Reviewerly/exHarmony huggingface dataset:
Learning Rich Features from Software-as-a-Service Cloud Computing for Detecting Trust Violations

We are witnessing a transition era of cloud security, as cloud computing paradigm is shifting its focus from provider to the consumer. Cloud service trust manipulation detection is different from traditional on-site service trust detection because cloud performs data operations at diverse geographically remote data centers; thus diminishing consumer control over the kind of service to be utilized. When the Cloud user submits a particular job to cloud, user has to rely upon the good behavior of the cloud to perform the task without violating the user trust in services utilized. However, due to lack of transparency in cloud, consumers find it hard to evaluate trust. Inspired by the recent progress of Spatial Rich Models (SRM) in image forensics domain, we propose to employ SRM and Machine learning approach to verify the trusted behavior of cloud by analyzing the rich features of the output produced by cloud service. We investigated noise distributions in data for violation detection. The approach is based on the hypothesis that every data processing task leaves certain distinct traces on the data. We identify those digital footprints to analyze whether cloud service provider has utilized the legitimate software-as-a-service for processing consumer requests. The inconsistency between authentic and obtained output acts as a proof-of-work for trust violation detection. The experimental results for the standard image dataset demonstrate that noise distributions in spatial domain can be successfully utilized to detect Cloud service trust violations.
Towards End-to-End Scene Text Spotting by Sharing Convolutional Feature Map

In this paper, we propose a new algorithm that conjointly address scene text detection and recognition by sharing convolutional feature map. Compared with most systems which consider text detection and recognition to be irrelevant tasks, we integrate text detection and recognition into an end-to-end trainable neural network based on convolutional and recurrent neural network It can precisely detect and recognize text during a simple forward propagation, avoiding redundant processes like image patch cropping, repeated calculation of feature map. We train this unified neural network just by images and corresponding ground truth bounding boxes and text labels. Our algorithm gains outstanding performance in terms of computation time and accuracy on standard benchmark datasets. The proposed model runs robustly on multi-ratios images without complicated post-processing steps.
Hacking Tool Identification in Penetration Testing

With the rapid development of information technology, cybercrimes are getting increasingly rampant. The popularity of various toolkits facilitates the cyber-attackers to intrude on online systems and access the vast streams of sensitive data. Prevention is better than cure. Penetration testing (PT) not only defends the illegal attacks but also identify potential vulnerabilities. It is capable of distinguishing network traffic on the massive amount of collected data from the following three phases: data reduction, data analysis, and data classification. This paper analyzes the PT network traffic and observes their tool marks.
Exact Solution to an Extremal Problem on Graphic Sequences with a Realization Containing Every 2-Tree on k Vertices

A simple graph G is a 2-tree if G = K3, or G has a vertex v of degree 2, whose neighbors are adjacent, and G — v is a 2-tree. Clearly, if G is a 2-tree on n vertices, then E(G) = 2n — 3. A non-increasing sequence π = (d1,...,dn) of nonnegative integers is a graphic sequence if it is realizable by a simple graph G on n vertices. [Acta Math. Sin. Engl. Ser., 25, 795–802 (2009)] proved that if k ≥ 2, n ≥ $$\frac{9}{2}{k^2} + \frac{{19}}{2}k$$ and π = (d1,...,dn) is a graphic sequence with $$\sum\nolimits_{i = 1}^n {{d_i}} > (k - 2)n$$, then p has a realization containing every 1-tree (the usual tree) on k vertices. Moreover, the lower bound (k — 2)n is the best possible. This is a variation of a conjecture due to Erdos and Sós. In this paper, we investigate an analogue problem for 2-trees and prove that if k ≥ 3 is an integer with k = i (mod3), $$k \equiv i(\bmod 3)n \ge 20{[\frac{k}{3}]^2} + 31[\frac{k}{3}] + 12$$ π = (d1,...,dn is a graphic sequence with $$\sum\nolimits_{i = 1}^n {{d_i}} > \max \{ (k - 1)(n - 1),2[\frac{k}{3}]n - 2n - {[\frac{k}{3}]^2} + [\frac{{2k}}{3}] + 1 - 1{( - 1)^i}\} $$, then π has a realization containing every 2-tree on k vertices. Moreover, the lower bound max $$\{ k - 1)(n - 1),2[\frac{k}{3}]n - 2n - {[\frac{{2k}}{3}]^2} + [\frac{{2k}}{3}] + 1 - 1{( - 1)^i}\} $$ is the best possible. This result implies a conjecture due to [Discrete Math. Theor. Comput. Sci., 17(3), 315–326 (2016)].
Security Risk Optimization for Multi-cloud Applications

Security proved to be a major concern when organizations outsource their data storage and processing. Encryption schemes do not provide solutions as they disable data processing in the cloud. Researchers have used constraint-based data fragmentation to increase security while maintaining availability. We build on this approach by applying fragmentation to the application logic in addition to the data in the database and propose a model for security risk assessment in a multi-cloud environment. By applying a multi-objective optimization algorithm to the proposed model, we determine pareto-optimal distributions of application and data fragments to the available cloud providers.
Wind Turbine Multi-label Fault Recognition Based on Combined Feature Selection and Neural Network

With the increasing status of renewable energy in energy system, efficient wind power generation has become one of the hot issues. As the layout and running environment of wind turbines are often complicated, high fault rate becomes a key factor influencing the improvement of power generation efficiency. In order to reduce operation, maintenance cost, and improve system reliability, we focus on early-warning wind turbine system fault type. This paper proposes a combined feature selection method and BP Neural Network model to realize the prediction of multi-label fault types of the wind turbine. This method combines the advantages of ML-ReliefF and PCA in feature selection to farthest minimize irrelevant and redundant features. The BP Neural Network is then constructed and trained to predict multiple fault types which will possibly occur. Based on the real SCADA system monitoring data, our experimental results show that feature selection method has significant effect on prediction, and the combined one of ML-ReliefF and PCA performs better than each single one of them. Based on it, the BP Neural Network multi-label fault prediction model can implement the fault early warning task with superior accuracy.
Design of Translator Android Application for Sign Language and Indonesian Using User-Centered Design Approach

The lack of knowledge about sign language resulted in normal students having difficulty communicating with Deaf students. The same is true when knowledge of Indonesian oral language by Deaf students is also minimal. From this problem, an Android-based application was designed to translate between sign language and Indonesian to facilitate normal students and Deaf students to communicate. The design of the application uses the User-Centered Design approach, which is a method that focuses on the user so that the design process from the beginning to the end involves the user and the application evaluation is emphasized on usability and user experience goals. The results of the design of Android applications are in the form of high-fidelity prototypes so that applications can be operated by students. The usability test results of the application design show that evaluation of usability and user experience goals get a value above the average System Usability Scale (SUS). Therefore, the results of the application design can be said to be successful and meet the needs of users with each score, namely effective to use 4,8, efficient to use 4, easy to learn 4,4, easy to remember how to use 4,6, helpful 4,8, and satisfying 4,8. However, further development can continue to be made so that interaction, interface, and translation system functionality of the Android application can be better and more helpful for users to be able to communicate with each other.
Energy-Aware Anomaly Detection in Industrial Multi-Modal IoT Applications

This paper proposes a novel technique to facilitate an energy-aware anomaly detection in the context of edge-cloud collaborative architecture, where anomaly-oriented attributes provided by multi-modal smart things are recognized as primary and secondary ones with respect to the characteristic of certain anomaly. Experimental results demonstrate the promising performance and efficiency of our proposed technique in reducing network traffic and energy consumption in comparison with the state of art's techniques.
On the Descriptive Power of Probability Logic

In a series of imaginary conversations, a philosopher of physics and a mathematical logician discuss the investigation of a mathematical relational universe and the description of the world by physical theory, as metaphors for each other. They take for granted that basic events can be observed directly, as atomic formulas, and ask about the ability to interpret deeper underlying structures. This depends on the logic used. They are interested specifically in the role of classical probability logic in this endeavor. Within the model they adopt, as a case study, probability logic is highly competent in discovering and describing spatial structures; where a very general notion of space is taken to correspond to the logical notion of monadic, both with natural relativistic corrections. However, this logic can go no further in itself, quite unlike the unbounded abilities of first-order logic. In some cases new first-order structures emerge at the point of failure of probability logic.
A New Variational Method for Deep Supervised Semantic Image Hashing

We present a supervised semantic hashing method which uses a variational autoencoder to represent each database image sample as a product Bernoulli distribution. We show that the probability parameters approach extreme values during training, allowing them to be used directly as hash bits. We show how our method allows balanced bits to be directly specified, and is superior to state-of-the-art methods across four datasets.
Learning Diverse Sub-Policies via a Task-Agnostic Regularization on Action Distributions

Automatic sub-policy discovery has recently received much attention in hierarchical reinforcement learning (HRL). The conventional approaches to learning sub-policies suffer from collapsing into just one sub-policy dominating the whole task, lacking techniques to ensure the diversity of different subpolicies. In this paper, we formulate the discovery of diverse sub-policies as a trajectory inference. Then, we propose an information-theoretic objective based on action distributions to encourage diversity. Moreover, two simplifications are derived on discrete and continuous action space for reducing the computation. Finally, the experimental results show that the proposed approach can further improve the state-of-theart approaches without modifying existing hyperparameters on two different HRL domains, suggesting the wide applicability and robustness of our approach.
Sound Cells in Genetic Improvisation: An Evolutionary Model for Improvised Music

Musical improvisation and biological evolution are similarly based on the principles of unpredictability and adaptivity. Within this framework, this research project examines whether and how structures of evolutionary developmental logic can be detected and described in free improvisation. The underlying concept of improvisation is participative in nature and, in this light, contains similar generative strategies as there are in evolutionary processes. Further implications of the theory of evolution for cultural development in the concept of memetics and the form of genetic algorithms build an interdisciplinary network of different theories and methodologies, from which the proposed model of genetic improvisation emerges.
Converting Written Language to Spoken Language with Neural Machine Translation for Language Modeling

When building a language model (LM) for spontaneous speech, the ideal situation is to have a large amount of spoken, in-domain training data. Having such abundant data, however, is not realistic. We address this problem by generating texts in spoken language from those in written language by using a neural machine translation (NMT) model. We collected faithful transcripts of fully spontaneous speech and corresponding written versions and used them as a parallel corpus to train the NMT model. We used top-k random sampling, which generates a large variety of texts of higher quality as compared to other generation methods for NMT. We indicate that the NMT model is capable of converting written texts in a certain domain to spoken texts, and that the converted texts are effective for training LMs. Our experimental results show significant improvement of speech recognition accuracy with the LMs.
Simultaneous reconstruction and segmentation of MRI image by manifold learning

This paper proposes a method implementing manifold learning techniques to accomplish simultaneous reconstruction and segmentation of brain MRI. Simultaneous reconstruction and segmentation by manifold learning can not only overcome the conflict of the efficiency and precision, but also concentrate on the region of interest. In our study, we present a unified framework for image reconstruction and segmentation based on data-driven supervised learning task that allows a mapping between sensor and image domain. The module was consisted of reconstructing phase and segmenting phase which pre-trained by ImageNet, MGH-USC HCP and Neonatal Brain Segmentation public database, respectively. The reconstruction block is developed basing on the cascading of neural networks. And the outputs including feature map transported to the segmentation block. The segmentation block employs the dilated dense net and spatial pyramid pooling to improve the performance. For evaluation, we have tested our framework on 30 subjects with k space date and manual tagging. Our experiments show the remarkable performances of the proposed method with the reconstruction mean square error of 0.036 and segmentation dice of 0.96. In addition to accomplishing the reconstruction and segmentation with a comparable performance to existing acquisition methodologies, our framework has potential to accelerate the development of brain disease diagnosis, progression assessment and therapy decision-making.
Detection of SMS Spam Using Machine-Learning Algorithms

Short message service (SMS) is considered as one of the most popular means of communication, it allows to the mobile phone users to exchange a short text message with a low cost. Its growing popularity and its dependence on mobile phone has increased the number of attacks, caused by sending an unsolicited message like SMS spam. In this paper, we address a comparative study, between multilayer perceptron (MLP), support vector machine (SVM), random forest and k-nearest neighbors (KNN). For extracting the feature vectors the bag-of-words (BOW) and the TF-IDF methods are applied. These feature vectors are used as input for training and testing the different machine-learning classifiers mentioned above. The results of different machine-learning classifiers, based on their accuracy, precision, recall, F-measure, and ROC (receiver operating characteristic) curve have shown that the MLP outperforms SVM, random forest and KNN in SMS spam detection. Although the MLP has achieved the highest accuracy by using the BOW, than by using the TF-IDF method.
Multi Objective Path Planning in Static Environment using Region of Sight

Finding appropriate path, that avoids the hurdles and reach the goal in optimal way, has been an interesting challenge in recent years. In this paper, we present a new method for robot path planning in a static environment. The main aim; to tackle path planning in simple and fast method, seeks to satisfy many robot movement's requirements such as shortest path length, safety, smoothness with less time consuming. This method ensures that safety and smoothness criteria are already met, path length is then shortening the usage of environment details, where each point of this path can be seen only by its previous and next points. The obtained result is compared with the well-known methods and shows the superiority in simple and complex environments in term of time, smoothness and safety and the obtained path lengths are shorter than those obtained with PSO and ABC as well.
PAADA: Physical-World-Aware Approximate Data Acquisition for Image Sensor Networks

To observe the complicated physical world, sensor networks are widely used for data collection. Moreover, due to the limited energy, storage and computation capacity of sensor nodes, approximate data acquisition in adaptive sampling manner is a wide choice. Nevertheless, existing data acquisition methods are most designed for univariate data (e.g., temperature), and thus not applicable to image data with high dimensions and complex structures. In this paper, we propose a framework of Physical-world-Aware Adaptive Data Acquisition (PAADA) for image sensor networks, to sample data adaptively with pre-specified error bound. First, based on the convolutional autoencoders (CAEs), PAADA compresses the high-dimensional image data into a feature vector with a handful of hidden variables which compactly capture the key features of the image data. Second, PAADA designs a Physical-world-aware Adaptive Sampling (PAS) algorithm based on the Hermitee interpolation. Under the feature space, the PAS algorithm adjusts the sampling frequency automatically by considering the change trend of the feature vector. In addition, the feature vectors at non-sampling time points can be recovered with O (∈) approximation guarantee to the ground truths. Next, PAADA recovers the image data at non-sampling time points based on the recovered feature vectors. Finally, for each sensor, PAADA returns an image series composed of sampled images (at sampling time points) and approximate images (at non-sampling time points). Experiments on real-world datasets demonstrate that the proposed PAADA has high performance in both accuracy and energy consumption.
Multilingual speech mode classification model for Indian languages

This paper explores the vocal tract and excitation source information for the multilingual speech mode classification (MSMC) task. MSMC is a language independent speech mode classification model that could detect the mode of speech spoken in any language. Here, we considered data of three broad speech modes: conversation, extempore, and read from three Indian languages, namely, Telugu, Bengali, and Odia. The vocal tract information is captured using Mel-frequency cepstral coefficients. The pitch contour processed at supra-segmental level represents the excitation source information. The MSMC model is developed using multilayer perceptron. Experimental results show that the vocal tract features provide better overall identification accuracy, compared to excitation source information. Further, an improvement in overall accuracy is achieved by combining the scores obtained by two separate MSMC model based on excitation source and vocal tract features. The results generated using a combined score, outperform the model developed using standard vocal tract feature.
A Taxonomy of Discretization Techniques based on Class Labels and Attributes' Relationship

Discretizing continuous attributes is one essential and important data preprocessing step in data mining. Various data mining techniques are designed to be applied to discrete attributes. There have been tremendous efforts to propose discretization techniques with different characteristics. However, a clear pathway that can guide the choice of the needed discretization technique for different types of datasets is lacking. This paper proposes a taxonomy based on the existence of class information and relationship between attributes in the analyzed dataset. We review different discretization techniques classified according to the proposed taxonomy. The proposed taxonomy emphasizes the advantages and disadvantages of each discretization technique to be able theoretically to find a suitable discretization technique for a particular dataset.
On Modeling the Dynamic Thermal Behavior of Electrical Machines Using Genetic Programming and Artificial Neural Networks

We describe initial attempts to model the dynamic thermal behavior of electrical machines by evaluating the ability of linear and non-linear (regression) modeling techniques to replicate the performance of simulations carried out using a lumped parameter thermal network (LPTN) and two different test scenarios. Our focus falls on creating highly accurate simple models that are well-suited for the real-time computational demands of an envisioned symbiotic interaction paradigm. Preliminary results are quite encouraging and highlight the very positive impact of integrating synthetic features based on exponential moving averages.
Experimental Study on Boundary Constraints Handling in Particle Swarm Optimization

With an improper boundary constraints handling method, particles may get “stuck in” the boundary. Premature convergence means that an algorithm has lost its ability of exploration. Population diversity (PD) is an effective way to monitor an algorithm's ability for exploration and exploitation. Through the PD measurement, useful search information can be obtained. PSO with a different topology structure and different boundary constraints handling strategy will have a different impact on particles' exploration and exploitation ability. In this chapter, the phenomenon of particles gets “stuck in” the boundary in PSO and is experimentally studied and reported. The authors observe the position diversity time-changing curves of PSOs with different topologies and different boundary constraints handling techniques and analyze the impact of these strategies on the algorithm's ability of exploration and exploitation.
Mobile learning android based teaching materials: efforts to provide Indonesian learning based on technology in elementary school

Abstract Technology-based learning is an obligation that must be applied today by teachers. Likewise in primary schools, the use of technology is not only intended as a means of learning, but instead provides direction to students to be wise in using it. In the past few years, the tendency of students to play online games has become a popular attraction among children. So as to be able to divert all the attention of children and do not even care about the environment. Based on this phenomenon, researchers conducted research by packaging teaching materials into systems based on Android 2-dimensional mobile learning applications with the aim of diverting attention and stimulating student motivation to achieve maximum learning outcomes. In addition, in this study reinforcement is given to students how to use technology in everyday life wisely in order to minimize the impact of trends on technology, especially mobile phones.
An assistive HCI system based on block scanning objects using eye blinks

Human-Computer Interaction (HCI) provides a new communication channel between human and the computer. We develop an assistive system based on block scanning techniques using eye blinks that presents a hands-free interface between human and computer for people with motor impairments. The developed system has been tested by 12 users who performed 10 common in computer tasks using eye blinks with scanning time 1.0 second. The performance of the proposed system has been evaluated by selection time, selection accuracy, false alarm rate and average success rate. The success rate has found 98.1%.