Reviewerly/exHarmony huggingface dataset:
MELODIC: Selection and Integration of Open Source to Build an Autonomic Cross-Cloud Deployment Platform

MELODIC is and open source platform for autonomic deployment and optimized management of Cross-Cloud applications. The MELODIC platform is a complete, enterprise ready solution using only open source software. The contribution of this paper is the discussion of approaches to integration and various options for large scale open source projects and their evaluation showing that only a combination of an Enterprise Service Bus (ESB) with Business Process Management (BPM) for platform integration and control, and the use of a distributed Event Management Services (EMS) for monitoring state and creating context awareness, will provide the required stability and reliability. Consequently, the selection, the evaluation, and the design process of these three crucial components of the MELODIC platform are described.
Self-Evacuation Drills by Mobile Virtual Reality Application to Enhance Earthquake Preparedness

Traditional evacuation drills by self-rescue practice are a common method used to educate society in communities, companies or schools in Indonesia which involving participants in large numbers. However, the participants in this approach are not always committed and doing it seriously in some cases. Besides, the participants were also unable to feel the earthquake sensation because no shaking at all when practice evacuation drills. For this reason, it is necessary to design a tool that can be used to practice by themselves with no involving many people, but the sensation of shaking can be felt by the users, one way is using Virtual Reality (VR) technology. This study aims to develop VR-based applications that can create a sensation of shaking such as an earthquake and can be used individually. The method used in this study is initial research, development, testing and analyzing, and recommendations. The development carried out by Unity 3D software which is equipped with GoogleVR plugin. The testing was conducted on 14 students of SMK Batik Batik 1 Surakarta, Indonesia, whose average age of 16 years old and the habit of playing games is 2-3 hours per day on average. From the experiments conducted, 71% stated that earthquake simulations using VR applications are more interesting than animated video, because they can interact with virtual objects directly and the simulations are more realistic. Thus, it can be concluded that the VR application can be used for earthquake evacuation exercises independently, further hope that user preparedness will be better in dealing with natural disasters, especially earthquakes
WatchPen

Pen-based input is often treated as auxiliary to mobile devices. We posit that cross-device interactions can inspire and extend the design space of pen-based interactions into new, expressive directions. We realize this through WatchPen, a smartwatch mounted on a passive, capacitive stylus that: (1) senses the usage context and leverages it for expression (e.g., changing colour), (2) contains tools and parameters within the display, and (3) acts as an on-demand output. As a result, it provides users with a dynamic relationship between inputs and outputs, awareness of current tool selection and parameters, and increased expressive match (e.g., added ability to mimic physical tools, showing clipboard contents). We discuss and reflect upon a series of interaction techniques that demonstrate WatchPen within a drawing application. We highlight the expressive power of leveraging multiple sensing and output capabilities across both the watch-augmented stylus and the tablet surface.
Automatic transformation between water level meter measurement data and altitude on the large-scale physical model

The water level is one of the most important physical parameters of the physical model experiment. A large-scale physical model usually has many water level meters, It is an important basis for the accurate simulation of the water level to determine the transformation relationship between those level meters readings and the altitude in the prototype. Based on collimated characteristics of laser and image processing by binarization of gray image and character segmentation, an automatic transformation system was developed to improve transformation accuracy. The main idea is to use laser scanner to build a reference plane, then light illuminates the water level meter, so that the CCD on the top of the water level meter could gather the laser beam and convert light intensity to image signals. With further image processing, such as threshold transformation, the system can get the coordinate of laser center. Due to the fixed relationship between the coordinate and the water level meter measurement data, then the system can found transformation relation between water level meter and altitude automatically. Experiments show that comparing with the manual calibration, relative error of automatic calibration water level meter altitude conversion decreased from ±1.34% to ±0.19%.
An optimization algorithm based on text clustering for warehouse storage location allocation

With the rapid development of e-commerce and logistics, people want to increase the efficiency of order picking in warehouses to meet the growing volume of orders. Optimizing the distribution of items in the warehouse location is one of the main ways to improve the efficiency of order picking. Some researchers have carried out correlation analysis to optimize the allocation of storage space in the case of strong correlation among order items. In the case of weak correlation among items, the method of pure correlation analysis is no longer applicable. In response to this situation, this paper proposes an algorithm based on text clustering and correlation analysis. In the case of weak correlation among items, this algorithm can optimize warehouse storage location allocation, aiming to reduce the total distance of order picking operations. In this paper, the real business history orders is used to simulate the experiment. By comparing with two other commonly-used storage location allocation algorithms, the allocation of the storage location determined by the algorithm proposed in this paper reduces significantly the total distance of the order picking operation.
SimilarMF: A Social Recommender System Using an Embedding Method

The main task of recommender systems is to recommend items or any other information which the particular user is interested in accurately and efficiently. However, traditional recommender systems usually suffer from the data sparsity problem and cold start problem. To alleviate these problems, social recommender systems usually combine the social network data and rating data together to improve the accuracy of prediction results. In this paper, we propose SimilarMF, a novel and accurate social recommender system trying to improve the performance by means of utilizing the implicit similarity information among users and items. Firstly, SimilarMF integrates social network with rating data to construct a user similarity graph and an item similarity graph. Secondly, it adopts an embedding method to acquire enough similar user-user pairs and item-item pairs. It also performs negative sampling for dissimilar pairs. Lastly, it incorporates the extracted information into matrix factorization framework to predict ratings more accurately. The experimental results show that our method offers better recommendations than its competitors.
Cryptanalysis of Two Chaotic Maps-Based Authentication Schemes in Edge Computing

Nowadays, Edge Computing plays an important role in today's constantly increasing data-demanding services such as healthcare and augmented reality. To facilitate external users to enjoy remote data directly and securely from such kinds of environments, user authentication constitutes the most prevailing access control mechanism. Over the past decades, considerable efforts have been devoted to proposing secure and usable two-factor authentication schemes by making use of chaotic maps, yet most of the existing schemes are far from practical. In this work, we make an attempt towards improving this undesirable situation by reviewing two representative schemes presented by Kumari et al. and Li et al. and providing reasonable solving methods. We find that Kumari et al.'s scheme is confronted with verifier table issue, also cannot withstand smart card loss attack, impersonation attack and Li et al.'s scheme fails to realize the imperative goal of truly two-factor security and forward secrecy. Finally, we present some reasonable fixes for these identified weaknesses.
Group Learning for High-Dimensional Sparse Data

We describe new methodology for supervised learning with sparse data, i.e., when the number of input features is (much) larger than the number of training samples (n). Under the proposed approach, all available (d) input features are split into several (t) subsets, effectively resulting in a larger number (t*n) of labeled training samples in lower-dimensional input space (of dimensionality d/t). This (modified) training data is then used to estimate a classifier for making predictions in lower-dimensional space. In this paper, standard SVM is used for training a classifier. During testing (prediction), a group of t predictions made by SVM classifier needs to be combined via intelligent post-processing rules, in order to make a prediction for a test input (in the original d-dimensional space). The novelty of our approach is in the design and empirical validation of these post-processing rules under Group Learning setting. We demonstrate that such post-processing rules effectively reflect general (common-sense) a priori knowledge (about application data). Specifically, we propose two different post-processing schemes and demonstrate their effectiveness for two real-life application domains, i.e., handwritten digit recognition and seizure prediction from iEEG signal. These empirical results show superior performance of the Group Learning approach for sparse data, under both balanced and unbalanced classification settings.
Pyramid Attention Dense Network for Image Super-Resolution

Recent deep convolution neural networks has made remarkable progress in single images super-resolution area. They achieved very high Peak Signal to Noise Ratio (PSNR) and structural similarity (SSIM), by improved learning of high-frequency details to enhance visual perception. However, current models usually ignore relations between adjacent pixels. In this work, we propose a network that incorporate gradients of adjacent pixels in addition to per-pixel loss and perceptual loss. In addition, we utilize multi-stage network learning to progressively generate high resolution images, by incorporate a new inter-stage feedback in the Laplacian pyramid network structure. Furthermore, we adopted recently proposed attention mechanism and dense block structure. The proposed Pyramid Attention Dense model for image super-resolution achieved state-of-the-art performance in experiments on four benchmark datasets.
BM25 Beyond Query-Document Similarity

The massive growth of information produced and shared online has made retrieving relevant documents a difficult task. Query Expansion (QE) based on term co-occurrence statistics has been widely applied in an attempt to improve retrieval effectiveness. However, selecting good expansion terms using co-occurrence graphs is challenging. In this paper, we present an adapted version of the BM25 model, which allows measuring the similarity between terms. First, a context window-based approach is applied over the entire corpus in order to construct the term co-occurrence graph. Afterward, using the proposed adapted version of BM25, candidate expansion terms are selected according to their similarity with the whole query. This measure stands out by its ability to evaluate the discriminative power of terms and select semantically related terms to the query. Experiments on two ad-hoc TREC collections (the standard Robust04 collection and the new TREC Washington Post collection) show that our proposal outperforms the baselines over three state-of-the-art IR models and leads to significant improvements in retrieval effectiveness.
Multi-label Classification Models for Detection of Phonetic Features in building Acoustic Models

Acoustic modeling in large vocabulary continuous speech recognition systems is commonly done by building the models for subword units such as phonemes, syllables or senones. In recent years, various end-to-end systems using acoustic models built at grapheme or phoneme level have also been explored. These systems either require a lot of data and/or heavily rely on the use of language models or pronunciation dictionary for good recognition performance. With the intention of reducing the dependence on data or external models, we have explored the usage of phonetic features in building acoustic models for speech recognition. The phonetic features describe a sound based on the speech production mechanism in humans. Multi-label classification models are built for detection of phonetic features in a given speech signal. The detected phonetic features are used along with the acoustic features as input to models for phoneme identification. The effectiveness of the proposed approach is demonstrated on TIMIT and Wall Street Journal corpora. Performance improvement over other phoneme recognition studies using the phonetic features is obtained.
Variational Attention for Commonsense Knowledge Aware Conversation Generation

Conversation generation is an important task in natural language processing, and commonsense knowledge is vital to provide a shared background for better replying. In this paper, we present a novel commonsense knowledge aware conversation generation model, which adopts variational attention for incorporating commonsense knowledge to generate more appropriate conversation. Given a post, the model retrieves relevant knowledge graphs from a knowledge base, and then attentively incorporates knowledge to its response. For enhancing attention to incorporate more clean and suitable knowledge into response generation, we adopt variational attention rather than standard neural attention on knowledge graphs, which is unlike previous knowledge aware generation models. Experimental results show that the variational attention based model can incorporate more clean and suitable knowledge into response generation.
Rumors of Our Death…

Abstract Núñez and colleagues (2019) question whether cognitive science still exists “as a coherent academic field with a well‐defined and cohesive interdisciplinary research program.” This worry may be premature on two grounds. First, we are not convinced that the Lakatosian criterion of coalescence around a core framework is the best standard for judging whether a field is well‐defined and productive. Second, although we acknowledge that cognitive science is not as visible as we would like, we doubt that this low profile accurately reflects the state of actual research and teaching programs based on the cognitive science approach.
Fundamentals of Software Quality

The goal of a software company is to develop high-quality innovative products and services at a competitive price to its customers, and this requires a focus on software quality and customer satisfaction. It is more effective to build quality into the software product rather than addressing quality issues late in the project. Customers have very high expectations on quality and expect high-quality software products to be consistently delivered on time and on budget. The focus on quality requires effective software processes to be in place so that quality software may be consistently produced.
Investigation of Matrix Power Asymmetric Cipher Resistant to Linear Algebra Attack

This paper continues our research of the so-called matrix power function and its application to cryptography. We consider the simplest case of the improved matrix power asymmetric cypher (MPAC). We show that our protocol is resistant to linear algebra attack, which can be applied to the initial version of MPAC to break it in polynomial time. Our main goal is to present the general idea for the choice of public parameters of MPAC protocol to avoid this attack while also avoiding exhaustive search attack.
Detecting and Translating Dropped Pronouns in Neural Machine Translation

Pronouns are commonly omitted in Chinese as well as other pro-drop languages, which causes a significant challenge to neural machine translation (NMT) between pro-drop and non-pro-drop languages. In this work, we propose a method to both automatically detect the dropped pronouns (DPs) and recover their translation equivalences rather than their original forms in source sentences. The detection and recovery are simultaneously performed as a sequence labeling task on source sentences. The recovered translation equivalences of DPs are incorporated into NMT as external lexical knowledge via a tagging mechanism. Experimental results on a large-scale Chinese-English dialogue translation corpus demonstrate that the proposed method is able to achieve a significant improvement over a strong baseline and is better than the method of recovering the original forms of DPs.
Clustering of Data With Missing Entries Using Non-Convex Fusion Penalties

The presence of missing entries in data often creates challenges for pattern recognition algorithms. Traditional algorithms for clustering data assume that all the feature values are known for every data point. We propose a method to cluster data in the presence of missing information. Unlike conventional clustering techniques where every feature is known for each point, our algorithm can handle cases where a few feature values are unknown for every point. For this more challenging problem, we provide theoretical guarantees for clustering using a $l_0$ fusion penalty based optimization problem. Furthermore, we propose an algorithm to solve a relaxation of this problem using saturating non-convex fusion penalties. It is observed that this algorithm produces solutions that degrade gradually with an increase in the fraction of missing feature values. We demonstrate the utility of the proposed method using a simulated dataset, the Wine dataset and the ASL dataset. It is shown that the proposed method is a promising clustering technique for datasets with large fractions of missing entries.
Automatic Question Tagging Using Multi-label Classification in Community Question Answering Sites

Community question answering sites facilitate their users to ask question and get answer from other users who has the knowledge to answer the questions. CQA sites uses tags to categorize questions into several topics so that users can easily find out the questions useful to them and related to their field of expertise. The task of annotating tags to question cannot be entirely left on users because sometimes it may happens that user who asks the question do not know the accurate tags for the question being asked. Questions with correct and accurate tags are informative and likely to get good answers. In this paper, we present a model to automatically provide tags to a question asked. We considered the problem of annotating tags to a question as a multi-label classification task. Multi-label classification is implemented to formulate question tagging as it allows adding multiple labels to a single instance. To evaluate the presented model, a set of evaluation parameters such as accuracy, hamming loss, and zero-one loss are used. The obtained results are remarkable in automatic question tagging.
Supervised Kernel Transform Learning

This work introduces certain supervised formulations for transform learning. Transform learning is the analysis equivalent of dictionary learning. Four different types of supervision penalties are proposed. The first one is class-sparsity, which imposes common sparse support within representations of each class. The second one imposes similarity among intra-class features in terms of a low-rank constraint (high cosine similarity). The third penalty enforces features of the same class to be nearby each other and features of different classes to be far apart. The final formulation is the well known label-consistency formulation which learns a linear map from the feature space to the class targets. For the first time, we show how transform learning (and its supervised versions can be kernelized). Finally this work also introduces stochastic regularization techniques like DropOut and DropConnect into the transform learning formulation. Experiments have been carried out on two different problems - computer vision and biomedial signal analysis. In both the problems, our method excels over all existing ones.
RNN-based speech synthesis using a continuous sinusoidal model

Recently in statistical parametric speech synthesis, we proposed a continuous sinusoidal model (CSM) using continuous F0 (contF0) in combination with Maximum Voiced Frequency (MVF), which was successfully giving state-of-the-art vocoders performance (e.g. similar to STRAIGHT) in synthesized speech. In this paper, we address the use of sequence-to-sequence modeling with recurrent neural networks (RNNs). Bidirectional long short-term memory (Bi-LSTM) is investigated and applied using our CSM to model contF0, MVF, and Mel-Generalized Cepstrum (MGC) for more natural sounding synthesized speech. For refining the output of the contF0 estimation, post-processing based on time-warping approach is applied to reduce the unwanted voiced component of the unvoiced speech sounds, resulting in an enhanced contF0 track. The overall conclusion is covered by objective evaluation and subjective listening test, showing that the proposed framework provides satisfactory results in terms of naturalness and intelligibility, and is comparable to the high-quality WORLD model based RNNs.
Efficient Randomized Feature Selection Algorithms

Feature selection is a core problem in machine learning. It plays an important role in making efficient and explainable machine-driven decisions. Embedded feature selection methods, such as decision trees and LASSO, suffer from learner dependency and cannot be applied well to many popular learners. Wrapper methods, which fit arbitrary learning models, are receiving growing interests in many scientific fields. In order to effectively search relevant features in wrapper methods, many randomized schemes have been proposed. In this paper, we present efficient randomized feature selection algorithms empowered by automatic breadth searching and attention searching adjustments. Our schemes are generic and highly parallelizable in nature and can be easily applied to many related algorithms. Theoretical analysis proves the efficiency of our algorithms. Extensive experiments on synthetic and real dataset show that our techniques achieve significant improvements in the selected features' quality and selection time.
Learning Stance Classification with Recurrent Neural Capsule Network

Stance classification is a natural language processing (NLP) task to detect author's stance when give a specific target and context, which can be applied in online debating forum, e.g., Twitter, Weibo, etc. In this paper, we present a novel target orientation recurrent neural capsule network, called TRNN-Capsule to solve the problem. In TRNN-Capsule, the target and context are both encoded by leveraging a bidirectional LSTM model. Then, capsule blocks are appended to produce the final classification outcome. Experiments on two benchmark data sets are conducted and the results show that the proposed TRNN-Capsule outperforms state-of-the-art competitors for the stance classification task.