Reviewerly/exHarmony huggingface dataset:
Variation-Aware Cloud Service Selection via Collaborative QoS Prediction

As the number of cloud services (CSs) offering similar functionality is growing, more attention has been payed on the quality of service (QoS) of CSs. However, in a dynamic cloud environment, the explicit and inherent variation of QoS causes the single CS selection via collaborative filtering techniques (CSS-CFT) to be challenging. A variation-aware approach via collaborative QoS prediction is proposed to select an optimal CS according to users’ non-functional requirements. Based on time series QoS data, this approach utilizes a set of specific cloud models to quantify the variation characteristics of QoS from the four aspects including central tendency, variation range, frequency of variation and period. To exactly identify the neighboring users for a current user, this paper employs the double Mahalanobis distances to measure the similarity of QoS cloud models. The variation-aware CSS-CFT is formulated as a multi-criteria decision-making problem, and an improved TOPSIS method is exploited to solve it, by considering both the objective QoS variation and subjective user preferences during different time periods. The experiments based on a real-world dataset demonstrate that the proposed approach can enhance the accuracy of CSS-CFT in a high-variance environment without noticeable increase of selection time, in comparison to the existing approaches.
LCC: Towards efficient label completion and correction for supervised medical image learning in smart diagnosis

Smart technology brings convenience to our lives, and smart medical diagnosis plays a particularly important role in smart cities and the smart world. In smart diagnosis, high-quality image labels are essential for supervised medical image learning. The correctness of labels has a substantial influence on smart disease diagnosis. However, the labelling of disease images by professional doctors is a time-consuming and labour-intensive project; therefore, obtaining correct and high-quality labels is a difficult task. This paper proposes a deep convolutional neural network (CNN)-based label completion and correction method (LCC) for supervised medical image learning in smart diagnosis. We use a small number of labelled images in a dataset to train a model, and design a strategy to complement most of the unlabelled data and correct noise label data, and then use the results of completion and correction to continually modify the model to achieve better results. We take the diagnosis of Seborrheic Keratosis (SK) and Flat Wart (FW) as examples; the experimental results show that this strategy can use limited labelled data to complete the labels for most of the unlabelled data and correct some noise labels in the dataset, which improves the accuracy rate of the model to identify diseases.
A Fractal-Based Authentication Technique Using Sierpinski Triangles in Smart Devices

The prevalence of smart devices in our day-to-day activities increases the potential threat to our secret information. To counter these threats like unauthorized access and misuse of phones, only authorized users should be able to access the device. Authentication mechanism provide a secure way to safeguard the physical resources as well the information that is processed. Text-based passwords are the most common technique used for the authentication of devices, however, they are vulnerable to a certain type of attacks such as brute force, smudge and shoulder surfing attacks. Graphical Passwords (GPs) were introduced as an alternative for the conventional text-based authentication to overcome the potential threats. GPs use pictures and have been implemented in smart devices and workstations. Psychological studies reveal that humans can recognize images much easier and quicker than numeric and alphanumeric passwords, which become the basis for creating GPs. In this paper a novel Fractal-Based Authentication Technique (FBAT) has been proposed by implementing a Sierpinski triangle. In the FBAT scheme, the probability of password guessing is low making system resilient against abovementioned threats. Increasing fractal level makes the system stronger and provides security against attacks like shoulder surfing.
Motion-Pattern Recognition System Using a Wavelet-Neural Network

This paper presents a wavelet-neural network recognition system for personal fitness assistance and elderly daily activity monitoring applications. This discrete wavelet transform radial basis neural network (DWT-RBNN) analyzes vibration induced by human motions, and identifies motion status automatically. The 3-D vibration signals are measured by integrated accelerometer chip, and then DWT extracts vibration features. Local energy of extracted feature is calculated and used by RBNN. A multi-channel RBNN is designed and used for recognition. The computation burden is reduced because of the DWT pre-processing. From experiment results, RBNN shows successful recognition capability. This paper also presents flow diagram to determine engineering parameters for the present and future product developments.
Parallelized Metaheuristic-Ensemble of Heterogeneous Feedforward Neural Networks for Regression Problems

A feedforward neural network ensemble trained through metaheuristic algorithms has been proposed by researchers to produce a group of optimal neural networks. This method, however, has proven to be very time-consuming during the optimization process. To overcome this limitation, we propose a metaheuristic-based learning algorithm for building an ensemble system, resulting in shorter training time. In our proposed method, a master-slave based metaheuristic algorithm is employed in the optimization process to produce a group of heterogeneous feedforward neural networks, in which the global search operations are executed on the master, and the tasks of objective evaluation are distributed to the slaves (workers). To reduce evaluation costs, the entire training dataset is randomly divided equally into several disjoint subsets. Each subset is randomly paired with another subset of the remainder and distributed to a worker for the objective evaluation. Following the optimization process, representative candidate solutions (individuals) from the entire population are selected to perform as the base components of the ensemble system. The performance of the proposed method has been compared with those of other state-of-the-art techniques in over 31 benchmark regression datasets taken from public repositories. The experimental results show that the proposed method not only reduces the computational time but also achieves significantly better prediction accuracy. Moreover, the proposed method achieved promising results in the application of a subset of the million song dataset, which identifies the release year of a song and predicts the buzz on Twitter.
A Classical-Quantum Hybrid Approach for Unsupervised Probabilistic Machine Learning

For training unsupervised probabilistic machine learning models, matrix computation and sample generation are the two key steps. While GPUs excel at matrix computation, they use pseudo-random numbers to generate samples. Contrarily, Adiabatic Quantum Processors (AQP) use quantum mechanical systems to generate samples accurately and quickly, but are not suited for matrix computation. We present a Classical-Quantum Hybrid Approach for training unsupervised probabilistic machine learning models, leveraging GPUs for matrix computations and the D-Wave quantum sampling library for sample generation. We compare this approach to classical and quantum approaches across four performance metrics. Our results indicate that while the hybrid approach–which uses one AQP and one GPU–outperforms quantum and one of the classical approaches, it performs comparably to the GPU approach, and is outperformed by the CPU approach, which uses 56 high-end CPUs. Lastly, we compare sampling on AQP versus sampling library and show that AQP performs better.
Active Learning with LEGO for Software Requirements

Case studies are one of the best active learning approaches for teaching software engineering in general and requirements engineering in particular. They offer an effective approach to understanding a system or a phenomenon that are too large or too difficult to represent in a lab setting. As a tangible manipulative, LEGO works well to support designing hands-on case studies that mix studying software engineering concepts with the elements of team building and playful creativity. In our project, we study the role that LEGO has in engaging students in software engineering through a set of activities that leverage the case study and play aspects. This paper presents our design approach for requirements engineering activities, as well as current results from classroom testing with a focus on student engagement with the subject matter.
Quantum speedup of twin support vector machines

We devise new quantum algorithms that exponentially speeds up the training and prediction procedures of twin support vector machines (TSVM). To train TSVMs using quantum methods, we demonstrate how to prepare the desired input states according to classical data, and these states are used in the quantum algorithm for the system of linear equations. In the prediction process, we employ a quantum circuit to estimate the distances from a new sample to the hyperplanes and then make a decision. The proposed quantum algorithms can learn two non-parallel hyperplanes and classify a new sample by comparing the distances from the sample to the two hyperplanes in $O(\log mn)$ time, where $m$ is the sample size and $n$ is the dimension of each data point. In contrast, the corresponding classical algorithm requires polynomial time for both the training and prediction procedures.
DCT and HOG Feature Sets Combined with BPNN for Efficient Face Classification

The problem of face classification is to classify faces based on their visual appearance of the faces. In this paper, we investigate this issue by means Back-Propagation Neural Network (BPNN) combined with both descriptors, which are the Discrete Cosine Transform (DCT) and the Histograms of Oriented Gradient (HOG). In the first case, the BPNN is used individually for feature extraction and classification, in the second case, the DCT and HOG descriptors are used to extract features of each image, then, these extracted features are utilized as the input of BPNN to classify faces and non-faces. We perform an experimental evaluation of two facial databases, BOSS, and MIT. Our proposed approach which is (HOG, BPNN) has a significant improvement on classification rate compared to other used methods getting 99.4% and 98.03% in terms of accuracy on BOSS and MIT databases respectively.
The rating model of corporate information for economic security activities

Industrial technology outflow incidents negatively affect corporations, the industry, and countries. Yet, corporate information security is weak, and there is low awareness of the issue’s seriousness. This study developed a rating model that can distinguish “importance” based on an objective standard. Fourteen components that can evaluate the importance of corporate information were derived from the related literature and verified for validity and reliability using factor analysis to organize final rating factors, such as Cost of Information Creation, Level of Information, Information Utilization, Effect of Internal Utilization, and Risk of External Leakage. A secondary survey targeted field experts to set the relative weights between five rating factors and give the relative weights for Effect of Internal Utilization Risk of External Leakage. A corporate information classification system was then designed to grades importance using the five factors. A final rating model of corporate information is suggested by defining security activity by level, granted by grade. This model is designed for corporate use and is expected to benefit economic security activity.
An Adaptive Fuzzy Level Set Model With Local Spatial Information for Medical Image Segmentation and Bias Correction

Medical image segmentation is still a challenging task due to noise and intensity inhomogeneity. An adaptive fuzzy level set model (AFLSM) with local spatial information is presented in this paper for accurately segmenting medical images and correcting bias field. A weighting scheme that can ensure each pixel in the neighborhood to have anisotropic weight is first introduced to remove noisy pixels and hence improve the robustness to noise. Then, a linear combination of orthogonal basis functions is used to represent bias field to ensure its smoothly and slowly varying property. Besides, to improve the robustness to initialization, this adaptive fuzzy level set model fuses a level set model with the membership function of fuzzy clustering, which can adaptively adjust the evolution of level set function. Finally, the distance regularization term in energy formulation is redefined with a novel double-well potential function to inherently maintain the accuracy and stability of the AFLSM. The AFLSM is first represented in the two-phase case and subsequently extended to the multi-phase formulation. The numerous visual segmentation results and quantitative evaluation can demonstrate the performance of the AFLSM on synthetic and real medical images. Comparison with the state-of-the-art models shows that the AFLSM can achieve better segmentation results with an improvement of 0.2286 ± 0.1477 in Dice coefficient and 0.1350 ± 0.0661 in Jaccard similarity coefficient in terms of robustness and the capability to correct bias field, respectively.
On the influence of low-level visual features in film classification

In this paper we present a model of parameters to aesthetically characterize films using a multi-disciplinary approach: by combining film theory, visual low-level video descriptors (modeled in order to supply aesthetic information) and classification techniques using machine and deep learning.Four different tests have been developed, each for a different application, proving the model's usefulness. These applications are: aesthetic style clustering, prediction of production year, genre detection and influence on film popularity.The results are compared against high-level information to determine the accuracy of the model to classify films without knowing such information previously. The main difference with other film characterization approaches is that we are able to isolate the influence of high-level descriptors to really understand the relevance of low-level features and, accordingly propose a useful set of low-level visual descriptors for that purpose. This model has been tested with a representative number of films to prove that it can be used for different applications.
Proteus: Language and Runtime Support for Self-Adaptive Software Development

Our software framework, Proteus, treats adaptation as a first-class object, enabling rapid development of robust, adaptive applications. Proteus developers specify their programs' intent and adaptable components (or knobs). A control-theoretic runtime continually monitors the running application, adjusting knobs so that the specified intent is met.
Protection of records and data authentication based on secret shares and watermarking

The rapid growth in communication technology facilitates the health industry in many aspects from transmission of sensor's data to real-time diagnosis using cloud-based frameworks. However, the secure transmission of data and its authenticity become a challenging task, especially, for health-related applications. The medical information must be accessible to only the relevant healthcare staff to avoid any unfortunate circumstances for the patient as well as for the healthcare providers. Therefore, a method to protect the identity of a patient and authentication of transmitted data is proposed in this study. The proposed method provides dual protection. First, it encrypts the identity using Shamir's secret sharing scheme without the increase in dimension of the original identity. Second, the identity is watermarked using zero-watermarking to avoid any distortion into the host signal. The experimental results show that the proposed method encrypts, embeds and extracts identities reliably. Moreover, in case of malicious attack, the method distorts the embedded identity which provides a clear indication of fabrication. An automatic disorder detection system using Mel-frequency cepstral coefficients and Gaussian mixture model is also implemented which concludes that malicious attacks greatly impact on the accurate diagnosis of disorders.
Evolving graph construction for successive recommendation in event-based social networks

Personalized recommendation can help individual users to quickly reserve their interested events, which makes it indispensable in event-based social networks (EBSNs). However, as each EBSN is often with large amount of entities and each upcoming event is normally with non-repetitive uniqueness, how to deal with such challenges is crucial to the success of event recommendation. In this paper, we propose an evolving graph-based successive recommendation (EGSR) algorithm to address such challenges: The basic idea is to exploit the random walk with restart (RWR) on a recommendation graph for ranking the upcoming events. In EGSR, we employ a sliding window mechanism to construct evolving graphs for successively recommending new events for each user. We propose a graph entropy-based contribution measure for adjusting the window length and for weighting the history information. In EGSR, we also apply a topic analysis technique for analyzing event text description. We then propose to establish each user an interest model and to compute the similarities in between event content and user interest as edges' weights for each recommendation graph. In successive recommendation, the number of upcoming events may experience great variations in different times. For a fair comparison, we also propose a set of cumulative evaluation metrics based on the traditional recommendation performance metrics. Experiments have been conducted based on the crawled one year data from a real EBSN for two cities. Results have validated the superiority of the proposed EGSR algorithm over the peer ones in terms of better recommendation performance and reduced computation complexity.
Cyber negotiation: a cyber risk management approach to defend urban critical infrastructure from cyberattacks

Technical tools dominate the cyber risk management market. Social cybersecurity tools are severely underutilised in helping organisations defend themselves against cyberattacks. We investigate a class of non-technical risk mitigation strategies and tools that might be particularly effective in managing and mitigating the effects of certain cyberattacks. We call these social-science-grounded methods Defensive Social Engineering (DSE) tools. Through interviews with urban critical infrastructure operators and cross-case analysis, we devise a pre, mid and post cyber negotiation framework that could help organisations manage their cyber risks and bolster organisational cyber resilience, especially in the case of ransomware attacks. The cyber negotiation framework is grounded in both negotiation theory and practice. We apply our ideas, ex post, to past ransomware attacks that have wreaked havoc on urban critical infrastructure. By evaluating how to use negotiation strategies effectively (even if no negotiations ever take place), we hope to show how non-technical DSE tools can give defenders some leverage as they engage with cyber adversaries who often have little to lose.
Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods

Morbidity prediction can be useful in improving the effectiveness and efficiency of medical services, but accurate morbidity prediction is often difficult because of the complex relationships between diseases and their influencing factors. This study investigates the effects of food contamination on gastrointestinal-disease morbidities using eight different machine-learning models, including multiple linear regression, a shallow neural network, and three deep neural networks and their improved versions trained by an evolutionary algorithm. Experiments on the datasets from ten cities/counties in central China demonstrate that deep neural networks achieve significantly higher accuracy than classical linear-regression and shallow neural-network models, and the deep denoising autoencoder model with evolutionary learning exhibits the best prediction performance. The results also indicate that the prediction accuracies on acute gastrointestinal diseases are generally higher than those on other diseases, but the models are difficult to predict the morbidities of gastrointestinal tumors. This study demonstrates that evolutionary deep-learning models can be utilized to accurately predict the morbidities of most gastrointestinal diseases from food contamination, and this approach can be extended for the morbidity prediction of many other diseases.
Geothermal resource assessment of the Gediz Graben utilizing TOPSIS methodology

The Gediz Graben possesses the second highest geothermal power generation potential in Western Anatolia, Turkey. In this study, the geothermal resource potential of the Gediz Graben has been assessed by following an ideal point methodology. For this purpose, nine criteria layers were introduced into a GIS environment, namely, distance to faults and fault density, distance to cap rock units, hot springs and graben center, Gutenberg-Richter b-value map, night-time surface temperature, NDVI and slope. Each layer was weighted by using the pairwise comparison method. Then, an ideal point methodology, namely, Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) was used to rank the geothermal favorability of the Gediz Graben from most favorable to least favorable. In order to assess the success of the geothermal favorability map, a comparison of the favorability classes with the known producing well data revealed that 76% of the wells fell within the highest two favorability classes where none of the wells were located in the least two favorable classes.
Learning Graph Similarity With Large Spectral Gap

Learning a good graph similarity matrix in data clustering is very crucial. The goal of clustering is to construct a good graph similarity matrix such that the similarity of points between the same classes is largest, and the similarity of points between different classes is smallest. In this paper, a more efficient subspace segmentation approach to learn a similarity matrix with large spectral gap is proposed. In our model, a robust self-representation coefficient matrix is learned by utilizing the Schatten-p norm instead of the conventional rank function. Besides, the fast block-diagonal structure of the coefficient representation matrix is enhanced by learning and optimizing the co-association matrix with the soft label of clustering results simultaneously in a unified framework. The affinity graphs constructed in this paper can clearly reveal the intrinsic structures of the data sets. Extensive experiments on the real data sets demonstrate that our proposed method can perform better than the state-of-the-art methods.
The life-changing magic of sharing your data

The benefits of data sharing to the scientific community are widely agreed upon. But does data sharing also benefit individual scientists? I argue that data sharing may carry tangible benefits to one’s own research that can outweigh any potential associated costs.