Reviewerly/exHarmony huggingface dataset:
Learning to Represent Bilingual Dictionaries

Bilingual word embeddings have been widely used to capture the correspondence of lexical semantics in different human languages.However, the cross-lingual correspondence between sentences and words is less studied, despite that this correspondence can significantly benefit many applications such as crosslingual semantic search and textual inference.To bridge this gap, we propose a neural embedding model that leverages bilingual dictionaries 1 .The proposed model is trained to map the lexical definitions to the cross-lingual target words, for which we explore with different sentence encoding techniques.To enhance the learning process on limited resources, our model adopts several critical learning strategies, including multi-task learning on different bridges of languages, and joint learning of the dictionary model with a bilingual word embedding model.We conduct experiments on two new tasks.In the cross-lingual reverse dictionary retrieval task, we demonstrate that our model is capable of comprehending bilingual concepts based on descriptions, and the proposed learning strategies are effective.In the bilingual paraphrase identification task, we show that our model effectively associates sentences in different languages via a shared embedding space, and outperforms existing approaches in identifying bilingual paraphrases.
Cybersecurity Evaluation of Enterprise Architectures: The e-SENS Case

Technology management through enterprise architectures has already become a widespread practice across large enterprises. Modeling and evaluating the cybersecurity aspect of it, however, has just begun to get the needed attention. This paper presents a cybersecurity evaluation methodology developed for the reference architecture of the e-SENS project and derives a generic framework for cybersecurity evaluation of an enterprise architecture. The evaluation addresses both the high-level design artefacts (the reference architecture) and operational solutions. Therefore, both a conceptual and an empirical framework are developed as part of the methodology. The former extends a goal-based security model with a threat-view incorporating standardized guidelines on security measures, whereas the latter captures and systematizes implemented project-specific security practices. The resulting methodology effectively supports the evaluation and is easy to grasp by non-technical people. Moreover, it is lendable to formalization, supporting a semi-automatic process of solution architecture design.
PCP-2LSTM: Two Stacked LSTM-Based Prediction Model for Power Consumption in Data Centers

As the size of data centers and cloud computing continue to expand, power consumption in data centers is rapidly increasing. It has a great significance to predict and analyze power consumption in the data center because power consumption prediction can help data center operators perform workflow scheduling, manage energy efficiency, provide high quality-of-service (QoS), and meet the requirements of green energy use. The current methods are mainly divided into two scopes: the one is establishing a static relationship between power consumption and relevant components/applications, and the other one is treating power consumption as sequential temporal data. However, the first scope does not consider the dynamic fluctuation of power, and the other one ignores the characteristics of the power consumption data. To solve these issues, in this paper, we present a power consumption prediction framework called PCP-2LSTM based on the mean smoothing and long short-term memory (LSTM) network. We first build a power consumption system to collect data and analyze the stationary of the power series. Then we use the mean smoothing to remove the noise from the time series of power consumption. After data preprocessing, because the time for workflow and container scheduling is usually 30 seconds, we use a stacked LSTM model to predict 30s power consumption in the future. The experimental result indicates that our approach outperforms other baselines.
Modeling of Characteristics on Artificial Intelligence IQ Test: a Fuzzy Cognitive Map-Based Dynamic Scenario Analysis

This research article uses a Fuzzy Cognitive Map (FCM) approach to improve an earlier proposed IQ test characteristics of Artificial Intelligence (AI) systems. The defuzzification process makes use of fuzzy logic and the triangular membership function along with linguistic term analyses. Each edge of the proposed FCM is assigned to a positive or negative influence type associated with a quantitative weight. All the weights are based on the defuzzified value in the defuzzification results. This research also leverages a dynamic scenario analysis to investigate the interrelationships between driver concepts and other concepts. Worst and best-case scenarios have been conducted on the correlation among concepts. We also use an inference simulation to examine the concepts importance order and the FCM convergence status. The analysis results not only examine the FCM complexity, but also draws insightful conclusions.
Investigating Reading Learning Strategies through Smartphones on Saudi Learners’ Psychological Autonomy in Reading Context

This study investigated the impact of reading learning strategies (RLS) mediated by smartphone features and applications on the learners' psychological autonomy in English as a foreign language (EFL) reading context among undergraduates in Saudi Arabia.A total of 70 male students in two intact reading classes participated in this study.A questionnaire was used to collect the quantitative data from the participants prior to and after an interventional programme.The findings of the study revealed that employment of RLS mediated by smartphone features and applications promoted the learners' psychologically autonomous features of motivation, self-efficacy, agency, positive attitudes, desire to seek information, need for achievement in EFL reading context.It is recommended that a training programme on strategy use through the technology of smartphones should be highly considered in curricula design, teaching and learning methods, training programmes in order to empower learners to take charge of their own learning of EFL reading skills.
Evaluation of geochemical baselines and metal enrichment factor values through high ecological quality reference points: a novel methodological approach

In this study, we propose a new approach to estimate geochemical local baselines and enrichment factor values for metals in riverine sediments. The goal is to describe catchment areas characterized by intensive and spread anthropogenic activities, for which it is challenging to identify undisturbed sites to utilize as reference. The case study is the Nestore river basin (Central Italy). Our approach is based on the use of ecological quality as a criterion to select the reference points in the normalization processes of metal baselines. The rationale is to assume that the sediments with a better environmental quality are anthropogenically least impaired. On these grounds, we detected geochemical local baselines and enrichment factor values of various metals (Ca, Co, Cr, Cu, Mn, Ni, Pb, Se, Sr, and Zn). Also, this approach allowed highlighting a major level of pollution for the most downstream site of Nestore river and its left tributaries.
Iterative Spaced Seed Hashing: Closing the Gap Between Spaced Seed Hashing and <i>k</i>-mer Hashing

Alignment-free classification of sequences has enabled high-throughput processing of sequencing data in many bioinformatics pipelines. Much work has been done to speed up the indexing of k-mers through hash-table and other data structures. These efforts have led to very fast indexes, but because they are k-mer based, they often lack sensitivity due to sequencing errors or polymorphisms. Spaced seeds are a special type of pattern that accounts for errors or mutations. They allow to improve the sensitivity and they are now routinely used instead of k-mers in many applications. The major drawback of spaced seeds is that they cannot be efficiently hashed and thus their usage increases substantially the computational time. In this article we address the problem of efficient spaced seed hashing. We propose an iterative algorithm that combines multiple spaced seed hashes by exploiting the similarity of adjacent hash values to efficiently compute the next hash. We report a series of experiments on HTS reads hashing, with several spaced seeds. Our algorithm can compute the hashing values of spaced seeds with a speedup in range of [3.5 × -7 × ], outperforming previous methods. Software and data sets are available at Iterative Spaced Seed Hashing.
Potential Risk Analysis Method for Malware Distribution Networks

In this study, the structural characteristics of malware distribution networks (MDNs) were examined and the network centrality of the relationships between websites containing malware, infection sites, intermediate connection sites, and initial connection sites were analyzed.The core malware sites within MDNs that contribute to the success of cyberattacks were identified, and the overall risk of the MDNs, which changes dynamically, was examined quantitatively to predict additional attacks.As such, real-time security events occurring in the information security systems of target organizations were collected and analyzed, and different types of security intelligence were assessed to recreate various MDNs.In addition, the risk levels of malicious URLs, IPs, etc. in MDNs were analyzed continuously over time, and a model suitable for predicting potential attack times was developed.The developed model identified the characteristics of potential future cyberattacks based on the analyzed initial MDN risk level, as well as the connectivity of and malware associated with the MDN, which change over time, thereby maintaining an average prediction accuracy of 94.9% over one week.
FH-GAN: Face Hallucination and Recognition Using Generative Adversarial Network

There are many factors affecting visual face recognition, such as low resolution images, aging, illumination and pose variance, etc. One of the most important problem is low resolution face images which can result in bad performance on face recognition. The modern face hallucination models demonstrate reasonable performance to reconstruct high-resolution images from its corresponding low resolution images. However, they do not consider identity level information during hallucination which directly affects results of the recognition of low resolution faces. To address this issue, we propose a Face Hallucination Generative Adversarial Network (FH-GAN) which improves the quality of low resolution face images and accurately recognize those low quality images. Concretely, we make the following contributions: (1) we propose FH-GAN network, an end-to-end system, that improves both face hallucination and face recognition simultaneously. The novelty of this proposed network depends on incorporating identity information in a GAN-based face hallucination algorithm via combining a face recognition network for identity preserving. (2) We also propose a new face hallucination network, namely Dense Sparse Network (DSNet), which improves upon the state-of-art in face hallucination. (3) We demonstrate benefits of training the face recognition and GAN-based DSNet jointly by reporting good result on face hallucination and recognition.
Key Success Factors of Information Systems Security

The issue of information systems security, and thus information as key resource in today's information society, is something that all organizations in all sectors face in one way or another. To ensure that information remain secure, many organizations have implemented a continuous, structured and systematic security approach to manage and protect an organization's information from undermining individuals by establishing security policies, processes, procedures, and information security organizational structures. However, despite this, security threats, incidents, vulnerabilities and risks are still raging in many organizations. One of the main causes of this problem is poor understanding of information systems security key success factors. Identifying and understanding of information security key success factors can help organizations to manage how to focus limited resources on those elements that really impact on success, therefore saving time and money and creating added value and further enabling operational business. This research, based on comprehensive literature review, summarizes most cited key success factors of information systems security identified in scientific articles indexed in relevant databases, of which the top three success factors were management support, information security policy and information security education, training and awareness. At the end, article states identified research gaps and provides readers with possible directions for further researches
Modification of Frodokem Using Gray and Error-Correcting Codes

Lattice-based cryptography is one of the most promising schemes for post-quantum cryptography. Among the many lattice-based cryptosystems, FrodoKEM is a well-known key-encapsulation mechanism (KEM) based on (plain) learning with errors (LWE) problems and is advantageous in that the hardness is based on the problem of unstructured lattices. There are many cryptosystems that adopt error-correcting codes (ECCs) to improve performance, such as LAC, ThreeBears, and Round5. However, for lattice-based cryptosystems that do not use ring structures such as FrodoKEM, it is difficult to use ECCs because the number of transmitted symbols is small. In this study, we propose a method to apply ECCs and Gray codes to FrodoKEM by encoding the bits converted from the encrypted symbols. It is shown that the proposed method improves the security level and/or the bandwidth of FrodoKEM, and 192 message bits, 50% more than the original 128 bits, can be transmitted using one of the modified Frodo-640's.
Improving Quality and Efficiency in Plan-based Neural Data-to-text Generation

We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al. (2019), in which the generation process is divided into a text-planning stage followed by a plan-realization stage.We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than the original planner; (2) we incorporate typing hints that improve the model's ability to deal with unseen relations and entities; (3) we introduce a verification-by-reranking stage that substantially improves the faithfulness of the resulting texts; (4) we incorporate a simple but effective referring expression generation module.These extensions result in a generation process that is faster, more fluent, and more accurate.
Squeezed Bilinear Pooling for Fine-Grained Visual Categorization

In this paper, we propose a supervised selection based method to decrease both the computation and the feature dimension of the original bilinear pooling. Different from currently existing compressed second-order pooling methods, the proposed selection method is matrix normalization applicable. Moreover, by extracting the selected highly semantic feature channels, we proposed the Fisher- Recurrent-Attention structure and achieved state-of-the-art fine-grained classification results among the VGG-16 based models.
Dynamic Embedding on Textual Networks via a Gaussian Process

Textual network embedding aims to learn low-dimensional representations of text-annotated nodes in a graph. Prior work in this area has typically focused on fixed graph structures; however, real-world networks are often dynamic. We address this challenge with a novel end-to-end node-embedding model, called Dynamic Embedding for Textual Networks with a Gaussian Process (DetGP). After training, DetGP can be applied efficiently to dynamic graphs without re-training or backpropagation. The learned representation of each node is a combination of textual and structural embeddings. Because the structure is allowed to be dynamic, our method uses the Gaussian process to take advantage of its non-parametric properties. To use both local and global graph structures, diffusion is used to model multiple hops between neighbors. The relative importance of global versus local structure for the embeddings is learned automatically. With the non-parametric nature of the Gaussian process, updating the embeddings for a changed graph structure requires only a forward pass through the learned model. Considering link prediction and node classification, experiments demonstrate the empirical effectiveness of our method compared to baseline approaches. We further show that DetGP can be straightforwardly and efficiently applied to dynamic textual networks.
Visualizing the Usage of Pythonic Idioms Over Time: A Case Study of the with open Idiom

Veterans within the Python community claim that the usage of Pythonic idiomatic writing style is usually preferred. Because of its conciseness and ease of understanding, the idiomatic code tends to be more efficient and less error-prone code. With the growth of Python developers outside the Python community, it is not certain to what extent how Python idiomatic code is used in real software projects, especially if there are consequences. In this paper, our aim is to understand when and how developers start to use idioms in their software projects. Specifically, we propose a technique to visualize and understand the usage of the with open Pythonic idiom, one of the popular idioms. Two visualizations are proposed: (1) a visualization of evolution of non-idiomatic and idiomatic style of writing in four Python software projects over time and (2) a visualization to show the amount of appearing and disappearing idioms by comparing from the first and the latest version of the projects. The results show that developers tend to adopt the idiomatic code over time. We also found that, in three out of the four projects, the developers fixed their code during the evolution of the software to improve their Pythonic coding styles.
A Proposal-Based Approach for Activity Image-to-Video Retrieval

Activity image-to-video retrieval task aims to retrieve videos containing the similar activity as the query image, which is a challenging task because videos generally have many background segments irrelevant to the activity. In this paper, we utilize R-C3D model to represent a video by a bag of activity proposals, which can filter out background segments to some extent. However, there are still noisy proposals in each bag. Thus, we propose an Activity Proposal-based Image-to-Video Retrieval (APIVR) approach, which incorporates multi-instance learning into cross-modal retrieval framework to address the proposal noise issue. Specifically, we propose a Graph Multi-Instance Learning (GMIL) module with graph convolutional layer, and integrate this module with classification loss, adversarial loss, and triplet loss in our cross-modal retrieval framework. Moreover, we propose geometry-aware triplet loss based on point-to-subspace distance to preserve the structural information of activity proposals. Extensive experiments on three widely-used datasets verify the effectiveness of our approach.
Comparison of sixteen methods for fusion of data from impulse-radar sensors and depth sensors applied for monitoring of elderly persons

This paper is devoted to the comparison of sixteen methods for fusion of measurement data from impulse-radar sensors and infrared depth sensors, i.e. two sensor technologies that may be employed in care services for elderly persons. These methods are compared with respect to their potential for decreasing the uncertainty of estimation of monitored person’s position: eight of them consist in fusing the impulse-radar data and depth data whenever new data points are available, and the other eight consist in fusing the whole sequences of the data acquired during a predefined time interval. The numerical experiments, based on the real-world data, show that the best overall results are obtained for two methods of data fusion, viz. a method based on the Kalman filter and a method using the Tikhonov regularization technique to generate a smooth approximation of the data.
Real-Time Classification Method of Network Traffic Based on Parallelized CNN

The era of big data brings explosive network traffic. Classification of network traffic can enhance the controllability of the network, help relevant personnel to grasp the distribution of traffic on the network, help network operators optimize service quality and prevent various cybercrime. However, traditional network traffic classification methods cannot satisfy the requirements of large-scale network traffic classification, and the current machine learning-based algorithms for network traffic classification are difficult to complete the high-speed parallelized real-time traffic classification task in the actual network environment. In this paper, a method is proposed to optimize the Convolutional Neural Networks (CNN) model in parallel using the Spark platform, and the Spark Streaming framework is put forward to implement the requirements for real-time classification of network traffic. Besides, the performance of our method is evaluated. The experimental results show that the proposed method has good real-time performance while ensuring high classification accuracy, and it can implement the task of highspeed parallel network flow real-time classification.
History-Adaption Knowledge Incorporation Mechanism for Multi-Turn Dialogue System

Keeping the conversation consistent and avoiding its repetition are two key factors to construct an intelligent multi-turn knowledge-grounded dialogue system. Although some works tend to combine history with external knowledge such as personal background information to boost dialogue quality, they are prone to ignore the fact that incorporating the same knowledge multiple times into the conversation leads to repetition. The main reason is the lack of effective control over the use of knowledge on the conversation level. So we design a history-adaption knowledge incorporation mechanism to build an effective multi-turn dialogue model. Our proposed model addresses repetition by recurrently updating the knowledge from the conversation level and progressively incorporating it into the history step-by-step. And the knowledge-grounded history representation also enhances the conversation consistency. Experimental results show that our proposed model significantly outperforms several retrieval-based models on some benchmark datasets. The human evaluation demonstrates that our model can maintain conversation consistent and reduce conversation repetition.
Scale robust deep oriented-text detection network

Text detection is a prerequisite of text recognition, and multi-oriented text detection is a hot topic recently. The existing multi-oriented text detection methods fall short when facing two issues: 1) text scales change in a wide range, and 2) there exists the foreground-background class imbalance. In this paper, we propose a scale-robust deep multi-oriented text-detection model, which not only has the efficiency of the one-stage deep detection model, but also has the comparable accuracy of the two-stage deep text-detection model. We design the feature refining block to fuse multi-scale context features for the purpose of keeping text detection in a higher-resolution feature map. Moreover, in order to mitigate the foreground-background class imbalance, Focal Loss is adopted to up weight the hard-classified samples. Our method is implemented on four benchmark text datasets: ICDAR2013, ICDAR2015, COCO-Text and MSRA-TD500. The experimental results demonstrate that our method is superior to the existing one-stage deep text-detection models and comparable to the state-of-the-art text detection methods.