Reviewerly/exHarmony huggingface dataset:
Towards a knowledge base to support global change policy goals

In 2015, it was adopted the 2030 Agenda for Sustainable Development to end poverty, protect the planet and ensure that all people enjoy peace and prosperity. The year after, 17 Sustainable Development Goals (SDGs) officially came into force. In 2015, GEO (Group on Earth Observation) declared to support the implementation of SDGs. The GEO Global Earth Observation System of Systems (GEOSS) required a change of paradigm, moving from a data-centric approach to a more knowledge-driven one. To this end, the GEO System-of-Systems (SoS) framework may refer to the well-known Data-Information-Knowledge-Wisdom (DIKW) paradigm. In the context of an Earth Observation (EO) SoS, a set of main elements are recognized as connecting links for generating knowledge from EO and non-EO data – e.g. social and economic datasets. These elements are: Essential Variables (EVs), Indicators and Indexes, Goals and Targets. Their generation and use requires the development of a SoS KB whose management process has evolved the GEOSS Software Ecosystem into a GEOSS Social Ecosystem. This includes: collect, formalize, publish, access, use, and update knowledge. ConnectinGEO project analysed the knowledge necessary to recognize, formalize, access, and use EVs. The analysis recognized GEOSS gaps providing recommendations on supporting global decision-making within and across different domains.
Singing Voice Synthesis Based on Generative Adversarial Networks

This paper proposes a generative adversarial training method for deep neural network (DNN)-based singing voice synthesis. The DNN-based approach has been used in statistical parametric singing voice synthesis and improved the naturalness of the synthesized singing voice [1]. Recently, generative adversarial networks (GANs) [2] have attracted significant attention in various machine learning research areas including speech synthesis [3]. GANs have achieved great success in modeling the distributions of complex data, and they have the potential to alleviate over-smoothing problem on the generated speech parameters in speech synthesis. In this paper, we propose a DNN-based singing voice synthesis system incorporating the GAN. Experimental results show that the proposed method outperforms the conventional method in the naturalness of the synthesized singing voice.
Put Your Warning Where Your Link Is

Phishing emails often disguise a link's actual URL. Thus, common anti-phishing advice is to check a link's URL before clicking, but email clients do not support this well. Automated phishing detection enables email clients to warn users that an email is suspicious, but current warnings are often not specific. We evaluated the effects on phishing susceptibility of (1) moving phishing warnings close to the suspicious link in the email, (2) displaying the warning on hover interactions with the link, and (3) forcing attention to the warning by deactivating the original link, forcing users to click the URL in the warning. We assessed the effectiveness of such link-focused phishing warning designs in a between-subjects online experiment (n=701). We found that link-focused phishing warnings reduced phishing click-through rates compared to email banner warnings; forced attention warnings were most effective. We discuss the implications of our findings for phishing warning design.
Weak efficiency of the cryptocurrency market: a market portfolio approach

Cryptocurrencies have attracted the attention of many investors and policymakers given the increase in popularity of Bitcoin. In this context, we analyse the cryptocurrency market by means of cap-weighted and equally weighted market portfolios that include all the altcoins available for three different periods (2015–2017, 2016–2017 and 2017). By using the most traditional tests of efficiency, we observe three main features of the cryptocurrency market: it is weak-form inefficient due to the behaviour of all the altcoins, it is more inefficient over time, especially in 2017, and the creation of new cryptocurrencies has not significantly changed the efficiency of the market.
Converging Blockchain and Machine Learning for Healthcare

The power of machine learning in understanding the patterns in data, analyzing and making decisions, has shown its importance in various sectors. Machine Learning requires reasonable amount of data to make accurate decisions. Data sharing and reliability of data is very crucial in machine learning in order to improve its accuracy. The decentralized database in Blockchain Technology emphasizes on data sharing. The consensus in Blockchain technology makes sure that data is legitimate and secured. The convergence of these two technologies can give highly accurate results in terms of machine learning with the security and reliability of Blockchain Technology. This paper gives an overview of how combining these two technologies can help in healthcare sectors.
Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology.
Fast Deep Neural Network Training on Distributed Systems and Cloud TPUs

Since its creation, the ImageNet-1k benchmark set has played a significant role as a benchmark for ascertaining the accuracy of different deep neural net (DNN) models on the image classification problem. Moreover, in recent years it has also served as the principal benchmark for assessing different approaches to DNN training. Finishing a 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU takes 14 days. This training requires 1018 single precision operations in total. On the other hand, the world's current fastest supercomputer can finish 3 x 1017 single precision operations per second (according to the Nov 2018 Top 500 results). If we can make full use of the computing capability of the fastest supercomputer, we should be able to finish the training in several seconds. Over the last two years, researchers have focused on closing this significant performance gap through scaling DNN training to larger numbers of processors. Most successful approaches to scaling ImageNet training have used the synchronous minibatch stochastic gradient descent (SGD). However, to scale synchronous SGD one must also increase the batch size used in each iteration. Thus, for many researchers, the focus on scaling DNN training has translated into a focus on developing training algorithms that enable increasing the batch size in data-parallel synchronous SGD without losing accuracy over a fixed number of epochs. In this paper, we investigate supercomputers' capability of speeding up DNN training. Our approach is to use a large batch size, powered by the Layer-wise Adaptive Rate Scaling (LARS) algorithm, for efficient usage of massive computing resources. Our approach is generic, as we empirically evaluate the effectiveness on five neural networks: AlexNet, AlexNet-BN, GNMT, ResNet-50, and ResNet-50-v2 trained with large datasets while preserving the state-of-the-art test accuracy. Compared to the baseline of a previous study from Goyal et al. [1], our approach shows higher test accuracy on batch sizes that are larger than 16K.When we use the same baseline, our results are better than Goyal et al. for all the batch sizes (Fig. 20). Using 2,048 Intel Xeon Platinum 8160 processors, we reduce the 100-epoch AlexNet training time from hours to 11 minutes. With 2,048 Intel Xeon Phi 7250 Processors, we reduce the 90-epoch ResNet-50 training time from hours to 20 minutes. Our implementation is open source and has been released in the Intel distribution of Caffe, Facebook's PyTorch, and Google's TensorFlow. The difference between this paper and the conference-version of our work [2] includes: (1) we implement our approach on Google's cloud Tensor Processing Unit (TPU) platform, which verifies our previous success on CPUs and GPUs. (2) we scale the batch size of ResNet-50-v2 to 32K and achieve 76.3 percent accuracy, which is better than the 75.3 percent accuracy achieved in our conference paper. (3) we apply our approach to Google's Neural Machine Translation (GNMT) application, which helps us to achieves 4x speedup on the cloud TPUs.
Entropy-based Sampling Approaches for Multi-Class Imbalanced Problems

In data mining, large differences between multi-class distributions regarded as class imbalance issues have been known to hinder the classification performance. Unfortunately, existing sampling methods have shown their deficiencies such as causing the problems of over-generation and over-lapping by oversampling techniques, or the excessive loss of significant information by undersampling techniques. This paper presents three proposed sampling approaches for imbalanced learning: the first one is the entropy-based oversampling (EOS) approach; the second one is the entropy-based undersampling (EUS) approach; the third one is the entropy-based hybrid sampling (EHS) approach combined by both oversampling and undersampling approaches. These three approaches are based on a new class imbalance metric, termed entropy-based imbalance degree (EID), considering the differences of information contents between classes instead of traditional imbalance-ratio. Specifically, to balance a data set after evaluating the information influence degree of each instance, EOS generates new instances around difficult-to-learn instances and only remains the informative ones. EUS removes easy-to-learn instances. While EHS can do both simultaneously. Finally, we use all the generated and remaining instances to train several classifiers. Extensive experiments over synthetic and real-world data sets demonstrate the effectiveness of our approaches.
GAN is a friend or foe?

Creating fake images such as replacing one's face with other person's face has become much easier due to the advancement of sophisticated image editing tools. In addition, Generative Adversarial Networks (GANs) enable creating natural looking human faces. However, fake images can cause many potential problems, as they can be misused to abuse information, hurt people, and generate fake identification. Therefore, detecting fake face images is critical for protecting individuals from various misuses. In this work, we propose an image forensic platform using neural networks, FakeFaceDetect, to detect various fake face images. In particular, we focus on detecting fake images automatically created from GANs as well as manually created by humans. In addition, we assume a strong adversary who can arbitrarily change and remove metadata of the original images. We demonstrate that FakeFaceDetect achieves high accuracy in detecting fake face images created by humans and GANs.
Machine Learning Systems are Stuck in a Rut

In this paper we argue that systems for numerical computing are stuck in a local basin of performance and programmability. Systems researchers are doing an excellent job improving the performance of 5-year-old benchmarks, but gradually making it harder to explore innovative machine learning research ideas.
Human or Robot? Consumer Responses to Radical Cognitive Enhancement Products

Human enhancement products allow consumers to radically enhance their mental abilities. Focusing on cognitive enhancements, we introduce and study a novel factor—dehumanization (i.e., denying a person emotional ability and likening them to a robot)—which plays a key role in consumers’ reluctance to use enhancement products. In study 1, consumers who enhance their mental abilities beyond normal levels were dehumanized, whereas consumers who use the same products to restore lost abilities were not. Moreover, dehumanization decreased prospective consumers’ interest in using the enhancement products themselves. Study 2 shows that emphasizing how the motivation to use an enhancement product can be prosocial (i.e., helping other people) inoculates the consumer against dehumanization, and study 3 supports this positioning strategy in an online advertising campaign. Together, these studies uncover dehumanization as an important obstacle to consumer adoption of enhancement products and demonstrate how to overcome this obstacle with a prosocial positioning strategy.
A Novel Geometric Hierarchical Approach for Dynamic Visual Servoing of Quadrotors

This paper proposes a novel nonlinear geometric hierarchical dynamic visual servoing approach to drive a quadrotor to the desired pose defined by a previously captured image of a planar target. Different from existing works, the key novelty is to extend the positionbased nonlinear hierarchical control to image-based nonlinear hierarchical control. More specifically, by seamlessly integrating the nonlinear hierarchical control with the geometric control, and taking full advantage of the cascade property of the system, the proposed visual servoing strategy does not require the thrust force or its derivative to be measurable when compared with the existing backstepping methods, which brings much convenience for practical applications. For the attitude loop, the axis-angle rotation representation is adopted to design a tracking control law on the vector space s (3). In the outer loop, perspective image moments in the virtual image plane are employed as image feedback to construct the outer-loop image-based visual servoing controller with geometric control and backstepping techniques. Based on Lyapunov techniques and the theory of cascade systems, it is rigorously proven that the proposed image-based controller achieves asymptotic stability. Comparative experiments are conducted to show that the proposed approach has advantages of better transient performance, better steady-state performance, and stronger robustness.
Detect Me If You Can: Spam Bot Detection Using Inductive Representation Learning

Spam Bots have become a threat to online social networks with their malicious behavior, posting misinformation messages and influencing online platforms to fulfill their motives. As spam bots have become more advanced over time, creating algorithms to identify bots remains an open challenge. Learning low-dimensional embeddings for nodes in graph structured data has proven to be useful in various domains. In this paper, we propose a model based on graph convolutional neural networks (GCNN) for spam bot detection. Our hypothesis is that to better detect spam bots, in addition to defining a features set, the social graph must also be taken into consideration. GCNNs are able to leverage both the features of a node and aggregate the features of a node’s neighborhood. We compare our approach, with two methods that work solely on a features set and on the structure of the graph. To our knowledge, this work is the first attempt of using graph convolutional neural networks in spam bot detection.
IOT Security Issues Via Blockchain

In the past few years block chain has gained lot of popularity because blockchain is the core technology of bitcoin. Its utilization cases are growing in number of fields such as security of Internet of Things (IoT), banking sector, industries and medical centres. Moreover, IoT has expanded its acceptance because of its deployment in smart homes and city developments round the world. Unfortunately, IoT network devices operate on limited computing power with low storage capacity and network bandwidth. Thus, they are extra close to attacks than other end-point devices such as cell phones, tablets, or PCs. This paper focus on addressing significant security issues of IoT and maps IoT security issues in contradiction of existing solutions found in the literature. Moreover issues that are not solved after implementation of blockchain are highlighted.
Real-Time Kinematic Control for Redundant Manipulators in a Time-Varying Environment: Multiple-Dynamic Obstacle Avoidance and Fast Tracking of a Moving Object

This paper presents a real-time kinematic control strategy to realize fast tracking of redundant robot manipulators in a time-varying environment. An obstacle avoidance method based on the law of conservation of energy is proposed to adjust the motion states of robot manipulators in real time. This method defines that the total energy for the end effector consists of an energy toward object (ETO) and an energy around obstacle (EAO), and that the total energy for each critical point on manipulator composes a relative kinematic energy (RKE) and an energy memory (EM). The total energies remain constant at each sampling period, and the conversions between the ETO and the EAO or between the RKE and the EM are recognized to obey a distance-related S-function. Such considerations ensure the smooth movement of the manipulator and avoid collisions with obstacles. In real-time planning, an unsupervised single neuron PID model is raised to adaptively increase the convergence ratio of moving object tracking via the online learning of the principal component analysis. Then, combined with the dynamic obstacle avoidance method based on conservation of energy, the kinematic control strategy is established for redundant manipulators to track a moving object rapidly in the presence of multiple dynamic obstacles. Theory analysis and various contrast experimental results show that the proposed kinematic control strategy is feasible and has fast convergence.
A High-Imperceptibility and Histogram-Shifting Data Hiding Scheme for JPEG Images

Data hiding technology plays an important role in many areas related to people's life, such as military and medical images.However, it is difficult to obtain high embedding capacity in compressed images, and it may cause obvious image distortion.Also, the target image's file size should be controlled, as a significant increase in file size may cause the interceptor's attention.In the field of data hiding, many people have proposed feasible solutions.However, considering the visual quality and embedding rate of images, more improvements are needed.In this paper, the histogram shift is used to realize data hiding of JPEG images.The secret message bits are embedded in the high-frequency coefficients to ensure a higher embedding rate, and the high-frequency coefficients are obtained by histogram distribution.The optimal threshold is used to select a discrete cosine transform (DCT) coefficient sub-block that is suitable to embed the secret message and further improve the visual quality of the target image.The experiments show that our solution is significantly better than the most advanced technology in terms of embedding rate and visual quality.
Toward a Theory of Information Systems Security Behaviors of Organizational Employees: A Dialectical Process Perspective

Practice-/policy-oriented abstract: Understanding why employees do or do not comply with information systems security (ISS) procedures is an imperative in today’s organizations whose futures often depend on how well they protect and harness information assets. We use a predominantly inductive approach to develop a theoretical understanding of how employees’ reasons for engaging to ISS behaviors (ISSBs) change over time, using ideas from dialectics as our scaffolding. Our dialectical view of this process suggests that explanations for engaging in different ISSBs change over time as individuals seek to balance contradictory demands. Furthermore, our view suggests that new experiences and external events initiate a process of reevaluating tensions that can in turn lead to behavioral changes. Based on our observations and interpretation, we conclude that it is beneficial for ISS practitioners not only to be aware of factors that seem to promote compliance with ISSPs, but also to pay attention to the importance of opposing values and pressures, triggers, and ways to resolve the existing tensions related to an individual’s ISSBs. This will likely enable awareness about the dilemmas that individuals confront in ISS-related situations and potentially contribute to the design of better controls and procedures for ensuring compliant ISSBs.
High Capacity Reversible Data Hiding Based on Multiple Histograms Modification

Among various techniques of reversible data hiding (RDH), histogram modification has been mostly investigated and used in practice. Recently, a new histogram modification technique, namely, multiple histograms modification (MHM) has been proposed and received the increasing attentions. By MHM, multiple histograms can be modified differently to achieve an excellent embedding performance. However, as the maximum modification on each pixel value is one, only one pair of bins in the prediction-error histogram (PEH) is allowed to be expanded for data embedding. This drawback leads to a low capacity and limits its extension for high capacity scenario. To remedy this, we extend the MHM scheme and propose an efficient solution for the high-capacity embedding. Specifically, instead of only selecting one pair of expansion bins in each PEH, in the proposed method, multiple pairs of bins are utilized for expansion in each PEH. Moreover, the embedding performance is optimized by an advisable expansion bin selection strategy. Consequently, the multiple PEHs are allowed to embed with different capacities according to the sharpness of distribution, i.e., the low-index PEH that consists of more smooth pixels is embedded with a larger capacity than the high-index one. Besides, a simplified parameter determination is developed to seek the optimal solution with a good computational efficiency. The experimental results show that the proposed method can achieve the high capacity and provide a better performance than some state-of-the-art RDH methods.
Deep Transfer Bug Localization

Many projects often receive more bug reports than what they can handle. To help debug and close bug reports, a number of bug localization techniques have been proposed. These techniques analyze a bug report and return a ranked list of potentially buggy source code files. Recent development on bug localization has resulted in the construction of effective supervised approaches that use historical data of manually localized bugs to boost performance. Unfortunately, as highlighted by Zimmermann et al., sufficient bug data is often unavailable for many projects and companies. This raises the need for cross-project bug localization - the use of data from a project to help locate bugs in another project. To fill this need, we propose a deep transfer learning approach for cross-project bug localization. Our proposed approach named TRANP-CNN extracts transferable semantic features from source project and fully exploits labeled data from target project for effective cross-project bug localization. We have evaluated TRANP-CNN on curated high-quality bug datasets and our experimental results show that TRANP-CNN can locate buggy files correctly at top 1, top 5, and top 10 positions for 29.9, 51.7, 61.3 percent of the bugs respectively, which significantly outperform state-of-the-art bug localization solution based on deep learning and several other advanced alternative solutions considering various standard evaluation metrics.