Reviewerly/exHarmony huggingface dataset:
AR CAPTCHA: Recognizing robot by augmented reality

Summary We propose a novel AR CAPTCHA that first uses Augmented Reality to design CAPTCHA. Users should use their cameras on mobile devices to capture a marker in the 3D physical world or PC screens to find the appropriate angle to recognize each 3D character rendered on the marker in a 3D registration manner, which is very hard for machines or robots to do the same thing. Besides, we add many random 2D characters on the marker to make the state‐of‐the‐art scene text recognition methods fail to recognize the target 3D characters. To ensure availability, we design dual‐channel scenario setting (mobile phone and physical world) that enhances the security level of CAPTCHA and single‐channel scenario setting (mobile phone only) that the gyroscope could be used for our CAPTCHA. The experimental results reveal that the recognition accuracy of machines is nearly 0%, while a human could recognize AR CAPTCHA in only a few seconds.
Using the Lens Model and Cognitive Continuum Theory to Understand the Effects of Cognition on Phishing Victimization

With the growing threat of phishing emails and the limited effectiveness of current mitigation approaches, there is an urgent need to better understand what leads to phishing victimization. There is a limited body of phishing research that identified cognitive automaticity as a potential factor, but more research on the relationship between user cognition and victimization is needed. Additionally, the current phishing research has not considered the characteristics of the environment in which phishing judgments are made. To fill these gaps, this work used the analysis capabilities afforded by the double system lens model (a judgment analysis technique) and the cognitive continuum theory, specifically the task continuum index and the cognitive continuum index. By calculating a task continuum index score, the cognition best suited for the email sorting task was identified. This calculation resulted in a value which indicated that more analytical cognition was most effective. The cognitive continuum index score evaluated the participants’s cognition level while making judgments. The relationships between these measures and achievement were evaluated. Results indicated that more analytical cognition was associated with lower rates of phishing victimization. This work provides a deeper insight into the phishing problem and has implications for combating phishing.
Incremental Learning of Abnormalities in Autonomous Systems

In autonomous systems, self-awareness capabilities are useful to allow artificial agents to detect abnormal situations based on previous experiences. This paper presents a method that facilitates the incremental learning of new models by an agent. Available learned models can dynamically generate probabilistic predictions as well as evaluate their mismatch from current observations. Observed mismatches are grouped through an unsupervised learning strategy into different classes, each of them corresponding to a dynamic model in a given region of the state space. Such clusters define switching Dynamic Bayesian Networks (DBNs) employed for predicting future instances and detect anomalies. Inferences generated by several DBNs that use different sensorial data are compared quantitatively. For testing the proposed approach, it is considered the multi-sensorial data generated by a robot performing various tasks in a controlled environment and a real autonomous vehicle moving at a University Campus.
K-Segments Under Bagging approach: An experimental Study on Extremely Imbalanced Data Classification

Imbalanced dataset could be found in many real-world domains of applications, e.g. fraud detection problem, threat detection, etc.,. There are various methods that have been proposed in the past to handle the imbalanced data classification problems, but there is still a lack of in-depth research dealing with the case of extremely imbalanced data. Moreover, we can find practically the explosion of imbalance issues within the research of big data analysis, in which the imbalance ratio increases uncontrollably. Based on these two facts, we propose in this study a simplified combination of under-sampling and ensemble learning which can adapt well with different scenarios of extreme imbalance. Experimentally, we conduct our test on 11 datasets, taken from the UCI repository and Kaggle, and can show that our proposed method is not only competitive with two common methods of Under Bagging (UB) and RUSBoost, but also very effective especially in the case of extremely imbalanced big data classification problems.
Combination of Spatial and Frequency Domains for Floating Object Detection on Complex Water Surfaces

In order to address the problems of various interference factors and small sample acquisition in surface floating object detection, an object detection algorithm combining spatial and frequency domains is proposed. Firstly, a rough texture detection is performed in a spatial domain. A Fused Histogram of Oriented Gradient (FHOG) is combined with a Gray Level Co-occurrence Matrix (GLCM) to describe global and local information of floating objects, and sliding windows are classified by Support Vector Machines (SVM) with new texture features. Then, a novel frequency-based saliency detection method used in complex scenes is proposed. It adopts global and local low-rank decompositions to remove redundant regions caused by multiple interferences and retain floating objects. The final detection result is obtained by a strategy of combining bounding boxes from different processing domains. Experimental results show that the overall performance of the proposed method is superior to other popular methods, including traditional image segmentation, saliency detection, hand-crafted texture detection, and Convolutional Neural Network Based (CNN-based) object detection. The proposed method is characterized by small sample training and strong anti-interference ability in complex water scenes like ripple, reflection, and uneven illumination. The average precision of the proposed is 97.2%, with only 0.504 seconds of time consumption.
Requirement Mining in Software Product Forums

The majority of software projects fail, around 71% according to recent research. A shortage of user feedback and missed requirements are cited as primary reasons for failure. There are several prominent online platforms where software users post product feedback, including: app stores, Twitter, issue trackers and product forums. I have identified the study of product forums as a gap in the current requirement mining literature, and have selected them as the focus of this research. Product forums are widely used in the software industry, supporting online discussions between a products users and owners. While their primary function is to help customers use the product, forums are also a rich source of untapped user generated requirements. However, the manual effort to extract these requirements is prohibitively time consuming due to their large volume and inconsistent quality. Analysis tools to assist in requirement mining have been applied successfully to online platforms previously, but as of yet, not in the forum domain, where current techniques may be insufficient. My preliminary research has found that forums contain feedback useful for software maintenance and evolution, including several categories of feedback not identified in the current literature. I have developed forum specific classifiers to help categorise the different feedback in forum posts. I demonstrate that these classifiers significantly outperform a leading app store tool when applied to forums. In this report I present my preliminary findings, then outline my research plan with the final goal of producing an industry evaluated, forum analysis tool.
A Trust-Based Experience-Aware Framework for Integrating Fuzzy Recommendations

Social rating systems are widely used for gathering user feedbacks on the quality of products, items, and services. Social rating systems accept various forms of numeric and non-numeric recommendations as input to their aggregation algorithm. Fuzzy recommendations, as one form of input recommendations, while common in areas such as stock market and educational systems, are challenging in terms of aggregation and scaling. Also, taking into account trust and experience of raters while aggregating fuzzy variables is another challenge that needs investigations. In this article, we propose a trust-based experience-aware method for aggregation of fuzzy recommendations. We propose to use trust and experience of raters along with the area under the curve of the membership of the fuzzy recommendations to compute a weight for recommendations. Then, we present an iterative algorithm to aggregate these computed weighted recommendations. We evaluate our method using a real-world dataset and compare its performance with three well-known iterative algorithms. The comparison results show the superiority of our method over other related approaches.
A Differential Privacy Random Forest Method of Privacy Protection in Cloud

This paper proposes a new random forest classification algorithm based on differential privacy protection. In order to reduce the impact of differential privacy protection on the accuracy of random forest classification, a hybrid decision tree algorithm is proposed in this paper. The hybrid decision tree algorithm is applied to the construction of random forest, which balances the privacy and classification accuracy of the random forest algorithm based on differential privacy. Experiment results show that the random forest algorithm based on differential privacy can provide high privacy protection while ensuring high classification performance, achieving a balance between privacy and classification accuracy, and has practical application value.
Self-Adaptive Appearance-Based Eye-Tracking with Online Transfer Learning

Eye-tracking plays a role in human-computer interactions and has proven useful in a wide variety of domains. We consider appearance-based eye-tracking, where one tracks eye movements based solely on conventional images (rather than on sophisticated additional hardware). Recent advances made in Deep Learning and, in particular, convolutional neural networks have allowed appearance-based eye-tracking to achieve better results than ever. However, current literature still lacks methods that generalize to different combinations of user, environment and device. In this work, we introduce Online Deep Appearance-Based Eye-Tracking (ODABE), which overcomes such a limitation by considering online transfer learning, thus enabling eye-tracking models to self-adapt to different context very rapidly. Our results show that ODABE improves upon previous research when context changes, decreasing the prediction error by 50.95% on average, on tested cases.
SIPchain: SIP Defense Cluster With Blockchain

The Session Initiation Protocol (SIP) is an application-layer control protocol for creating, modifying, and terminating Voice/Video over IP sessions. While deployed globally to facilitate multimedia communications, SIP is subject to various attacks. The defense against SIP attacks, however, often lack expertise due to the limited resources within the organization. When there is a large footprint of SIP systems, scaling and keeping up SIP defense becomes crucial in safeguarding these systems. This paper proposes SIPchain, a distributed SIP defense cluster system that leverages Blockchain technology as a distributed, highly-available, and permanent ledger of Indicator of Compromise (IOC). Each node in this cluster is a sensor and shares attack intelligence with other nodes via Blockchain. Each node reads information from the Blockchain and implements the appropriate firewall rule based on this information. This approach scales the defense because each node can leverage the actionable intelligence provided by other nodes and does not have to perform detection on their own. Experiments have been performed using a cluster of three SIP nodes in three different countries (US, UK, and Singapore) and the Ethereum Blockchain network. The result shows that when a node detected an attack, it produced and stored the IOC information at the Ethererum. Fellow SIP nodes retrieved this information, implemented firewall rule based on this information, and were proactively prepared when the same attack was launched against them. This SIPchain approach scales the SIP defense effort by utilizing Blockchain technology to secure the ever-growing footprint of SIP systems within the organization.
Basis Learning Autoencoders for Hybrid Collaborative Filtering in Cold Start Setting

The following topics are dealt with: learning (artificial intelligence); neural nets; feature extraction; convolutional neural nets; image classification; optimisation; Bayes methods; pattern classification; pattern clustering; medical signal processing.
Event Prediction in Complex Social Graphs via Feature Learning of Vertex Embeddings

Social network graphs and structures possess implicit and latent knowledge about their respective vertices/actors and edges/links which may be exploited, using effective and efficient techniques, for predicting events within the social graphs. Understanding the intrinsic relationship patterns among spatial social actors as well as their respective properties are very crucial factors to be taken into consideration with regard to event prediction in social network graphs. Thus, in this paper, we model an event prediction task as a classification problem; and we propose a unique edge sampling approach for predicting events in social graphs by learning the context of each actor via neighboring actors/nodes with the goal of generating vector-space embeddings per actor. Successively, these relatively low-dimensional node embeddings are fed as input features to a downstream classifier for event prediction about the reference social graph. Training and evaluation of our approach was done on popular and real-world datasets, viz: Cora and Citeseer.
Selection of Effective Features for BGP Anomaly Detection

Border Gateway Protocol (BGP) is the internet's default protocol for managing connectivity between Autonomous Systems (AS). Anomalies happen to occur time to time and it is a threat to cyber security. There are various types of BGP anomalies and over the years researches have been done for their detection. Here machine learning techniques are used for detection of BGP anomaly from BGP update messages by considering the problem as a two class classification problem. A set of 35 features are extracted from BGP update messages for Slammer, Nimda and Code Red I attacks. The main objective of this study is to find out important features for detection of BGP anomaly. Popular feature selection algorithms, wrapper as well as several filter based algorithms are used for feature ranking. It is found that at most top 10 features are sufficient for the best classification accuracy which is verified by several classifiers.
Designing PSO-Based PI-type Fuzzy Logic Controllers

Recently, a fuzzy logic technique - based control strategy has become one of the most efficient control schemes in dealing with complicated and nonlinear control systems. The working principle of a fuzzy logic controller (FLC) is based on experiences of experts, thereby it should be one of the artificial intelligence controllers which can efficiently replace with conventional regulators. This paper concentrates on the design of a typical PI-type FLC incorporation with the particle swarm optimization (PSO) technique. The PSO algorithm is used to successfully determine three scaling factors of the PI-type FLC, thereby the proposed control strategy will be able to obtain much better control performances in comparison with the conventional regulator and/or the FLCs applying the other optimization counterparts, i.e. genetic algorithm (GA) and differential evolution (DE) algorithm. This study also investigates feasible solutions for the load-frequency control (LFC) problem of an interconnected hydroelectric power system. In fact, the LFC problem is one of the most important control issues of multi-area interconnected hydroelectric power systems. Comparative simulations with conventional PID regulators, PD-type FLC, GA- and DE-based PI-like FLCs will be implemented in this study to demonstrate the effectiveness of the proposed PSO-based PI-type FLC. The control quality of the proposed control approach revealing through major control criteria obtained, such as overshoots, steady-state error and settling time, is much better than that of the other controllers for solving the LFC problem of a hydropower system.
Parallel Implementation of Chaos Neural Networks for an Embedded GPU

The Internet of Things (IoT) has become ubiquitous, and the need for higher information security is increasing. The CPU usage cost of IoT devices to process information security tasks is large. In the present paper, we study a parallel implementation of chaos neural networks for an embedded GPU using the Open Computing Language (OpenCL). We evaluate this parallel implementation, and the results indicate that it can extract a pseudo-random number series at high speed and with low CPU usage. This implementation is remarkably faster than the implementation in the CPU and is approximately 49% faster than AES in counter mode. The rate of pseudo-random number generation is higher than 2.1 Gbps when using 100 compute units of a GPU. Applying a stream cipher is sufficient even for Internet communication. Extracted pseudo-random number series are independent, have fine randomness properties, and can merge into one series applied to a stream cipher.
REM4DSPL

Context: Dynamic Software Product Line (DSPL) is a set of software products capable of self-adapt and configure in runtime. DSPL products have common features (commonalities) and varying features (managed in runtime according to context changes). Objective: DSPL requirements engineering is challenging. Requirements engineers have to carefully plan self-adaptation while eliciting, modeling, and managing variability requirements. This paper introduces a method for DSPL requirements engineering. Method: We relied on empirically-derived activities of DSPL requirements engineering to build our method. We selected techniques and templates used in other domains such as SPL for refinement and incorporation into the method. We asked DSPL experts via a survey on the method applicability. Result: We introduced the Requirements Engineering Method for DSPL (REM4DSPL). Elicitation is guided by supervised discussions. Modeling relies on feature models. Variability Management is tool-assisted and validated via feature model inspection. DSPL experts agreed on the method applicability and suggested improvements. Conclusion: REM4DSPL relies on empirically-derived activities, techniques that have been successfully used by previous work, and templates adapted to the DSPL context. We expect our method to guide requirements engineers in practice.
Medical Image Inpainting Using Multi-Scale Patches and Neural Networks Concepts

Abstract In this paper, we consider the problem of medical images inpainting, where the goal is to reconstruct missing or damaged parts of the image. This is a good tool for medical applications such as vascular restoration, removal of specular reflections for endoscopic images, removal of MRI artifacts, etc. The new method combines the search for patches of various sizes and the operation of a pre-trained neural network. The large patches are used to reconstruct homogeneous areas, and then small patches are used to reconstruct structure image details. As a result, the proposed method provides a plausible of medical images inpainting. Experimental results demonstrate the effectiveness of the proposed method in the tasks of medical images inpainting.
Deep Learning For Super-Resolution Of Unregistered Multi-Temporal Satellite Images

Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing tasks. However, deep learning for multi-image superresolution from multitemporal imagery has received little attention so far. We propose a residual CNN that exploits both spatial and temporal correlations in the low-resolution image set by using 3D convolutional layers to combine multiple images from the same scene. The experiments have been carried out using a dataset of PROBA-V satellite ground images, composed of several low-resolution and high-resolution images taken at different times from instruments on the same platform, in the context of a challenge issued by the European Space Agency.
Software process anti-pattern detection in project data

There is a significant amount of guidance on Project Management (PM) including software development methodologies, best practices and anti-patterns (APs). There is, however, a lack of automated way of applying this knowledge by analyzing readily available data from tools aiding in software PM, such as Application Lifecycle Management (ALM) tools. We propose a method of detecting process and PM anti-patterns in project data which can be used to warn software development teams about a potential threat to the project, or to conduct more general studies on the impact of AP occurrence on project success and product quality. We previously published a concept for the data mining and analysis toolset distinct from other research approaches and related work. Based on this toolset, we devised a formalized basis for our detection method in the form of standardized AP description template and a model for pattern operationalization over project data extracted from ALM tools. The main contribution of this paper is the general method for AP operationalization taking the description template as a starting point, discussed together with its potential limitations. We performed an initial validation of the method on data from student projects, using an AP we encountered in practice called "Collective Procrastination" which we also describe in this paper together with its detailed formal operationalization.
A novel motion detecting strategy for rehabilitation in smart home

Recent years have witnessed the flourish in research and development efforts towards clinical rehabilitation systems in smart home applications. One of the most prior is that such systems are needed to provide real-time motion information about patients. In this paper, a motion detecting approach is proposed for efficiently understanding the human movement on the foundation of the Internet of Things (IoT) based architecture. Specifically, the detection algorithm is based on the fuzzy neural network (FNN), which learns to detect the variation among different gait phases. The moving phases as well as the instability of the tester are identified for recognition. On the tasks of identifying the subject motion using wearable sensing devices, this model achieves a significant high accuracy. Experimental results prove that the system is feasible for application designs and could be implemented on technological platforms.
Managing the “backend” of LIS research projects: A project management perspective

There is very little guidance in library and information science (LIS) literature about how researchers should manage the scope, time, costs, quality, human resources, communications, and risks associated with LIS research projects. To fill this gap, researchers tested the utility of project management principles (PMP) for planning and managing a project designed to enhance the information, digital, and financial literacy of the people earning less than $2 per day in India. The customization of PMP through 29 mechanisms and 60 action items was used to conduct focus groups and in-person surveys with over 150 participants, in their native language, at 10 public libraries. PMP were most helpful for managing risks (13 solutions), communications (11 solutions), and human resources (10 solutions) of the project and treating participants ethically. PMP developed in the West were helpful before, during, and after data collection in the LIS research project in a developing country.