Reviewerly/exHarmony huggingface dataset:
Autonomously Learning One-To-Many Social Interaction Logic from Human-Human Interaction Data

We envision a future where service robots autonomously learn how to interact with humans directly from human-human interaction data, without any manual intervention. In this paper, we present a data-driven pipeline that: (1) takes in low-level data of a human shopkeeper interacting with multiple customers (28 hours of collected data); (2) autonomously extracts high-level actions from that data; and (3) learns -- without manual intervention -- how a robotic shopkeeper should respond to customers' actions online. Our proposed system for learning the interaction logic uses neural networks to first learn which customer actions are important to respond to and then learn how the shopkeeper should respond to those important customer actions. We present a novel technique for learning which customer actions are important by first learning the hidden causal relationship between customer and shopkeeper actions. In an offline evaluation, we show that our proposed technique significantly outperforms state-of-the-art baselines, in both which customer actions are important and how to respond to them.
Fault tolerating multi-tenant service-based systems with dynamic quality

The rapid development of multi-tenant service-based systems (SBSs) is mainly due to the popularity of cloud computing. SBS combines the existing services in the form of business processes to meet the needs of multiple tenants. There are two kinds of services: functional and non-functional. Multiple services can have the same functionality while changing their non-functional properties, or QoS (Quality of Service). QoS value is an important criterion for service selection or recommendation. In the existing work, QoS value in service selection or recommendation is regarded as a constant. However, in the dynamic and volatile cloud environment, the performance of services may fluctuate due to various factors (e.g. techniques of service studied, network conditions, etc.). A constant value does not reflect the consistency of the service. This paper proposes FT4MTS-D (fault-tolerant strategies for multi-tenant service-based systems with dynamic quality), a novel method that evaluates the criticality of dynamic cost-effective fault tolerance strategies for multi-tenant service-based systems. First, the consistency of each given service is evaluated based on the feature vector of the cloud model. Second, this paper measures the consistency of given service by the sensitivity of probability degradation of an SBS in comparison to the probability degradation of that service. The goal is to find the critical services in the SBS and then determine the optimal fault-tolerant strategy for the given service. The experimental results show that FT4MTS-D has obvious advantages compared with several existing representative methods.
Image restoration using hybrid features improvement on morphological component analysis

Images are generally corrupted by impulse noise during acquisition and transmission. Noise deteriorates the quality of images. To remove corruption noise, we propose a hybrid approach to restoring a random noise-corrupted image, including a block matching 3D (BM3D) method, an adaptive non-local mean (ANLM) scheme, and the K-singular value decomposition (K-SVD) algorithm. In the proposed method, we employ the morphological component analysis (MCA) to decompose an image into the texture, structure, and edge parts. Then, the BM3D method, ANLM scheme, and K-SVD algorithm are utilized to eliminate noise in the texture, structure, and edge parts of the image, respectively. Experimental results show that the proposed approach can effectively remove interference random noise in different parts; meanwhile, the deteriorated image is able to be reconstructed well.
Increasing sample efficiency in deep reinforcement learning using generative environment modelling

Abstract Reinforcement learning is a broad scheme of learning algorithms that, in recent times, has shown astonishing performance in controlling agents in environments presented as Markov decision processes. There are several unsolved problems in current state‐of‐the‐art that causes algorithms to learn suboptimal policies, or even diverge and collapse completely. Parts of the solution to address these issues may be related to short‐ and long‐term planning, memory management and exploration for reinforcement learning algorithms. Games are frequently used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible and easy to control environments. Regardless, few games feature the ability to perceive how the algorithm performs exploration, memorization and planning. This article presents The Dreaming Variational Autoencoder with Stochastic Weight Averaging and Generative Adversarial Networks (DVAE‐SWAGAN), a neural network‐based generative modelling architecture for exploration in environments with sparse feedback. We present deep maze, a novel and flexible maze game‐engine that challenges DVAE‐SWAGAN in partial and fully observable state‐spaces, long‐horizon tasks and deterministic and stochastic problems. We show results between different variants of the algorithm and encourage future study in reinforcement learning driven by generative exploration.
Use patterns for ebooks: The effects of subject, age and availability on rate of use

This article shares the results of a study of subject use of nearly 100,000 ebooks over 10 years in the OhioLINK academic library consortium. The study reveals information about how quickly ebooks in different subjects are found and used, how long they remain in use, and how consortial use by subject compares to use at individual institutions. Taken in the context of the literature about print obsolescence, the data reveals that the use of ebooks follows patterns established for print monographs, including that prior use predicts future use, use declines as titles age, and ebooks have their greatest chance of being used when they are new. Even so, this study shows that ebooks do not drop in use dramatically as they grow older; rather, the majority are used for many years. Rate of use of ebook content is, in general, highest for subjects in the sciences and lowest for humanities subjects, but these patterns may differ greatly for individual user groups.
A Descriptive Review and Classification of Organizational Information Security Awareness Research

Information security awareness (ISA) is a vital component of information security in organizations. The purpose of this research is to descriptively review and classify the current body of knowledge on ISA. A sample of 59 peer-reviewed academic journal articles, which were published over the last decade from 2008 to 2018, were analyzed. Articles were classified using coding techniques from the grounded theory literature-review method. The results show that ISA research is evolving with behavioral research studies still being explored. Quantitative empirical research is the dominant methodology and the top three theories used are general deterrence theory, theory of planned behavior, and protection motivation theory. Future research could focus on qualitative approaches to provide greater depth of ISA understanding.
TDCCN: A Two-Phase Deep Color Correction Network for Traditional Chinese Medicine Tongue Images

In Traditional Chinese Medicine (TCM), tongue diagnosis is an indispensable diagnostic method. Due to the limitations of acquisition devices and variations of the illumination, there is a significant color distortion between the captured tongue image and the human visual perceived image. In this paper, we proposed a Two-phase Deep Color Correction Network (TDCCN) for TCM tongue images. In the first phase, a deep color correction network was designed to model the mapping between the captured image and the target objective chromatic values under a standard lighting condition. The first phase provides consistent color tongue images by using different cameras and capture devices. The output tongue images at this phase can be used for further automatic quantitative analysis. The second phase provides flexible color adjusting options to adapt to different work environments and the subjective preference of doctors for visually perceived color appearance. Only three additional parameters are used to describe the adjustment operation. Experimental results show that our method achieved state-of-the-art performance in objective color correction and obtained satisfactory perceptual adaptation.
Machine learning based sensitivity analysis for the applications in the prediction and detection of cancer disease

Machine learning is used in almost all the medical fields by the diagnostics and doctors especially in predicting and detecting the risk of cancer. This growing trend of machine learning utilization in this approach enables the researchers to survey on the various types and approaches of machine learning or deep learning methods. Many methods are noted including an increased dependence on protein biomarkers and micro array data, increasing application leads to various types of cancer and instead of depending on an older Artificial Neural Network (ANN) methods, a newer trend of more interpretable machine learning methods are used. From the recent studies in the field, it is observed that machine learning or deep learning methods can be used appropriately in the range (20-30%) to improve the accuracy of prediction, development and progression, recurrence and mortality. In this paper, it is proved that unsupervised learning techniques could be used for predicting and detecting cancer tissues and appropriate analysis would be done on the data. The major merits of the proposed method over the existing cancer detection methods is the possibility of applying data from different types of cancer which describes the feature automatically and it helps to enhance the prediction and detection capabilities very specifically.
Relaxing relationship queries on graph data

In many domains we have witnessed the need to search a large entity-relation graph for direct and indirect relationships between a set of entities specified in a query. A search result, called a semantic association (SA), is typically a compact (e.g., diameter-constrained) connected subgraph containing all the query entities. For this problem of SA search, efficient algorithms exist but will return empty results if some query entities are distant in the graph. To reduce the occurrence of failing query and provide alternative results, we study the problem of query relaxation in the context of SA search. Simply relaxing the compactness constraint will sacrifice the compactness of an SA, and more importantly, may lead to performance issues and be impracticable. Instead, we focus on removing the smallest number of entities from the original failing query, to form a maximum successful sub-query which minimizes the loss of result quality caused by relaxation. We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities. To efficiently find a certificate of the success of a maximum sub-query, we propose a best-first search algorithm that leverages distance-based estimation to effectively prune the search space. We further improve its performance by adding two fine-grained heuristics: one based on degree and the other based on distance. Extensive experiments over popular RDF datasets demonstrate the efficiency of our algorithm, which is more scalable than baselines.
New Local Difference Feature for Off-Line Handwritten Signature Verification

In this work, we propose a new textural feature for solving offline handwritten signature verification. The proposed feature that is called Local Difference Feature (LDF) is a LBP-like texture descriptor. LDF calculates differences between a central pixel and eight neighbors taken on a specific neighborhood radius. Differences are coded into binary thresholded values before evaluating the histogram of codes. The verification step is achieved by SVM classifier trained on genuine signatures. Furthermore, the test stage is performed on both genuine signatures and skilled forgeries. Exepriments are conducted on GPDS and CEDAR datasets. Results obtained showed that LDF outperforms classical features such as Histogram of oriented gradients and local binary patterns. Also, the comparison with the state of the art highlights the LDF robustness for handwritten signature characterization.
Classification of different vehicles in traffic using RGB and Depth images: A Fast RCNN Approach

The Fast RCNN framework utilizes the region proposals generated from the RGB images in general for object classification and detection. This paper describes about the vehicle classification employing the Fast RCNN framework and utilizing the information provided from the combination of depth images and RGB images in the form of region proposals for object detection and classification. We use this underlying system architecture to perform evaluation on the Indian and Thailand vehicle traffic datasets. Overall, we achieve a mAP of 72.91% using RGB region proposals, and mAP of 73.77% using RGB combined with depth proposals, for the Indian dataset; and mAP of 80.61% on RGB region proposals, and mAP of 81.25% on RGB combined with depth region proposals, for the Thailand dataset. Our results show that RGB combined with depth region proposals mAP performance is slightly better than the region proposals generated using RGB images only. Furthermore, we provide insights on the performance of AP(Average Precision) for each vehicle on Thailand dataset and how effective region proposals generation is crucial for object detection using the FastRCNN framework.
Patterned Cipher Block for Low-Latency Secure Communication

Despite the increasing importance of network security, increasing Internet of Things (IoT) uptake and traffic tends to apply tighter resource constraints for cryptography. To cope with the constraints, security systems must choose between time cost and security. Cyber-attack model evolution and quantum computing technologies have severely limited current cryptography uptake and imposed too much overhead to operate effectively on lightweight communication environments. Therefore, we propose a new operation mode using multiple symmetric key ciphers alternately in a regularized order. The proposed design exploits lightweight cryptography methods, reducing encryption/decryption overhead compared to a single heavy cryptography approach, as well as avoiding exhaustive key extraction attack. Since sequences can change both time cost and security performance widely, the design can be applied to various situations, from the delay-constrained communications to highly secure networks. Our cryptography design incorporates patterned cipher block (PCB) operation, an integrity verification technique to identify if a ciphertext has been forged, handshaking protocol exchanging pattern information and a key using two-round communication, and pattern optimization to maximize the cryptographic performance. We confirmed the proposed operation mode numerically, and verified the outcomes experimentally, confirming that the proposed scheme outperformed current best practice cryptography.
Sentiment aggregation of targeted features by capturing their dependencies: Making sense from customer reviews

Ideation is an important phase in the new product development process at which product designers innovate and select novel ideas that can be added as features to an existing product. One way to find novel ideas is to transfer uncommon features of products of other domains and integrate them into the product to be improved. However, before incorporating such targeted features into the product, they need to be evaluated against the customers' acceptance in social media using sentiment aggregation tools. Despite the many studies in sentiment analysis, mapping the customers' opinions towards both high-level and technical features of a product extracted from social media to their best corresponding component in that product is still a challenge. Furthermore, none of the existing approaches ascertains the sentiment value of a targeted feature by capturing its dependencies on other features. In this paper, to address these drawbacks, we propose the sentiment aggregation framework for targeted features (SA-TF). SA-TF determines the sentiment of a targeted feature by assisting product designers in the tasks of mapping the features discussed in the reviews to the right product components, sentiment aggregation and considering feature dependencies to determine their polarity. The superiority of the different phases of SA-TF is demonstrated with experiments and comparing it with an existing approach.
Online Worker Selection Towards High Quality Map Collection for Autonomous Driving

Vehicle-based crowdsourcing is expected to be an economic yet efficient solution to build and maintain an accurate, fine-grained, and up-to-date environment map (i.e., high-definition map) for autonomous vehicles, which is an essential building block for safe and intelligent autonomous driving. However, how to select crowdsourcing workers with performance maximization is prudent and quite challenging since vehicles are highly dynamic and have unpredictable routes. In this paper, we study the worker selection problem for crowdsourced on-route map collection where the trade- off between the real-time worker exploration and exploitation is the main focus. Specifically, by adopting the multi-armed bandit model, we formulate a cumulative platform utility maximization problem. To solve this problem, we propose an Online Worker Selection (OWS) scheme, to learn drivers' performance and make worker selection decisions in real time. Essentially, two key designs are integrated in OWS: 1) performance transfer. If a new driver joins the crowdsourcing, we will initialize the new driver's performance based on the knowledge transferred from the existing drivers' records; and 2) marginal utility. Particularly, we carefully incorporate the platform utility to embody the marginal effect, i.e., repeated coverage by multiple vehicles on a certain road will undermine the utility. Based on the real-world vehicular GPS trace, we conduct extensive trace- driven simulations, and results demonstrate that our scheme can effectively obtain high-quality environment map, with on average 40.5% crowdsourcing utility gain over other benchmark schemes.
L*ReLU: Piece-wise Linear Activation Functions for Deep Fine-grained Visual Categorization

Deep neural networks paved the way for significant improvements in image visual categorization during the last years. However, even though the tasks are highly varying, differing in complexity and difficulty, existing solutions mostly build on the same architectural decisions. This also applies to the selection of activation functions (AFs), where most approaches build on Rectified Linear Units (Re- LUs). In this paper, however, we show that the choice of a proper AF has a significant impact on the classification accuracy, in particular, if fine, subtle details are of relevance. Therefore, we propose to model the degree of absence and the degree presence of features via the AF by using piecewise linear functions, which we refer to as L*ReLU. In this way, we can ensure the required properties, while still inheriting the benefits in terms of computational efficiency from ReLUs. We demonstrate our approach for the task of Fine-grained Visual Categorization (FGVC), running experiments on seven different benchmark datasets. The results do not only demonstrate superior results but also that for different tasks, having different characteristics, different AFs are selected.
The Extraction of Individual Music Preference Based on Deep Time-series CCA

This paper presents a novel method to extract individual music preference. The novel CCA, named Deep Time-series Canonical Correlation Analysis (DTCCA), is proposed to realize the aforementioned extraction. In contrast with standard CCA, our novel DTCCA can deal with not only the correlation between the input features but also the time-series relation in each input those simultaneously. A DTCCA-based latent space of projected features effectively reflects relationship between his/her EEG features and corresponding audio features rather than standard CCA's one since the audio and EEG signals have time-series relation respectively.
Eye Focus Detection Based on OpenCV

With the rapid development of computer technology and artificial intelligence, face recognition technology has been widely used in many fields. Eye location and pupil tracking play an important role in face recognition. Accurate positioning and tracking of human eyes and pupils is the core technology in many practical applications. On the basis of accurate positioning and tracking of pupils, attention analysis and calculation of attention time of human eyes can be carried out. Therefore, how to locate pupils quickly and accurately to improve the accuracy of attention and attention time detection has become the key issue of this design. This system finally realizes the positioning and judgment of the pupil of human eyes and the detection of eye attention and attention time is realized.
A Gaussian Data Augmentation Technique on Highly Dimensional, Limited Labeled Data for Multiclass Classification Using Deep Learning

In recent years, using oceans of data and virtually infinite cloud-based computation power, deep learning models leverage the current state-of-the-art classification to reach expert level performance. Researchers continue to explore applications of deep machine learning models ranging from face-, text- and voice-recognition to signal and information processing. With the continuously increasing data collection capabilities, datasets are becoming larger and more dimensional. However, manually labeled data points cannot keep up. It is this disparity between the high number of features and the low number of labeled samples what motivates a new approach to integrate feature reduction and sample augmentation to deep learning classifiers. This paper explores the performance of such approach on three deep learning classifiers: MLP, CNN, and LSTM. First, we establish a baseline using the original dataset. Second, we preprocess the dataset using principal component analysis (PCA). Third, we preprocess the dataset with PCA followed by our Gaussian data augmentation (GDA) technique. To estimate performance, we add k-fold cross-validation to our experiments and compile our results in a numerical and graphical using the confusion matrix and a classification report that includes accuracy, recall, f-score and support. Our experiments suggest superior classification accuracy of all three classifiers in the presence of our PCA+GDA approach.