Reviewerly/exHarmony huggingface dataset:
Evaluating multi-label classifiers and recommender systems in the financial service sector

The objective of this paper is to evaluate multi-label classification techniques and recommender systems for cross-sell purposes in the financial services sector. We carried out three analyses using data obtained from an international financial services provider. First, we tested four multi-label classification techniques, of which the two problem transformation methods were combined with several base classifiers. Second, we benchmarked the performance of five state-of-the-art recommender approaches. Third, we compared the best performing multi-label classification and recommender approaches with each other. The results identify user-based collaborative filtering as the top performing recommender system, with a cross-validated F1 measure of 42.20% and G-mean of 42.64%. Classifier chains binary relevance with adaboost and binary relevance with random forest are the top performing multi-label classification algorithms for respectively F1 measure and G-mean, yielding a cross-validated F1 measure of 53.33% and G-mean of 54.37%. The statistical comparison between the best performing approaches confirms the superiority of multi-label classification techniques. Our study provides important recommendations for financial services providers, who are interested in the most effective methods to determine cross-sell opportunities. In previous studies, multi-label classification techniques and recommender systems were always investigated independently of each other. To the best of our knowledge, our study is therefore the first to compare both techniques in the financial services sector.
Short-term forecasting of solar irradiance

Five statistical models for nowcasting solar irradiance are evaluated from different perspectives. The first four models are purely statistical ones: random walk, moving average, exponential smoothing and autoregressive integrated moving average. These models can be considered as benchmarks of different levels of complexity. The fifth model is a version of the two-state model, an applications suite for nowcasting solar irradiance developed by our team. The two-state model connects in an innovative manner an empirical estimator for clear-sky solar irradiance with a statistical predictor for the sunshine number, a binary indicator stating whether the sun shines or not. On the basis of different error metrics, the models' performances are analyzed from four perspectives: forecast accuracy, forecast precision, data series granularity and variability in data series. The study is conducted with high-quality radiometric data measured at a high frequency of four samples per minute on the Solar Platform of the West University of Timisoara, Romania. No model is ranked as the best, but the peculiarities that cause a model to perform better than others are discussed. By processing information about the atmospheric transmittance, the two-state model proves a slight advance in the forecast accuracy and a notable performance in the forecast precision.
Employee profiling via aspect-based sentiment and network for insider threats detection

Historically, the harm caused by insiders has proven to be one of the greatest concerns for any organization. As such, it has received considerable attention from both the industrial and research communities. Existing works mainly focused on modeling the employees’ normal biometric behavior (e.g., human to device interaction pattern) to detect anomalous behavior which corresponds to the insider activity. However, it is unattainable to stop the insider at the final moment when the malicious act is being carried out. In this paper, we propose a novel framework which performs employee profiling based on aspect-based sentiments and social network information and examine its applicability for early detection of potential insider threats. On the contrary to the traditional sentiment analysis, aspect-based sentiment analysis provides more fine-grained information on the employee. Our framework employs a combination of deep learning techniques such as Gated Recurrent Unit (GRU) and skipgram to build temporal sentiment profiles for the employees. It then performs anomaly detection on the profiles and ranks the employees based on their respective anomaly score. Due to the absence of relevant benchmark dataset, we augmented the publicly available real-world Enron email corpus with an insider threat scenario to evaluate our framework. The evaluation results demonstrate that the augmentation is indeed reflected in the augmented employee’s anomaly ranking (i.e., from normal to abnormal) and her close associates are indeed placed closely to her when the profiles are visualized in the 2D space. The profiles obtained from our framework can also be used to complement any existing expert and intelligent systems with additional capabilities in handling textual information such as, integration with profiles obtained from biometric behavior to form a more comprehensive threat detection system.
Forecasting cryptocurrency prices time series using machine learning approach

This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&amp;RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).
The performance of ELM based ridge regression via the regularization parameters

The extreme learning machine (ELM) which is a single layer feedforward neural network provides extremely fast training speed and good generalization performance. The ELM however, has its respective drawback: it is known to be sensitive to the ill-conditioned data. To overcome the ill-conditioning problem in ELM, ELM based on ridge regression (RR-ELM) was proposed. Since RR-ELM is a biased method, ELM based on almost unbiased ridge regression (AUR-ELM) was accordingly proposed to reduce the bias in a certain extent. RR-ELM and AUR-ELM introduced in the existence of multicollinearity, depend on the regularization parameter. The regularization parameter affects the performance of both RR-ELM and AUR-ELM. There is no consensus on the selection of the regularization parameter. Although there are various methods in linear regression to select the regularization parameter, only one method based on the selection minimizing the mean squared error was used in RR-ELM. In this study, AIC, BIC and CV criteria in the context of RR-ELM and AUR-ELM were proposed as alternative methods for the selection of the regularization parameter. An experimental study was conducted on eight data sets which are widely known and used in machine learning studies. The analyzes are considered as purposive for regression studies which are the most important fields of expert systems and machine learning. The results obtained demonstrate that the selection method of the regularization parameter is significantly effective on both the generalization and particularly stability performance of RR-ELM and AUR-ELM when compared to ELM
Vision Based Hand Gesture Recognition Using 3D Shape Context

Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient. The representation of hand gestures is critical for recognition. In this paper, we propose a new method to measure the similarity between hand gestures and exploit it for hand gesture recognition. The depth maps of hand gestures captured via the Kinect sensors are used in our method, where the 3D hand shapes can be segmented from the cluttered backgrounds. To extract the pattern of salient 3D shape features, we propose a new descriptor-3D Shape Context, for 3D hand gesture representation. The 3D Shape Context information of each 3D point is obtained in multiple scales because both local shape context and global shape distribution are necessary for recognition. The description of all the 3D points constructs the hand gesture representation, and hand gesture recognition is explored via dynamic time warping algorithm. Extensive experiments are conducted on multiple benchmark datasets. The experimental results verify that the proposed method is robust to noise, articulated variations, and rigid transformations. Our method outperforms state-of-the-art methods in the comparisons of accuracy and efficiency.
A Study on Security and Privacy Guidelines, Countermeasures, Threats: IoT Data at Rest Perspective

The Internet of Things (IoT) makes our lives much easier, more valuable, and less stressful due to the development of many applications around us including smart cities, smart cars, and smart grids, offering endless services and solutions. Protecting IoT data of such applications at rest either on the objects or in the cloud is an indispensable requirement for achieving a symmetry in the handling and protection of the IoT, as we do with data created by persons and applications. This is because unauthorised access to such data may lead to harmful consequences such as linkage attacks, loss of privacy, and data manipulation. Such undesired implications may jeopardise the existence of IoT applications if protection measures are not taken, and they stem from two main factors. One is that IoT objects have limited capabilities in terms of memory capacity, battery life, and computational power that hamper the direct implementation of conventional Internet security solutions without some modifications (e.g., traditional symmetric algorithms). Another factor is the absence of widely accepted IoT security and privacy guidelines for IoT data at rest and their appropriate countermeasures, which would help IoT stakeholders (e.g., developers, manufacturers) to develop secure IoT systems and therefore enhance IoT security and privacy by design. Toward this end, we first briefly describe the main IoT security goals and identify IoT stakeholders. Moreover, we briefly discuss the most well-known data protection frameworks (e.g., General Data Protection Regulation (GDPR), Health Insurance Portability (HIPAA)). Second, we highlight potential attacks and threats against data at rest and show their violated security goals (e.g., confidentiality and integrity). Third, we review a list of protection measures by which our proposed guidelines can be accomplished. Fourth, we propose a framework of security and privacy guidelines for IoT data at rest that can be utilised to enhance IoT security and privacy by design and establish a symmetry with the protection of user-created data. Our framework also presents the link between the suggested guidelines, mitigation techniques, and attacks. Moreover, we state those IoT stakeholders (e.g., manufacturers, developers) who will benefit most from these guidelines. Finally, we suggest several open issues requiring further investigation in the future, and we also discuss the limitations of our suggested framework.
A Novel and Efficient CVAE-GAN-Based Approach With Informative Manifold for Semi-Supervised Anomaly Detection

Semi-supervised anomaly detection identifies abnormal (testing) observations which are different from normal (training) observations. In many practical situations, anomalies are poorly insufficient and not well defined, while the normal data are easily sampled, have a wide variety, and may not be classified. For this paradigm, we propose a novel end-to-end deep network as an anomaly detector only trained on normal samples. Our architecture consists of a conditional variational auto-encoder (CVAE), a feature discriminator (FD), and an adversarially trained WGAN-GP discriminator. The CVAE is designed as a generator to reconstruct images. It leverages underlying category information and multivariate Gaussian distributions to regularize the latent space, enabling a smooth and informative manifold. For anomalies which have a certain similarity to normal data, we perform active negative training by generating potential outliers from the latent space to limit network generative capability. In order to capture data characteristics, we maximize the mutual information between the inputs and the latent codes by the FD. It enhances the relationship between the high-dimensional image space and corresponding encoded vectors. To promote reconstruction, a structural similarity loss is applied to robustly recover local texture details and the WGAN-GP discriminator is employed to aid in generating photo-realistic images. We distinguish anomalies by computing a reconstruction-based anomaly score. Different from recent encoder-decoder or GAN-based architectures, our approach considers input categories, constructs, and exploits a useful manifold in an unsupervised manner and has a stronger reconstruction capability. The experimental results demonstrate that the proposed approach outperforms state-of-the-art methods over several benchmark datasets.
Entanglement spectroscopy with a depth-two quantum circuit

Noisy intermediate-scale quantum (NISQ) computers have gate errors and decoherence, limiting the depth of circuits that can be implemented on them. A strategy for NISQ algorithms is to reduce the circuit depth at the expense of increasing the qubit count. Here, we exploit this trade-off for an application called entanglement spectroscopy, where one computes the entanglement of a state $| \psi \rangle$ on systems $AB$ by evaluating the R\'enyi entropy of the reduced state $\rho_A = {\rm Tr}_B(| \psi \rangle \langle \psi |)$. For a $k$-qubit state $\rho(k)$, the R\'enyi entropy of order $n$ is computed via ${\rm Tr}(\rho(k)^{n})$, with the complexity growing exponentially in $k$ for classical computers. Johri, Steiger, and Troyer [PRB 96, 195136 (2017)] introduced a quantum algorithm that requires $n$ copies of $| \psi \rangle$ and whose depth scales linearly in $k*n$. Here, we present a quantum algorithm requiring twice the qubit resources ($2n$ copies of $| \psi \rangle$) but with a depth that is independent of both $k$ and $n$. Surprisingly this depth is only two gates. Our numerical simulations show that this short depth leads to an increased robustness to noise.
Rethinking Technologies for Behavior Change

Human-Computer Interaction researchers are increasingly designing technologies aimed at supporting “behavior change.” The model of change, which most of these works embrace, focuses on the idea that change occurs on the behavioral level and that it is externalistic, monistic, mechanistic, fragmented, and episodic. We think that a different take, focusing on the internal aspects of change, may integrate and extend what has been done using this behavioral model. We conducted 20 interviews exploring how individuals live, account for, and manage life changes. Then, we outlined five tentative patterns we identified across different kinds of changes reported by the interviewees, pointing out that change might be internalistic, multiple, intentional, holistic, and continuous. This led us to propose a set of design considerations for the evolution of the current behavior change technologies. Finally, we suggested some preliminary lines of future research, which aim to open the design space of technologies for change.
Digital factory – virtual reality environments for industrial training and maintenance

This study evaluates the use of virtual reality (VR) platforms, which is an integrated part of digital factory for industrial training and maintenance system.The digital factory based VR platform provides an intuitive and immersive human-computer interface, which can be an efficient tool for industrial training and maintenance services.The outcomes from this study suggested that use of the VR platform for training and maintenance of complex industrial tasks should be encouraged and use of the VR platform for that purpose should be further evaluated.This paper highlighted the generic concept of the application of virtual reality technique within digital factory to industrial maintenance and to build a low-cost VR application for training and maintenance system.An application case on virtual reality technique in a power plant operations and maintenance is demonstrated within the scope of this research.Overall research implications on virtual reality concept in industrial applications are concluded with future research directions.
Beyond Personalization

An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. In this paper, we propose a model called Social Explorative Attention Network (SEAN) for content recommendation. SEAN uses a personalized content recommendation model to encourage personal interests driven recommendation. Moreover, SEAN allows the personalization factors to attend to users' higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets from a popular decentralized content distribution platform, Steemit, we compare SEAN with state-of-the-art CF and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation performance.
Dual Adversarial Autoencoders for Clustering

As a powerful approach for exploratory data analysis, unsupervised clustering is a fundamental task in computer vision and pattern recognition. Many clustering algorithms have been developed, but most of them perform unsatisfactorily on the data with complex structures. Recently, Adversarial Auto-Encoder (AAE) shows effectiveness on tackling such data by combining Auto-Encoder (AE) and adversarial training, but it cannot effectively extract classification information from the unlabeled data. In this work, we propose Dual Adversarial Auto-encoder (Dual-AAE) which simultaneously maximizes the likelihood function and mutual information between observed examples and a subset of latent variables. By performing variational inference on the objective function of Dual-AAE, we derive a new reconstruction loss which can be optimized by training a pair of Auto-encoders. Moreover, to avoid mode collapse, we introduce the clustering regularization term for the category variable. Experiments on four benchmarks show that Dual-AAE achieves superior performance over state-of-the-art clustering methods. Besides, by adding a reject option, the clustering accuracy of Dual-AAE can reach that of supervised CNN algorithms. Dual-AAE can also be used for disentangling style and content of images without using supervised information.
A context sensitive energy thresholding based 3D Otsu function for image segmentation using human learning optimization

In this paper, a novel context-based 3D Otsu algorithm using human learning optimization (HLO) is proposed for multilevel color image segmentation. The performance of 3D Otsu algorithm is reported to be poor while dealing with between-class variances through the aid of three-dimensional histogram. To overcome this problem, the concept of context thresholding has been exploited to derive pixel intensity values and spatial information. The nature of spatial context and histogram of an image is very similar. The use of energy curve for 3D Otsu gives satisfactory results but it is more time-consuming during the process of threshold selection. HLO is a recently developed meta-heuristic optimization algorithm that involves the use of learning operators developed by mimicking human learning mechanisms. In this paper, in order to avoid an exhaustive search to obtain optimal thresholds, HLO is used. Experimental studies reported in this paper demonstrate that the proposed method is better than the histogram-based 1D Otsu, 2D Otsu, and 3D Otsu methods. These claims have been confirmed by comparing fidelity parameters such as mean error (ME), mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM) and entropy.
Future design of accessibility in games: A design vocabulary

Games represent one of the most significant cultural artefacts of this century. They are a massive force in economies around the world and are enjoyed by millions of players worldwide. With their cultural significance firmly in place, it is important to ensure that all people can participate in and play games in order to feel included in our wider society. For people with disabilities, games in particular provide a cultural outlet where they can be included with everyone else, and enabled to do things on an even footing with their non-disabled peers. However, this only happens if we create the necessary design environments that provide inclusive opportunities to game alongside the rest of the player base. Guidelines have been successful in raising awareness of accessibility in games and still function well for evaluating finished games. However, they are not the generative design thinking tools that developers need. Further in being divided to address specific disabilities, they are not capturing the diversity of needs of players with disabilities and the personalised and idiosyncratic adaptations that they make in order to play. We therefore propose developing a vocabulary and language of game accessibility which is no longer about whether someone can perceive or operate an interactive technology, but instead as to whether they can have the experience they want to have. We propose the structure for such a vocabulary showing that it needs to distinguish between access to controls, enablement to meet the challenges of the game and the player experience itself. We show how the intermediate-level knowledge embodied in guidelines can be reformulated in this way to be more generative and so support designers to develop games that deliver accessible player experiences.
SecEQP: A Secure and Efficient Scheme for SkNN Query Problem Over Encrypted Geodata on Cloud

Nowadays, location-based services are proliferating and being widely deployed. For example, a Yelp user can obtain a list of the recommended restaurants near his/her current location. For some small or medium location service providers, they may rely on commercial cloud services, e.g., Dropbox, to store the tremendous geospatial data and deal with a number of user queries. However, it is challenging to achieve a secure and efficient location-based query processing over encrypted geospatial data stored on the cloud. In this paper, we propose the Secure and Efficient Query Processing (SecEQP) scheme to address the secure k nearest neighbor (SkNN) query problem. SecEQP employs the projection function-based approach to code neighbor regions of a given location. Given the codes of two locations, the cloud server only needs to compare whether codes equal or not to check the proximity of the two locations. The codes are further embedded into an indistinguishable Bloom filter tree to build a secure and efficient index. The security of SecEQP is formally proved in the random oracle model. We further prototype SecEQP scheme and evaluate its performance on both real-world and synthetic datasets. Our evaluation results show that SecEQP is a highly efficient approach, e.g., top-10 NN query over 1 million datasets only needs less than 40 msec to get queried results.