Reviewerly/exHarmony huggingface dataset:
Cellular learning automata based bare bones PSO with maximum likelihood rotated mutations

In this work, a new variant of multi-swarm bare bones PSO is presented which adaptively learns the promising alignments to re-orient its updating distributions. The idea is to maximize the likelihood of generating new particles along appropriate directions. As these alignment directions are unknown a priori, the proposed method employs a learning mechanism for adaptive learning of suitable alignments. The learning mechanism presented in this paper is based on cellular learning automata. Several alignment strategies are developed for each particle in the proposed method, and the cellular learning automata guides the particles toward the most promising directions by adjusting these strategies during the searching process. The new proposed algorithm is compared with a group of stochastic optimization approaches on the CEC2013 benchmark set. Experiential studies suggest the effectiveness of the proposed method in solving complex optimization problems.
Causal Inference in Geoscience and Remote Sensing From Observational Data

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's science. In remote sensing and geosciences, this is of special relevance to better understand the earth's system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires: 1) two regression models in the forward and backward directions and 2) the estimation of statistical independence between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the sensitivity (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence, the criterion is robust to spurious detections. We illustrate the performance in a collection of 28 geoscience causal inference problems, a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and assessing the impact of different regression models in a carbon cycle problem. The criterion achieves the state-of-the-art detection rates in all cases, and it is generally robust to noise sources and distortions. The presented approach confirms the validity in observational bivariate problems in the earth sciences.
Why Johnny can’t develop a secure application? A usability analysis of Java Secure Socket Extension API

Lack of usability of security Application Programming Interfaces (APIs) is one of the main reasons for mistakes that programmers make that result in security vulnerabilities in software applications they develop. Especially, APIs that provide Transport Layer Security (TLS) related functionalities are sometimes too complex for programmers to learn and use. Therefore, applications are often diagnosed with vulnerable TLS implementations due to mistakes made by programmers. In this work, we evaluated the usability of Java Secure Socket Extension (JSSE) API to identify usability issues in it that persuade programmers to make mistakes while developing applications that would result in security vulnerabilities. We conducted a study with 11 programmers where each of them spent around 2 hours and attempted to develop a secure programming solution using JSSE API. From data we collected, we identified 59 usability issues that exist in JSSE API. Then we divided those usability issues into 15 cognitive dimensions and analyzed how those issues affected the experience of participant programmers. Results of our study provided useful insights about how TLS APIs and similar security APIs should be designed, developed and improved to provide a better experience for programmers who use them.
How hand gestures influence the enjoyment in gamified mobile marketing

With the rapid development of mobile technologies, gamification has been deployed pervasively on mobile platforms as an effective marketing tool. In this study, we focus on how mobile gesture technology influences consumer enjoyment of mobile marketing games by examining two types of hand gestures: surface and motion gestures. Considering the characteristics of hand gestures and embodied cognition theories, we propose that hand gestures influence the enjoyment of mobile marketing games with two other game elements: object visual presentation and reward setting. Specifically, the interaction between surface gesture (vs. motion gesture) and the object visual presentation of a real product picture (vs. symbolic brand logo image) leads to greater enjoyment of marketing games through mental simulation. Similarly, the interaction between motion gesture (vs. surface gesture) and reward setting of uncertainty (vs. certainty) leads to greater enjoyment of marketing games through perceived control. Three online experiments are conducted to support the proposed hypotheses. Results provide implications for marketing practitioners that hand gesture design should be aligned with congruent object visual presentation and reward setting in order to enhance consumer enjoyment of mobile marketing games. This study contributes to the extant literature of gamification, as well as human-computer interaction.
Insider trading ahead of cyber breach announcements

Stock market reactions to cybersecurity breach announcements are generally negative. We find significant evidence of opportunistic insider trading, with insiders saving an average of $35,009 due to timely selling in the three months before the announcement of a cybersecurity breach. Late filing violations by insiders are more likely to occur near the announcement of a cyber breach. The bulk of opportunistic trading tends to occur 55–72 days before the public announcement. The results lend support to the U.S. Security and Exchange Commission's recently announced goal of tightening restrictions on insider trading ahead of cyber breach announcements.
How UX Practitioners Produce Findings in Usability Testing

Usability testing has long been a core interest of HCI research and forms a key element of industry practice. Yet our knowledge of it harbours striking absences. There are few, if any detailed accounts of the contingent, material ways in which usability testing is actually practiced . Further, it is rare that industry practitioners’ testing work is treated as indigenous and particular (instead subordinated as a ‘compromised’ version). To service these problems, this article presents an ethnomethodological study of usability testing practices in a design consultancy. It unpacks how findings are produced in and as the work of observers analysing the test as it unfolds between moderators taking participants through relevant tasks. The study nuances conventional views of usability findings as straightforwardly ‘there to be found’ or ‘read off’ by competent evaluators. It explores how evaluators/observers collaboratively work to locate relevant troubles in the test's unfolding. However, in the course of doing this work, potential candidate troubles may also routinely be dissipated and effectively ‘ignored’ in one way or another. The implications of the study suggest refinements to current understandings of usability evaluations, and affirm the value to HCI in studying industry practitioners more deeply.
Learning Intra-Video Difference for Person Re-Identification

Siamese networks are prevalent in person re-identification (re-id) tasks to address the similarity and dissimilarity among video frames. It mainly focuses on the inter-video variation between spatio-temporal features extracted from different videos, while the variation between features of the same video has been rarely discussed. In this paper, we introduce the concept of “mean-body” and define an intra-video loss to address the variation between spatio-temporal features of the same video. A novel loss is presented to boost the training of the re-id networks by combining the proposed intra-video loss and the Siamese loss. Specifically, the intra-video loss uses the unique mean-body of each camera viewpoint to make the video sequence more clustered, while the Siamese loss is to make the wrong matching videos more separated. To train the whole network, we update the network and the mean-body in an iterative manner. As a result, the proposed loss is expected to improve the generalization capability of the re-id networks on the testing set. Extensive results demonstrate that the presented approach outperforms the state-of-the-art algorithms on the publicly available data sets, such as PRID2011, iLIDS-VID, and MARS, in terms of re-id accuracy.
Impact of random weights on nonlinear system identification using convolutional neural networks

Randomized algorithms have been successfully applied in modeling dynamic system. How do random weights affect system identification and why do they sometimes work well? In this paper, we use the convolutional neural network (CNN) as an identification model to answer these questions. Since the convolution operation is an important property of the dynamic system and in the frequency domain it becomes the product, the CNN model is analyzed in the frequency domain. We first modify the CNN model, so that it can model both the input and the output series. Then we analyze the impact of the random weights of CNN in the frequency domain. We prove the existence of optimal weights and analyze the modeling accuracy under optimal weights and random weights. Through theoretical analysis, we propose a two-step training method and compare it with the random weight algorithm. The proposed CNN model with random weights is validated with three benchmark problems.
Visual-Attention-Based Pixel Dimming Technique for OLED Displays of Mobile Devices

Organic light-emitting diode (OLED) display has been widely used for mobile devices due to its features of wide color range and high contrast. However, display systems dominate the significant power consumption of mobile devices that limit its usage time. Hence, this paper proposes a visual-attention-based pixel dimming (VAB-PD) technique to achieve power saving for real-time video displaying on OLED mobile devices. First, the proposed method extracts motion vector (MV) array from the bitstream. Next, considering the perception of human vision to moving objects, a saliency map is calculated from the MV array. Finally, a pixel dimming function is designed to reduce the brightness of the pixels lying in the unnoticeable parts of the video frames, thereby elevating the energy efficiency without deteriorating the visual quality. In addition, an absolute category rating 11-grade scale (ACR11) subjective visual quality test is conducted to determine the optimal coefficient for the dimming function. The experimental results demonstrate that VAB-PD can accomplish 13% of power reduction on average while maintaining acceptable visual quality.
Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation

Recent advancements in artificial intelligence are providing the insurance industry with new opportunities to create tailored solutions and services based on newfound knowledge of consumers, and the execution of enhanced operations and business functions. However, insurance data are heterogeneous, and imbalanced class distribution with low frequency and high dimensions, which presents four major challenges to machine learning in real-world business. Traditional machine learning algorithms can typically apply to standard data sets, which are normally homogeneous and balanced. In this paper, we focus on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require preprocessing. Our approach comprises a novel, unified, end-to-end cost-sensitive parallel neural network that learns real-world heterogeneous data. A specifically designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications, and the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. We also study the CPLF-based architecture for a real-world insurance intelligence operation system, and demonstrate fraud detection and policy renewal experiments on this system. The results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of our design.
Development of content‐based SMS classification application by using Word2Vec‐based feature extraction

While mobile instant messaging applications such as WhatsApp, Messenger, Viber offer benefits to phone users such as price, easy usage, stable, collective and direct communication, SMS (short message service) is still considered a more reliable privacy-preserving technology for mobile communication. This situation directs the institutions that want to perform the product promotion such as advertising, informing, promotion etc. to use SMS. However, spam messages sent from unknown sources constitute a serious problem for SMS recipients. In this study, a content-based classification model which uses the machine learning to filter out unwanted messages is proposed. From the selected dataset, the model to be used in the classification is created with the help of Word2Vec word embedding tool. Thanks to this model, two new features are revealed for calculating the distances of messages to spam and ham words. The performances of the classification algorithms are compared by taking these two new features into consideration. The random forest method succeeded with a correct accuracy rate of 99.64%. In comparison to other studies using the same dataset, more successful correct classification percentage is achieved.
Salient object detection based on an efficient End-to-End Saliency Regression Network

Salient object detection aims at detecting and segmenting the most salient objects from images or videos. It serves as a pre-processing step for a variety of computer vision and image processing tasks. Therefore, efficient and simple detection procedure is the primary requirement of salient object detection. Although many methods with impressive performances have been proposed, they always include complicated procedures. They are time-consuming and not easy to be applied in practical application. In order to address this issue, we propose an efficient and simple salient object detection architecture based on saliency regression network. Our method is a simplified end-to-end deep neural network without any pre-processing and post-processing. It can directly predict a dense full-resolution saliency map for a given image with a compact pipeline. Experimental results on five benchmark datasets show that the proposed method can achieve comparable or better precision performance than the state-of-the-art methods while get an improvement in the detection speed.
Multiobjective Rule-Based Cooperative Continuous Ant Colony Optimized Fuzzy Systems With a Robot Control Application

This paper proposes a new rule-based cooperative framework for multiobjective evolutionary fuzzy systems (FSs). Based on the framework, a multiobjective rule-based cooperative continuous ant-colony optimization (MO-RCCACO) algorithm is proposed to optimize all of the free parameters in FSs. Instead of optimization using a single colony of FSs (solutions), the MO-RCCACO consists of r subcolonies of size N cooperatively optimizing an FS that consists of r rules, with a subcolony optimizing only a single fuzzy rule. In addition, an auxiliary colony is created to store all of the fuzzy rules in the best-so-far N FSs to enhance the optimization ability of MO-RCCACO. The performance ranking of different fuzzy rules in the same subcolony is performed based on the multiobjective function values of their participating FSs by using Pareto nondominated sorting and the crowding distance. The MO-RCCACO is applied to find the Pareto-optimal fuzzy controllers (FCs) of a mobile robot for wall following with multiple control objectives. The optimization ability of the MO-RCCACO is verified through comparisons with various multiobjective population-based optimization algorithms in the robot wall-following control problem. Experimental results verify the effectiveness of the MO-RCCACO-based FCs for the boundary following control of a real robot.
Quantum simulation of multiphoton and nonlinear dissipative spin-boson models

We present a framework for the realization of dissipative evolutions of spin-boson models, including multiphoton exchange dynamics, as well as nonlinear transition rates. Our approach is based on the implementation of a generalized version of a dissipative linear quantum Rabi model. The latter comprises a linearly coupled spin-boson term, spin rotations, and standard dissipators. We provide numerical simulations of illustrative cases supporting the good performance of our method. Our work allows for the simulation of a large class of fundamentally different quantum models where the effect of distinct dissipative processes can be easily investigated.
A New Approach for Conceptual Extraction-Transformation-Loading Process Modeling

Erroneous or incomplete data generated from various sources can have direct impact in business analysis. Extracted data from sources need to load into data warehouse after required transformation to reduce error and minimize data loss. This process is also known as Extraction-Transformation-Loading (ETL). High-level view of the system activities can be visualized by conceptual modeling of ETL process. It provides the advantage of pre-identification of system error, cost minimization, scope and risk assessment etc. A new modeling approach is proposed for conceptualization ETL process by using a standard Systems Modeling Language (SysML). For handling increasing complexity of any system model, it is preferable to go through verification and validation process in early stage of system development. In this article, the authors' previous work is extended by presenting a MBSE based approach to automate the SysML model's validation by using No Magic simulator. Here, the main objective is to overcome the gap between modeling and simulation and to examine the performance of the proposed SysML model. The usefulness of the authors' approach is exhibited by using a use case scenario.
Accurate and Scalable Image Clustering Based on Sparse Representation of Camera Fingerprint

Clustering images according to their acquisition devices is a well-known problem in multimedia forensics, which is typically faced by means of camera sensor pattern noise (SPN). Such an issue is challenging since SPN is a noise-like signal, hard to be estimated, and easy to be attenuated or destroyed by many factors. Moreover, the high dimensionality of SPN hinders large-scale applications. Existing approaches are typically based on the correlation among SPNs in the pixel domain, which might not be able to capture intrinsic data structure in the union of vector subspaces. In this paper, we propose an accurate clustering framework, which exploits linear dependences among SPNs in their intrinsic vector subspaces. Such dependences are encoded under sparse representations, which are obtained by solving an LASSO problem with non-negativity constraint. The proposed framework is highly accurate in a number of clusters' estimation and image association. Moreover, our framework is scalable to the number of images and robust against double JPEG compression as well as the presence of outliers, owning big potential for real-world applications. Experimental results on Dresden and Vision database show that our proposed framework can adapt well to both medium-scale and large-scale contexts and outperforms the state-of-the-art methods.
Privacy preserved wireless sensor location protocols based on mobile edge computing

The emergence of all kinds of Internet of Things applications have brought great convenience to the people’s life, while wireless sensor location is one of the most important basements for them. Security and privacy issues should always the top priority of a system, however, the privacy protection of wireless sensor location is particularly difficult. In this paper, corresponding to the most common location algorithms, trilateration and multilateration, two privacy preserved location protocols are proposed based on Paillier’s homomorphic encryption scheme. The protocols are designed in Mobile-Edge Computing architecture. Different from other schemes who protect location privacy by identity anonymity, the proposed protocols protect the privacy of the real location information. The base stations who are semi-honest and not colluding with each other, send the encrypted distance data to edge server, and edge server computes on ciphertext and products the encrypted location of the sensor. The security of the protocols is based on the semantic security of Paillier encryption. To obtain the sensor’s position in a privacy protect manner, all the extra calculations include one step of encryption for each base station and one step of decryption for sensor, and only two times of message transmission are needed, the proposed protocols are secure and practical.
Dynamic Autoselection and Autotuning of Machine Learning Models for Cloud Network Analytics

Cloud network monitoring data is dynamic and distributed. Signals to monitor the cloud can appear, disappear or change their importance and clarity over time. Machine learning (ML) models tuned to a given data set can therefore quickly become inadequate. A model might be highly accurate at one point in time but may lose its accuracy at a later time due to changes in input data and their features. Distributed learning with dynamic model selection is therefore often required. Under such selection, poorly performing models (although aggressively tuned for the prior data) are retired or put on standby while new or standby models are brought in. The well-known method of Ensemble ML (EML) may potentially be applied to improve the overall accuracy of a family of ML models. Unfortunately, EML has several disadvantages, including the need for continuous training, excessive computational resources, requirement for large training datasets, high risks of overfitting, and a time-consuming model-building process. In this paper, we propose a novel cloud methodology for automatic ML model selection and tuning that automates model building and selection and is competitive with existing methods. We use unsupervised learning to better explore the data space before the generation of targeted supervised learning models in an automated fashion. In particular, we create a Cloud DevOps architecture for autotuning and selection based on container orchestration and messaging between containers, and take advantage of a new autoscaling method to dynamically create and evaluate instantiations of ML algorithms. The proposed methodology and tool are demonstrated on cloud network security datasets.