Reviewerly/exHarmony huggingface dataset:
Accelerating Machine Learning I/O by Overlapping Data Staging and Mini-batch Generations

The training dataset used in deep neural networks (DNNs) keeps on increasing. When a training dataset grows larger, the reading performance of such a large training dataset becomes a problem. A high-performance computing (HPC) cluster has high performance I/O storage devices, for example, NVMe SSD, as local storage on each compute node. This high-performance I/O storage can mitigate the I/O bottleneck. However, such storage devices provide only temporary storage, therefore the users have to copy the training dataset from shared storage (such as Lustre) into local storage. Large datasets (over a few hundred GiB) takes a long time to copy the datasets between local storage and shared storage. To solve this problem, we propose a method to conceal the time spent on copying dataset to local storage by overlapping the copying and reading of training data. We implemented the proposed method at the machine learning framework Chainer. The results of our experiments showed that the read I/O bandwidth of our method improved from 1.38 times to 6.19 times compared with reading the dataset from Lustre directly using Chainer standard class. Moreover, evaluation of data parallel training showed that our method improved the performance from 1.26 times to 1.74 times for the same comparison.
A Conceptual Model for a Metric Based Framework for the Monitoring of Information Security Tasks’ Efficiency

Information Security Governance Systems are not adequate to measure the effectiveness and efficiency of security tasks for the enterprises. Although some of the systems offer ways for measurement, they still need the definition of measurement objectives and metrics. This study proposes a conceptual framework mode which has human and tool/process related metrics. This system also allows the collection of evidence data for security-related tasks and ways to motivate the security staff to provide a more productive environment. This system may be applied to any size of enterprise independent of its business domain or functions as long as the aim is to improve the effectiveness and efficiency of security-related tasks.
Concept Preserving Hashing for Semantic Image Retrieval With Concept Drift

Current hashing-based image retrieval methods mostly assume that the database of images is static. However, this assumption is not true in cases where the databases are constantly updated (e.g., on the Internet) and there exists the problem of concept drift. The online (also known as incremental) hashing methods have been proposed recently for image retrieval where the database is not static. However, they have not considered the concept drift problem. Moreover, they update hash functions dynamically by generating new hash codes for all accumulated data over time which is clearly uneconomical. In order to solve these two problems, concept preserving hashing (CPH) is proposed. In contrast to the existing methods, CPH preserves the original concept, that is, the set of hash codes representing a concept is preserved over time, by learning a new set of hash functions to yield the same set of hash codes for images (old and new) of a concept. The objective function of CPH learning consists of three components: 1) isomorphic similarity; 2) hash codes partition balancing; and 3) heterogeneous similarity fitness. The experimental results on 11 concept drift scenarios show that CPH yields better retrieval precisions than the existing methods and does not need to update hash codes of previously stored images.
Bayesian Reconstruction of Past Land Cover From Pollen Data: Model Robustness and Sensitivity to Auxiliary Variables

Abstract Realistic depictions of past land cover are needed to investigate prehistoric environmental changes, effects of anthropogenic deforestation, and long‐term land cover‐climate feedbacks. Observation‐based reconstructions of past land cover are rare, and commonly used model‐based reconstructions exhibit considerable differences. Recently, Pirzamanbein et al. (2018, 10.1016/j.spasta.2018.03.005 , Spatial Statistics, 24:14–31) developed a statistical interpolation method that produces spatially complete reconstructions of past land cover from pollen assemblage. These reconstructions incorporate a number of auxiliary data sets raising questions regarding the method's sensitivity to different auxiliary data sets. Here the sensitivity of the method is examined by performing spatial reconstructions for northern Europe during three time periods (1900 CE, 1725 CE, and 4000 BCE). The auxiliary data sets considered include the most commonly utilized sources of past land cover data—for example, estimates produced by a dynamic vegetation model and anthropogenic land cover change models. Five different auxiliary data sets were considered, including different climate data driving the dynamic vegetation model and different anthropogenic land cover change models. The resulting reconstructions were evaluated using cross validation for all the time periods. For the recent time period, 1900 CE, the different land cover reconstructions were also compared against a present day forest map. The validation confirms that the statistical model provides a robust spatial interpolation tool with low sensitivity to differences in auxiliary data and high capacity to capture information in the pollen‐based proxy data. Further auxiliary data with high spatial detail improves model performance for areas with complex topography or few observations.
The comparative African regional economics of globalization in financial allocation efficiency: the pre-crisis era revisited

Abstract This study assesses the role of globalization-fueled regionalization policies on the financial allocation efficiency of four economic and monetary regions in Africa from 1980 to 2008. Banking and financial system efficiency proxies are used as dependent variables and seven bundled and unbundled globalization variables are employed as independent indicators. The bundling is achieved by principal component analysis, while the empirical evidence is based on interactive fixed effects regressions. The findings are as follows. First, financial allocation efficiency is more sensitive to financial openness compared to trade openness and most sensitive to globalization. The relationship between allocation efficiency and globalization-fueled regionalization policies is defined by: (i) a Kuznets or inverted U-shaped curve in the UEMOA and CEMAC zones (evidence of decreasing returns for allocation efficiency from globalization-fueled regionalization) and (ii) a U-shaped relationship overwhelmingly in the COMESA and scantily in the EAC (increasing returns to allocation efficiency due to globalization-fueled regionalization). These relationships are relevant to the specific globalization dynamics within regions. Economic and monetary regions are more prone to surplus liquidity than pure economic regions are. Policy implications and measures for reducing surplus liquidity are also discussed.
How harmful are online firestorms for brands?

Purpose Adopting an exploratory approach, this paper aims to focus on the potential negative consequences that online firestorms (OFs) might have on consumer–brand relationships. Specifically, the authors focus on the individual level through taking a close look at the content that users generate during these attacks. Design/methodology/approach The authors conducted content analysis to study four recent brand-related OFs that occurred on Twitter. Findings The results show that brands are at the core of the users’ conversations, although other actors, such as competing firms, can also be affected. Negative comments greatly exceed positive ones. Actions against the brand, both passive (avoidance) and active (vengeance), emerged during the OFs. Research limitations/implications The exploratory nature of the study could cast doubt on the generalizability of the results. Moreover, the number of OF analyzed is limited, although they represent an interesting variety of brand misconducts. Practical implications Nowadays, brands are publicly scrutinized through social networks, as the networks enable users to speak out about brands’ perceived mistakes and wrongdoings. This paper confirms that managers should monitor, understand and try to respond to OFs to minimize their impact. Originality/value Online firestorms are a recent phenomenon that has gained attention finally, as they can reach hundreds of users in real-time and can involve a huge amount of comments posted online against a brand. These attacks could severely damage the brand, even when there is no strong evidence of the posted content being true. This paper adds to the scarce literature on the topic and analyzes the negative effects for brands.
Multi-document Summarization Using Adaptive Composite Differential Evolution

In the current paper, a system for multi-document summarization (MLDS) is developed which simultaneously optimized different quality measures to obtain a good summary. These measures include anti-redundancy, coverage, and, readability. For optimization, multi-objective binary differential evolution (MBDE) is utilized which is an evolutionary algorithm. MBDE consists of a set of solutions and each solution represents a subset of sentences to be selected in the summary. Generally, MBDE uses a single DE variant, but, here, an ensemble of two different DE variants measuring diversity among solutions and convergence towards the global optimal solution, respectively, is employed for efficient search. Three versions of the proposed model are developed varying the syntactic/semantic similarity between sentences and DE parameters selection strategy. Results are evaluated on the standard DUC datasets using ROUGE-2 measure and significant improvements of $$15.5\%$$ and $$4.56\%$$ are attained by the proposed approach over the two exiting techniques.
The Role of Environment Assertions in Requirements-Based Testing

Software developers dedicate a major portion of their development effort towards testing and quality assurance (QA) activities, especially during and around the implementation phase. Nevertheless, we continue to see an alarmingly increasing trend in the cost and consequences of software failure. In an attempt to mitigate such loss and address software issues at a much earlier stage, researchers have recently emphasized on the successful coordination of requirements engineering and testing. In addition, the notion of requirements-based testing (RBT) has also emerged with a focus on checking the correctness, completeness, unambiguity, and logical consistency of requirements. One seminal work points out that requirements reside in the environment which is comprised of certain problem domain phenomena. Environmental assertions, which connect some of these phenomena in the indicative mood, play a key role in deciding whether a software solution is acceptable. Despite that requirements are located in the environment, little is known about if and how the environment assertions would impact testing and QA activities. In order to address this gap, we present a detailed empirical study, with 114 developers, on the prominence of environment assertions in RBT. Although the results suggest that paying attention to correct, complete, and useful environment assertions has a positive impact on RBT, developers often face difficulty in formulating good assertions from scratch. Our work, to that end, illuminates the potential usefulness of automated support in generating environment assertions.
Disease Prediction and Early Intervention System Based on Symptom Similarity Analysis

With the development of computer technology, the electronic of medical data has become a reality.Now, how to analyze the data sufficiently to predict patient's disease and conduct early intervention has become a focused research direction.The patient's intuitive expression of feelings is also an aspect that cannot be ignored.Doctors record the pathological characteristics of patients in system.In the paper, we proposed a sentence similarity model to carry out symptom similarity analysis to achieve elementary disease prediction and early intervention, which makes use of word embedding and convolutional neural network (CNN) to extract a sentence vector that contains keyword information about the patient's feelings and symptoms.In order to increase the accuracy of sentence similarity computation, this model integrated syntactic tree and neural network into the computation process.Our main innovation is to use symptom similarity analysis model for disease prediction and early intervention.In addition, the SPO kernel is also one of the innovations.Finally, the results of experiment on Microsoft research paraphrase identification (MSRP) indicated that our model can achieve an excellent performance reached 83.9% in the terms of F1 and accuracy.Furthermore, we also conducted experiments on the data of the Semantic Textual Similarity task.Pearson correlation coefficient indicates that our result is closer to the gold standard scores, which illustrates that it can extract the key information of sentence well to realize the prediction of disease and carry out early intervention.
RE4CPS: Requirements Engineering for Cyber-Physical Systems

Cyber-Physical Systems (CPSs) connect the cyber world with the physical world through a network of interrelated elements, such as sensors and actuators, robots, and other computing devices. There are increasing number of beneficial applications in dependable sectors such as aviation, transportation, aerospace, healthcare, etc.. The inherent characteristics of CPSs pose a number of challenges to requirements engineering. Unlike normal information systems, CPSs need to continuously detect and adapt to the environment changes. The interactive environment becomes the first-class citizen because the features and the changing patterns in environment are must-to-be considered. Moreover, in such systems, many non-functional requirements are environment related, like timing, safety, security, and privacy requirements. This tutorial will introduce an environment modelling based approach to engineering the requirements of CPSs. Extending the Problem Frames representations, this approach structures the model of the environmental elements and provides analysis methods for deriving and specifying requirements. We deliver this tutorial with a few supporting tools that assist the modelling and verification of the system specification, demonstrated with working examples in sufficient details. After the tutorial, participants will be able to work on the environment modelling requirements engineering for their own projects, with some hands-on experience and a good knowledge of some tool support.
Face Recognition based on Illumination Invariant Techniques Model

In modern years, face recognition is becoming popular in many applications in different areas being video conferencing, security, banking, law requirement, and human-computer interaction. The performance of the face verification system depends on many challenges. The Most challenge in face recognition is illumination variations. In this approach, Median Filter, Gabor Filter, and Histogram Equalization are used to reduce the illumination effect of the face images as the preprocessing stage. The extraction of the Eigen faces features use Principal Component Analysis (PCA). After that, recognize face by using multiclass Support Vector Machines. The standard databases of ORL and Yale are used in the experiment. The results show that the system efficiently increased the accuracy of face recognition rate of the system, especially in various lighting situations.
Winning the RoboCup Logistics League with Fast Navigation, Precise Manipulation, and Robust Goal Reasoning

The RoboCup Logistics League is a robotics competition in a Smart Factory scenario in which a team of robots has to assemble products for dynamically generated orders. In 2019, the Carologistics was able to win the competition with a redesigned manipulation system, improved navigation, and an incremental and distributed goal reasoning system. In this paper, we describe the major components of our approach that enabled us to win the competition, with a particular focus on this year's changes.
GGNN: Graph-Based GPU Nearest Neighbor Search

Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory representations. Since PQT, FAISS, and SONG started to leverage the massive parallelism offered by GPUs, GPU-based implementations are a crucial resource for today's state-of-the-art ANN methods. While most of these methods allow for faster queries, less emphasis is devoted to accelerating the construction of the underlying index structures. In this paper, we propose a novel GPU-friendly search structure based on nearest neighbor graphs and information propagation on graphs. Our method is designed to take advantage of GPU architectures to accelerate the hierarchical construction of the index structure and for performing the query. Empirical evaluation shows that GGNN significantly surpasses the state-of-the-art CPU- and GPU-based systems in terms of build-time, accuracy and search speed.
Efficiency and Scalability of Multi-lane Capsule Networks (MLCN)

Some Deep Neural Networks (DNN) have what we call lanes, or they can be reorganized as such. Lanes are paths in the network which are data-independent and typically learn different features or add resilience to the network. Given their data-independence, lanes are amenable for parallel processing. The Multi-lane CapsNet (MLCN) is a proposed reorganization of the Capsule Network which is shown to achieve better accuracy while bringing highly-parallel lanes. However, the efficiency and scalability of MLCN had not been systematically examined. In this work, we study the MLCN network with multiple GPUs finding that it is 2x more efficient than the original CapsNet when using model-parallelism. Further, we present the load balancing problem of distributing heterogeneous lanes in homogeneous or heterogeneous accelerators and show that a simple greedy heuristic can be almost 50% faster than a naïve random approach.
Mobile Data Collection Using Open Data Kit

In an era where mobile devices are spreading around the world and becoming cheaper, many organizations are turning to mobile data collection. Open Data Kit (ODK) is one of the most well known mobile data collection framework. ODK has two suites of software: ODK and ODK-X. The purpose of this paper is to propose two architectures, one for ODK and an other for ODK-X (formely ODK 2); these architectures show data trajectories from the creation of the forms until to collected data visualization, and also help the user to choose between these two suites, the one that best fits his needs and context. In addition, this paper present a comparative study of several mobile data collection systems. This comparative study concluded that, among plenty frameworks, ODK and ODK-X are the ones that offer a complete and fully open source mobile data collection solution.
Applying Deep Learning Approach to Targeted Aspect-based Sentiment Analysis for Restaurant Domain

Sentiment analysis is a well-studied topic in social media analysis and it becomes an important decision-making tool to classify the opinion of user on the Web. In a few years ago, there are so many research works for sentiment analysis using machine learning. Nowadays, deep learning models are implemented in AI applications to gain better performance. This paper proposes to extend the traditional LSTM approach by adding the external knowledge. LSTM is a kind of recurrent neural network (RNN) and it can learn the past and future information. Unlike traditional RNN, it can capture the long sequence of text. With the LSTM cell, SenticNet is used as an external knowledge, to improve the accuracy in classification. We termed SenticNet multi-attentive LSTM (MA-LSTM). The results from the SenticNet MA-LSTM are classified as positive, negative or neutral and finally, compare the results with other state-of-the-art LSTM methods: standard LSTM, TD-LSTM, TC-LSTM, AE-LSTM, and ATAE-LSTM. To our knowledge, this is the first work for using multi-attention LSTM with external knowledge in targeted aspect-based sentiment analysis task.
Horizon line detection using supervised learning and edge cues

Traditionally, edge detection has been extensively employed as the basic step for the horizon line detection problem. However, generally such methods do not discriminate between edges belonging to horizon boundary and others due to clouds or other natural phenomenon. Additionally, most edge based methods suffer more in the presence of edge gaps. To address these issues, we propose an edge-less horizon line detection approach based on pixel classification, hence not relying on edge information. The key idea is formulating a multi-stage graph using classification maps, instead of edge maps, where each node cost reflects the likelihood of pixel belonging to the horizon boundary. The shortest path is found in the formulated multi-stage graph using dynamic programming which conforms to the detected horizon line. We demonstrate the performance of the proposed approach on two challenging data sets and provide comparisons with two edge-based methods: one relying on edge detection while the other based on edge classification. Overall, the proposed approach achieves comparable performance against carefully crafted edge based formulations. A by-product of the edge-less approach is its capability of associating a confidence level with the found solution, which can be used to confirm the presence or absence of a horizon line in a given image. The method is also capable of dealing with partial horizon line in an image. To further improve the detection performance, we propose a fusion strategy which combines both edge-based and edge-less information. Extensive evaluations, including a publicly available data set, illustrate the superiority of the proposed fusion approach.
Reversible Data Hiding Scheme Based on Difference Expansion Using Shiftable Block Strategy for Enhancing Image Fidelity

The difference expansion (DE) proposed by the Tian is one of the most famous methods of reversible information hiding. This method uses the difference between pixels to embed secret information into the image and can restore the stego-image to the original image, to reach a high embedding capacity and keep low distortion. However, in applications using multi-layer embedding, difference expansion may cause the image quality to deteriorate drastically. In this paper, we proposed a multi-layer shiftable block strategy to modify the block partitioning to hide the secret message. This is done to prevent the data from being hidden in the same block of consecutive layers. Therefore, image distortion is not amplified by the pixel difference of the previous layer, causing a recursive effect by hiding the secret message in the latter layer; thus enhancing image fidelity. The experimental results show an improvement in the image quality of the proposed method.