Reviewerly/exHarmony huggingface dataset:
Abstractive Text Summarization by Incorporating Reader Comments

In neural abstractive summarization field, conventional sequence-to-sequence based models often suffer from summarizing the wrong aspect of the document with respect to the main aspect. To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect. Unlike traditional abstractive summarization task, reader-aware summarization confronts two main challenges: (1) Comments are informal and noisy; (2) jointly modeling the news document and the reader comments is challenging. To tackle the above challenges, we design an adversarial learning model named reader-aware summary generator (RASG), which consists of four components: (1) a sequence-to-sequence based summary generator; (2) a reader attention module capturing the reader focused aspects; (3) a supervisor modeling the semantic gap between the generated summary and reader focused aspects; (4) a goal tracker producing the goal for each generation step. The supervisor and the goal tacker are used to guide the training of our framework in an adversarial manner. Extensive experiments are conducted on our large-scale real-world text summarization dataset, and the results show that RASG achieves the stateof-the-art performance in terms of both automatic metrics and human evaluations. The experimental results also demonstrate the effectiveness of each module in our framework. We release our large-scale dataset for further research1.
Using a Tablet Computer Application to Advance High School Students’ Laboratory Learning Experiences: A Focus on Electrical Engineering Education

This study proposed an innovative instructional strategy for pre-college engineering education. Learning topics on electrical engineering education in a laboratory setting were selected for further exploration. Specifically, the study investigated how tablet computers with one adopted application influenced student engineering learning. The study adopted a quasi-experimental pretest and posttest research design. The educational experiment lasted for 6 weeks. The research participants comprised 57 ninth-grade students from a public high school in Taiwan. The students employed either a tablet computer or laptop computer to engage in weekly 3-h laboratory studies. A criterion test was developed to measure student knowledge of electrical engineering. The results revealed that the instructional effectiveness was identical for all the students, regardless of whether they used a tablet or laptop computer. However, those using the tablet computers achieved greater learning improvement. In addition, the qualitative data indicated that the tablet computers facilitated student learning in various engineering learning activities. The results of the study suggested that engineering educators in different educational levels might consider similar instructional methods to inspire students’ engineering learning.
A Queuing Delay Utilization Scheme for On-Path Service Aggregation in Services-Oriented Computing Networks

In services-oriented computing networks, packets in the process of routing to a data center must wait for a sufficient amount of data before service aggregation to reduce the network transmission load. However, packets must be uploaded to the data center as soon as possible to reduce delay. With the exponential growth in the number of IoT connected devices, the wait time for packets is longer at routers due to massive amounts of data, which causes a large queuing delay. If this queuing time can be utilized for service aggregation in a service-oriented computing network, the network performance will be substantially improved. Therefore, a queuing delay utilization scheme for on-path service aggregation (SAQD) is proposed in this paper. This scheme has the following innovations: 1) SAQD fully utilizes the queuing delay of packets for service aggregation, which can effectively reduce the transmission volume and communication overhead. Based on the proposed service aggregation algorithm, packets are divided into forwarding packets and aggregating packets, and the service aggregation of aggregating packets is completed by utilizing the transmission time of forwarding packets to ensure that the transmission volume and communication overhead are effectively reduced without additional latency. 2) SAQD can effectively alleviate the traffic pressure of the data center and balance the workload of routers. By the service aggregation and intranet cache of routers, some requests for the data center can be handled by routers, which reduces the traffic pressure of the data center, especially in the peak period. Compared with conventional schemes, the experimental results demonstrate that SAQD reduces the workload of the data center by 55.8%-66.26% and provides users with a better quality of experience by reducing the request response delay by 31.33%~51.41%.
Absent Multiple Kernel Learning Algorithms

Multiple kernel learning (MKL) has been intensively studied during the past decade. It optimally combines the multiple channels of each sample to improve classification performance. However, existing MKL algorithms cannot effectively handle the situation where some channels of the samples are missing, which is not uncommon in practical applications. This paper proposes three absent MKL (AMKL) algorithms to address this issue. Different from existing approaches where missing channels are first imputed and then a standard MKL algorithm is deployed on the imputed data, our algorithms directly classify each sample based on its observed channels, without performing imputation. Specifically, we define a margin for each sample in its own relevant space, a space corresponding to the observed channels of that sample. The proposed AMKL algorithms then maximize the minimum of all sample-based margins, and this leads to a difficult optimization problem. We first provide two two-step iterative algorithms to approximately solve this problem. After that, we show that this problem can be reformulated as a convex one by applying the representer theorem. This makes it readily be solved via existing convex optimization packages. In addition, we provide a generalization error bound to justify the proposed AMKL algorithms from a theoretical perspective. Extensive experiments are conducted on nine UCI and six MKL benchmark datasets to compare the proposed algorithms with existing imputation-based methods. As demonstrated, our algorithms achieve superior performance and the improvement is more significant with the increase of missing ratio.
Detecting Malicious Social Bots Based on Clickstream Sequences

With the significant increase in the volume, velocity, and variety of user data (e.g., user-generated data) in online social networks, there have been attempted to design new ways of collecting and analyzing such big data. For example, social bots have been used to perform automated analytical services and provide users with improved quality of service. However, malicious social bots have also been used to disseminate false information (e.g., fake news), and this can result in real-world consequences. Therefore, detecting and removing malicious social bots in online social networks is crucial. The most existing detection methods of malicious social bots analyze the quantitative features of their behavior. These features are easily imitated by social bots; thereby resulting in low accuracy of the analysis. A novel method of detecting malicious social bots, including both features selection based on the transition probability of clickstream sequences and semi-supervised clustering, is presented in this paper. This method not only analyzes transition probability of user behavior clickstreams but also considers the time feature of behavior. Findings from our experiments on real online social network platforms demonstrate that the detection accuracy for different types of malicious social bots by the detection method of malicious social bots based on transition probability of user behavior clickstreams increases by an average of 12.8%, in comparison to the detection method based on quantitative analysis of user behavior.
An Empirical Study of Obsolete Answers on Stack Overflow

Stack Overflow accumulates an enormous amount of software engineering knowledge.However, as time passes, certain knowledge in answers may become obsolete.Such obsolete answers, if not identified or documented clearly, may mislead answer seekers and cause unexpected problems (e.g., using an out-dated security protocol).In this paper, we investigate how the knowledge in answers becomes obsolete and identify the characteristics of such obsolete answers.We find that: 1) More than half of the obsolete answers (58.4%) were probably already obsolete when they were first posted.2) When an obsolete answer is observed, only a small proportion (20.5%) of such answers are ever updated.3) Answers to questions in certain tags (e.g., node.js,ajax, android, and objective-c) are more likely to become obsolete.Our findings suggest that Stack Overflow should develop mechanisms to encourage the whole community to maintain answers (to avoid obsolete answers) and answer seekers are encouraged to carefully go through all information (e.g., comments) in answer threads.
Survey on Human Activity Recognition based on Acceleration Data

Human activity recognition is an important area of machine learning research as it has many utilization in different areas such as sports training, security, entertainment, ambientassisted living, and health monitoring and management.Studying human activity recognition shows that researchers are interested mostly in the daily activities of the human.Therefore, the general architecture of HAR system is presented in this paper, along with the description of its main components.The state of the art in human activity recognition based on accelerometer is surveyed.According to this survey, Most of the researches recently used deep learning for recognizing HAR, but they focused on CNN even though there are other deep learning types achieved a satisfied accuracy.The paper displays a two-level taxonomy in accordance with machine learning approach (either traditional or deep learning) and the processing mode (either online or offline).Forty eight studies are compared in terms of recognition accuracy, classifier, activities types, and used devices.Finally, the paper concludes different challenges and issues online versus offline also using deep learning versus traditional machine learning for human activity recognition based on accelerometer sensors.
Complexity geometry of a single qubit

The computational complexity of a quantum state quantifies how hard it is to make. Complexity geometry, first proposed by Nielsen, is an approach to defining computational complexity using the tools of differential geometry. Here we demonstrate many of the attractive features of complexity geometry using the example of a single qubit, which turns out to be rich enough to be illustrative but simple enough to be illuminating.
Reranking for Neural Semantic Parsing

Semantic parsing considers the task of transducing natural language (NL) utterances into machine executable meaning representations (MRs).While neural network-based semantic parsers have achieved impressive improvements over previous methods, results are still far from perfect, and cursory manual inspection can easily identify obvious problems such as lack of adequacy or coherence of the generated MRs.This paper presents a simple approach to quickly iterate and improve the performance of an existing neural semantic parser by reranking an n-best list of predicted MRs, using features that are designed to fix observed problems with baseline models.We implement our reranker in a competitive neural semantic parser and test on four semantic parsing (GEO, ATIS) and Python code generation (DJANGO, CONALA) tasks, improving the strong baseline parser by up to 5.7% absolute in BLEU (CONALA) and 2.9% in accuracy (DJANGO), outperforming the best published neural parser results on all four datasets. 1
Graph-based Dependency Parsing with Graph Neural Networks

We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing.Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently.We use graph neural networks (GNNs) to learn the representations and discuss several new configurations of GNN's updating and aggregation functions.Experiments on PTB show that our parser achieves the best UAS and LAS on PTB (96.0%, 94.3%) among systems without using any external resources.
Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference

Natural Language Inference (NLI) datasets often contain hypothesis-only biases-artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis.We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets.In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise.We evaluate our methods on synthetic and existing NLI datasets by training on datasets containing biases and testing on datasets containing no (or different) hypothesis-only biases.Our results indicate that these methods can make NLI models more robust to dataset-specific artifacts, transferring better than a baseline architecture in 9 out of 12 NLI datasets.Additionally, we provide an extensive analysis of the interplay of our methods with known biases in NLI datasets, as well as the effects of encouraging models to ignore biases and fine-tuning on target datasets. 1
Multi-Labeled Relation Extraction with Attentive Capsule Network

To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works.
Correlation-Sensitive Next-Basket Recommendation

Items adopted by a user over time are indicative of the underlying preferences. We are concerned with learning such preferences from observed sequences of adoptions for recommendation. As multiple items are commonly adopted concurrently, e.g., a basket of grocery items or a sitting of media consumption, we deal with a sequence of baskets as input, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of related items that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating information on pairwise correlations among items would help to arrive at more coherent basket recommendations. Towards this objective, we develop a hierarchical network architecture codenamed Beacon to model basket sequences. Each basket is encoded taking into account the relative importance of items and correlations among item pairs. This encoding is utilized to infer sequential associations along the basket sequence. Extensive experiments on three public real-life datasets showcase the effectiveness of our approach for the next-basket recommendation problem.
Conditional GAN with Discriminative Filter Generation for Text-to-Video Synthesis

Developing conditional generative models for text-to-video synthesis is an extremely challenging yet an important topic of research in machine learning. In this work, we address this problem by introducing Text-Filter conditioning Generative Adversarial Network (TFGAN), a conditional GAN model with a novel multi-scale text-conditioning scheme that improves text-video associations. By combining the proposed conditioning scheme with a deep GAN architecture, TFGAN generates high quality videos from text on challenging real-world video datasets. In addition, we construct a synthetic dataset of text-conditioned moving shapes to systematically evaluate our conditioning scheme. Extensive experiments demonstrate that TFGAN significantly outperforms existing approaches, and can also generate videos of novel categories not seen during training.
Human-grounded Evaluations of Explanation Methods for Text Classification

Due to the black-box nature of deep learning models, methods for explaining the models' results are crucial to gain trust from humans and support collaboration between AIs and humans.In this paper, we consider several model-agnostic and model-specific explanation methods for CNNs for text classification and conduct three human-grounded evaluations, focusing on different purposes of explanations: (1) revealing model behavior, (2) justifying model predictions, and (3) helping humans investigate uncertain predictions.The results highlight dissimilar qualities of the various explanation methods we consider and show the degree to which these methods could serve for each purpose.
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding

Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works.However, most existing models fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance.To address this issue, in this paper we propose a novel Collaborative Memory Network (CM-Net) based on the well-designed block, named CM-block.The CM-block firstly captures slot-specific and intent-specific features from memories in a collaborative manner, and then uses these enriched features to enhance local context representations, based on which the sequential information flow leads to more specific (slot and intent) global utterance representations.Through stacking multiple CM-blocks, our CM-Net is able to alternately perform information exchange among specific memories, local contexts and the global utterance, and thus incrementally enriches each other.We evaluate the CM-Net on two standard benchmarks (ATIS and SNIPS) and a self-collected corpus (CAIS).Experimental results show that the CM-Net achieves the state-of-the-art results on the ATIS and SNIPS in most of criteria, and significantly outperforms the baseline models on the CAIS.Additionally, we make the CAIS dataset publicly available for the research community 1 .
Surface-Aware Blind Image Deblurring

Blind image deblurring is a conundrum because there are infinitely many pairs of latent image and blur kernel. To get a stable and reasonable deblurred image, proper prior knowledge of the latent image and the blur kernel is urgently required. Different from the recent works on the statistical observations of the difference between the blurred image and the clean one, our method is built on the surface-aware strategy arising from the intrinsic geometrical consideration. This approach facilitates the blur kernel estimation due to the preserved sharp edges in the intermediate latent image. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on deblurring the text and natural images. Moreover, our method can achieve attractive results in some challenging cases, such as low-illumination images with large saturated regions and impulse noise. A direct extension of our method to the non-uniform deblurring problem also validates the effectiveness of the surface-aware prior.
BERT is Not an Interlingua and the Bias of Tokenization

Multilingual transfer learning can benefit both high-and low-resource languages, but the source of these improvements is not well understood.Cananical Correlation Analysis (CCA) of the internal representations of a pretrained, multilingual BERT model reveals that the model partitions representations for each language rather than using a common, shared, interlingual space.This effect is magnified at deeper layers, suggesting that the model does not progressively abstract semantic content while disregarding languages.Hierarchical clustering based on the CCA similarity scores between languages reveals a tree structure that mirrors the phylogenetic trees hand-designed by linguists.The subword tokenization employed by BERT provides a stronger bias towards such structure than character-and wordlevel tokenizations.We release a subset of the XNLI dataset translated into an additional 14 languages at https://www.github.com/salesforce/xnli_extension to assist further research into multilingual representations.
SECURITY AND PRIVACY MECHANISM USING BLOCKCHAIN

Block chain being a foundational technology impacting and attracting a wide range of applications has become predominant in solving the problem of privacy preserving and security in multitude sectors that is under the control of the government and the private. The paper also presents the security and the privacy mechanism using the block chain to prevent the misuse and the corruption in the sharing of huge set of data generated from the judiciary, security, legislature, commercial code registries etc. The proposed system enables reliability and the trust in the data sharing in the communication channels utilizing the block chain with the RSA digital signature. The proposed system is simulated as a java programming version to evince the enhancement in the latency in the sharing of the information’s along with the privacy and the security.