonstrate our proposed approach can efficiently slow down the pace of mode collapse for the adversarial text generators. Overall, our proposed method is able to outperform the baseline approaches with significant margins in terms of both generation quality and diversity in the testified domains.