Reviewerly/exHarmony huggingface dataset:
Textual data summarization using the Self-Organized Co-Clustering model

Recently, different studies have demonstrated the use of co-clustering, a data mining technique which simultaneously produces row-clusters of observations and column-clusters of features. The present work introduces a novel co-clustering model to easily summarize textual data in a document-term format. In addition to highlighting homogeneous co-clusters as other existing algorithms do we also distinguish noisy co-clusters from significant co-clusters, which is particularly useful for sparse document-term matrices. Furthermore, our model proposes a structure among the significant co-clusters, thus providing improved interpretability to users. The approach proposed contends with state-of-the-art methods for document and term clustering and offers user-friendly results. The model relies on the Poisson distribution and on a constrained version of the Latent Block Model, which is a probabilistic approach for co-clustering. A Stochastic Expectation-Maximization algorithm is proposed to run the model’s inference as well as a model selection criterion to choose the number of co-clusters. Both simulated and real data sets illustrate the efficiency of this model by its ability to easily identify relevant co-clusters.
A Multi-Task Architecture on Relevance-based Neural Query Translation

We describe a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query translation.The translation process for Cross-lingual Information Retrieval (CLIR) task is usually treated as a black box and it is performed as an independent step.However, an NMT model trained on sentence-level parallel data is not aware of the vocabulary distribution of the retrieval corpus.We address this problem with our multitask learning architecture that achieves 16% improvement over a strong NMT baseline on Italian-English query-document dataset.We show using both quantitative and qualitative analysis that our model generates balanced and precise translations with the regularization effect it achieves from multi-task learning paradigm.
Poetry to Prose Conversion in Sanskrit as a Linearisation Task: A Case for Low-Resource Languages

The word ordering in a Sanskrit verse is often not aligned with its corresponding prose order.Conversion of the verse to its corresponding prose helps in better comprehension of the construction.Owing to the resource constraints, we formulate this task as a word ordering (linearisation) task.In doing so, we completely ignore the word arrangement at the verse side.kāvya guru, the approach we propose, essentially consists of a pipeline of two pretraining steps followed by a seq2seq model.The first pretraining step learns task specific token embeddings from pretrained embeddings.In the next step, we generate multiple hypotheses for possible word arrangements of the input (Wang et al., 2018).We then use them as inputs to a neural seq2seq model for the final prediction.We empirically show that the hypotheses generated by our pretraining step result in predictions that consistently outperform predictions based on the original order in the verse.Overall, kāvya guru outperforms current state of the art models in linearisation for the poetry to prose conversion task in Sanskrit.
Predicting Students’ Security Behavior Using Information-Motivation-Behavioral Skills Model

The Information-Motivation-Behavioral Skills (IMB) Model has shown reliability in predicting behaviors related to health and voting. In this study, we examine whether the IMB Model could predict security behavior among university students. Using a cross-sectional design and proxy IMB variables, data was collected from 159 Finnish students on their security threats' awareness (representing IMB's information variable), attitude toward information security and social motivation (replacing IMB's motivation variable), self-efficacy and familiarity with security measures (variables related to IMB's behavioral skills), and self-reported security behavior (IMB outcome variable). An analysis conducted with PLS-SEM v3.2 confirmed that the IMB Model was an appropriate model to explain and predict security behavior of the university students. Path analysis showed that behavioral skills measures predict security behavior directly, while students' information and motivation variables predicted security behavior through behavioral skills (self-efficacy and familiarity with security measures). The findings suggest that the security behavior of students can be improved by improving threat knowledge, their motivation and behavioral skills – supporting the use of the IMB Model in this context and combination with existing predictors.
DBSCAN-MS: Distributed Density-Based Clustering in Metric Spaces

DBSCAN is one of important density-based clustering methods, which has a wide range of applications in machine learning and data mining, to name but a few. However, the rapid growing volume and variety of data nowadays challenges traditional DBSCAN, and thus, distributed DBSCAN in metric spaces is required. In this paper, we propose DBSCAN-MS, a distributed density-based clustering in metric spaces. To ensure load balancing, we present a k-d tree based partitioning approach. It utilizes pivots to map the data in metric spaces to vector spaces, and employs k-d tree partitioning technique to equally divide the data. To avoid unnecessary computation and communication cost, we propose a framework that divides data into partitions, find out local DBSCAN result, and merge local result based on a merging graph. In addition, the pivot filtering and the sliding window techniques are also used in the framework for pruning. Extensive experiments with both real and synthetic datasets demonstrate the efficiency and scalability of our proposed DBSCAN-MS.
Uncovering Probabilistic Implications in Typological Knowledge Bases

The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have postpositions.Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which potentially leaves key linguistic universals unexplored.In this paper, we present a computational model which successfully identifies known universals, including Greenberg universals, but also uncovers new ones, worthy of further linguistic investigation.Our approach outperforms baselines previously used for this problem, as well as a strong baseline from knowledge base population.
NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wild

As online customer forums and product comparison sites increase their societal influence, users are actively expressing their opinions and posting their recommendations on their fellow customers online.However, systems capable of recognizing suggestions still lack in stability.Suggestion Mining, a novel and challenging field of Natural Language Processing, is increasingly gaining attention, aiming to track user advice on online forums.In this paper, a carefully designed methodology to identify customer-to-company and customerto-customer suggestions is presented.The methodology implements a rule-based classifier using heuristic, lexical and syntactic patterns.The approach ranked at 5 th and 1 st position, achieving an f1-score of 0.749 and 0.858 for SemEval-2019/Suggestion Mining sub-tasks A and B, respectively.In addition, we were able to improve performance results by combining the rule-based classifier with a recurrent convolutional neural network, that exhibits an f1-score of 0.79 for subtask A.
Vehicle tracking based on shape information and inter-frame motion vector

Vehicle tracking is one of the most fundamental aspects in traffic surveillance systems. However, there is always a trade–off between tracking accuracy and speed. Tracking algorithms with excellent tracking accuracy tend to have low processing speeds, whereas fast tracking algorithms usually have low tracking accuracy. This paper proposes a vehicle tracking algorithm based on the simple motion of vehicles in traffic surveillance videos. In the proposed algorithm, an inter–frame motion vector is defined using vehicle speed and acceleration in the current frame to forecast the vehicle speed in subsequent frames. Further, speed and shape error functions are defined to match the target. The results of experiments conducted comparing the proposed method with kernelized correlation filter, multiple instance learning, and Kalman filter indicate that only the proposed algorithm achieves a balance in terms of both speed and accuracy, resulting in satisfactory tracking accuracy and meeting real–time requirements.
Enhanced Video Compression Based on Effective Bit Depth Adaptation

This paper presents a novel Convolutional Neural Network (CNN) based effective bit depth adaptation approach (EBDA-CNN) for video compression. It applies effective bit depth down-sampling before encoding and reconstructs the original bit depth using a deep CNN based up-sampling method at the decoder. The proposed approach has been integrated with the High Efficiency Video Coding reference software HM 16.20, and evaluated under the Joint Video Exploration Team Common Test Conditions using the Random Access configuration. The results show consistent coding gains on all tested sequences, with an average bitrate saving of 6.4%, based on Bjøntegaard Delta measurements using PSNR.
Batch tournament selection for genetic programming

Lexicase selection achieves very good solution quality by introducing ordered test cases. However, the computational complexity of lexicase selection can prohibit its use in many applications. In this paper, we introduce Batch Tournament Selection (BTS), a hybrid of tournament and lexicase selection which is approximately one order of magnitude faster than lexicase selection while achieving a competitive quality of solutions. Tests on a number of regression datasets show that BTS compares well with lexicase selection in terms of mean absolute error while having a speed-up of up to 25 times. Surprisingly, BTS and lexicase selection have almost no difference in both diversity and performance. This reveals that batches and ordered test cases are completely different mechanisms which share the same general principle fostering the specialization of individuals. This work introduces an efficient algorithm that sheds light onto the main principles behind the success of lexicase, potentially opening up a new range of possibilities for algorithms to come.
Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs

Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts.Typically, the contexts of a word are defined as its closest neighbours, but they can also be retrieved from its syntactic dependency relations.In this work, we propose a new dependencybased DSM.The novelty of our model lies in associating an independent meaning representation, a matrix, with each dependency-label.This allows it to capture specifics of the relations between words and contexts, leading to good performance on both intrinsic and extrinsic evaluation tasks.In addition to that, our model has an inherent ability to represent dependency chains as products of matrices which provides a straightforward way of handling further contexts of a word.
OleNet at SemEval-2019 Task 9: BERT based Multi-Perspective Models for Suggestion Mining

This paper describes our system participated in Task 9 of SemEval-2019: the task is focused on suggestion mining and it aims to classify given sentences into suggestion and non-suggestion classes in domain specific and cross domain training setting respectively.We propose a multi-perspective architecture for learning representations by using different classical models including Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Feed Forward Attention (FFA), etc.To leverage the semantics distributed in large amount of unsupervised data, we also have adopted the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model as an encoder to produce sentence and word representations.The proposed architecture is applied for both sub-tasks, and achieved f1-score of 0.7812 for subtask A, and 0.8579 for subtask B. We won the first and second place for the two tasks respectively in the final competition.
The teaching size: computable teachers and learners for universal languages

The theoretical hardness of machine teaching has usually been analyzed for a range of concept languages under several variants of the teaching dimension: the minimum number of examples that a teacher needs to figure out so that the learner identifies the concept. However, for languages where concepts have structure (and hence size), such as Turing-complete languages, a low teaching dimension can be achieved at the cost of using very large examples, which are hard to process by the learner. In this paper we introduce the teaching size, a more intuitive way of assessing the theoretical feasibility of teaching concepts for structured languages. In the most general case of universal languages, we show that focusing on the total size of a witness set rather than its cardinality, we can teach all total functions that are computable within some fixed time bound. We complement the theoretical results with a range of experimental results on a simple Turing-complete language, showing how teaching dimension and teaching size differ in practice. Quite remarkably, we found that witness sets are usually smaller than the programs they identify, which is an illuminating justification of why machine teaching from examples makes sense at all.
On the Performance of Interleavers for Quantum Turbo Codes

Quantum turbo codes (QTC) have shown excellent error correction capabilities in the setting of quantum communication, achieving a performance less than 1 dB away from their corresponding hashing bounds. Existing QTCs have been constructed using uniform random interleavers. However, interleaver design plays an important role in the optimization of classical turbo codes. Consequently, inspired by the widely used classical-to-quantum isomorphism, this paper studies the integration of classical interleaving design methods into the paradigm of quantum turbo coding. Simulations results demonstrate that error floors in QTCs can be lowered significantly, while decreasing memory consumption, by proper interleaving design without increasing the overall decoding complexity of the system.
Cryptographic one-way function based on boson sampling

The quest for practical cryptographic primitives that are robust against quantum computers is of vital importance for the field of cryptography. Among the abundance of different cryptographic primitives one may consider, one-way functions stand out as fundamental building blocks of more complex cryptographic protocols, and they play a central role in modern asymmetric cryptography. We propose a mathematical one-way function, which relies on coarse-grained boson sampling. The evaluation and the inversion of the function are discussed in the context of classical and quantum computers. The present results suggest that the scope and power of boson sampling may go beyond the proof of quantum supremacy and pave the way toward cryptographic applications.
From Imprecise N-Way Model Matching to Precise N-Way Model Merging.

N-way model merging is a key technique for managing software variability, by integrating N concurrent model variants/versions into one.Most merging techniques are based on three operators: (1) compare, (2) match and (3) merge.As finding optimal solutions for (2) in terms of matching precision is NP-hard, many proposals for scalable yet sufficiently precise matching heuristics exist.However, most approaches are either generic which obstructs precision if applied to realistic modeling languages, or they require excessive computational upfront investment already in step (1) to ensure sufficient precision.We propose an alternative approach for automated construction of precise model merges by focusing on step (3): given an arbitrarily imprecise matching, we incrementally apply default modelrefactoring operators, as available for most mature modeling languages, to identify and unify further similarities among initially unmatched model elements.For those model-refactoring operators to produce correct results if applied to merged models, we utilize variability encoding as known from product-line engineering.Our tool implementation supports any EMFcompliant modeling language and is instantiated for UML class diagrams to demonstrate our methodology.Our evaluation results show that our technique (a) preserves the precision of near optimal matchings and (b) remarkably improves the precision of arbitrarily imprecise matchings while requiring acceptable computational effort.
Using Phonetic Posteriorgram Based Frame Pairing for Segmental Accent Conversion

Accent conversion (AC) aims to transform non-native utterances to sound as if the speaker had a native accent. This can be achieved by mapping source speech spectra from a native speaker into the acoustic space of the target non-native speaker. In prior work, we proposed an AC approach that matches frames between the two speakers based on their acoustic similarity after compensating for differences in vocal tract length. In this paper, we propose a new approach that matches frames between the two speakers based on their phonetic (rather than acoustic) similarity. Namely, we map frames from the two speakers into a phonetic posteriorgram using speaker-independent acoustic models trained on native speech. We thoroughly evaluate the approach on a speech corpus containing multiple native and non-native speakers. The proposed algorithm outperforms the prior approach, improving ratings of acoustic quality (22% increase in mean opinion score) and native accent (69% preference) while retaining the voice quality of the non-native speaker. Furthermore, we show that the approach can be used in the reverse conversion direction, i.e., generating speech with a native speaker's voice quality and a non-native accent. Finally, we show that this approach can be applied to non-parallel training data, achieving the same accent conversion performance.
Multilayer Convolutional Feature Aggregation Algorithm for Image Retrieval

In image retrieval tasks, the single-layer convolutional feature has insufficient image semantic representation ability. A new image description algorithm ML-RCroW based on multilayer multiregion cross-weighted aggregational deep convolutional features is proposed. First, the ML-RCroW algorithm inputs an image into the VGG16 (a deep convolutional neural network developed by researchers at Visual Geometry Group and Google DeepMind) network model in which the fully connected layer is discarded. The visual feature information in the convolutional neural network (CNN) is extracted, and the target response weight map is generated by combining with the spatial weighting algorithm of the target fuzzy marker. Then, visual features in the CNN are divided into multiple regions, and the pixels of each region are weighted by regional spatial weight, regional channel weight, and regional weight. The image global vector is generated by aggregating and encoding every region in the weighted feature map. Finally, features of each layer of the VGG16 network model are extracted and then aggregated and dimensionally reduced to obtain the final feature vector of the image. The experiments are carried out on the Oxford5k and Paris6k datasets provided by Oxford VGG. The experimental results show that the average accuracy of image retrieval based on the image feature description algorithm ML-RCroW is better than that achieved by the other commonly used algorithms such as SPoC, R-MAC, and CroW.
Facial Action Units for Training Convolutional Neural Networks

This paper deals with the problem of training convolutional neural networks (CNNs) with facial action units (AUs).In particular, we focus on the imbalance problem of the training datasets for facial emotion classification.Since training a CNN with an imbalanced dataset tends to yield a learning bias toward the major classes and eventually leads to deterioration in the classification accuracy, it is required to increase the number of training images for the minority classes to have evenly distributed training images over all classes.However, it is difficult to find the images with a similar facial emotion for the oversampling.In this paper, we propose to use the AU features to retrieve an image with a similar emotion.The query selection from the minority class and the AU-based retrieval processes repeat until the numbers of training data over all classes are balanced.Also, to improve the classification accuracy, the AU features are fused with the CNN features to train a support vector machine (SVM) for final classification.The experiments have been conducted on three imbalanced facial image datasets, RAF-DB, FER2013, and ExpW.The results demonstrate that the CNNs trained with the AU features improve the classification accuracy by 3%-4%.
A dual-mode deep transfer learning (D2TL) system for breast cancer detection using contrast enhanced digital mammograms

Full-field digital mammography (FFDM) and magnetic resonance imaging (MRI) are gold-standard techniques for breast cancer detection. The newly contrast-enhanced digital mammography (CEDM) integrates the complementary strengths of FFDM and MRI, and is being incorporated into the practice of leading institutions. The current clinical practice using CEDM is sub-optimal because it is primarily based on clinicians' trained eyes. Automated diagnostic systems under the conventional machine learning paradigm suffer from drawbacks, such as the requirement for precise segmentation, extraction of shallow features that do not suffice for diagnostic images, and adoption of a sequential design without a global objective. We propose a deep learning (DL)-empowered diagnostic system using CEDM, the core of which is a novel dual-mode deep transfer learning (D2TL) model. The proposed system is innovative in several aspects, including (1) a dual-mode deep architecture design; (2) use of transfer learning to facilitate robust model estimation under small sample-size; (3) development of visualization techniques to help interpret the model results and facilitate inter- and intra-tumor malignancy quantification; and (4) minimization of human bias. We apply D2TL to classify benign vs. malignant tumors using the CEDM data collected from the Mayo Clinic in Arizona. D2TL outperforms competing models and approaches.