Reviewerly/exHarmony huggingface dataset:
Offensive Language Detection using Artificial Neural Network

Governments and social media providers put an effort to tackle offensive, abusive, and profanity in social media as an abuse of speech freedom. Considering the number of Internet user in Indonesia and the conflict caused by offensive content about religion, race, and inter-group issues in Indonesia, there is an urge to develop offensive content detection for posts written in Bahasa. This paper uses an artificial neural network model for not only classifying the words as (non)offensive words but also considering the structure of the sentence to get its context. The challenges are informal grammar and word abbreviation used in social media. Hence, there are noise elimination and normalization processes to address these challenges. The computer simulation results show excellence accuracy of 99.18% training, 94.28% validation, and 96.8% testing, only by utilizing the sigmoid activation function. This model can assist government enforcing the information and electronic transaction law and decreases the number of disputes due to aspiration freedom abuse in social media.
Machine learning assisted geological interpretation of drillhole data: Examples from the Pilbara Region, Western Australia

In minerals exploration, routine drilling is performed and the data logged from these drillholes, including lithological composition, assays, and downhole geophysical measurements such as natural gamma logs, are used to create geological interpretations of the strata within each drillhole. A 3D geological model can be created by identifying corresponding stratigraphic boundaries within multiple drillholes. These models can be used for understanding the formation and the mineral endowment of a deposit. We introduce a system for producing stratigraphic interpretations of iron ore exploration drillholes in the Pilbara region in Western Australia. The algorithm firstly classifies each data modality independently for each geological interval, for example 2 m, with classification results for each stratigraphic unit as output. These classifiers, for geological logging, assays, gamma logs, were trained on historical datasets over a wide range of strata in the Pilbara. The influence of each classifier can be adjusted according to the user's preference, and a novel optimisation algorithm incorporates known geological features such as dykes, faults and thicknesses of various stratigraphic units, to objectively create the best fit interpretation of the geology. A geologist can then adjust this interpretation to include local knowledge. Manual interpretations of 396 drillholes from a high-grade iron ore deposit are compared to interpretations of the same hole prepared by the algorithm. Analysis of interval-by-interval interpretations, and basement geology demonstrate that without any human input, similar interpretations are produced while reducing manual effort.
Subspace Clustering Without Knowing the Number of Clusters: A Parameter Free Approach

Subspace clustering, the task of clustering high dimensional data when the data points come from a union of subspaces, is one of the fundamental tasks in unsupervised machine learning. Most of the existing algorithms for this task require prior knowledge of the number of clusters along with few additional parameters which need to be set or tuned apriori according to the type of data to be clustered. In this work, a parameter free method for subspace clustering is proposed, where the data points are clustered on the basis of the difference in the statistical distributions of the angles subtended by the data points within a subspace and those by points belonging to different subspaces. Given an initial fine clustering, the proposed algorithm merges the clusters until a final clustering is obtained. This, unlike many existing methods, does not require the number of clusters apriori. Also, the proposed algorithm does not involve the use of an unknown parameter or tuning for one. A parameter free method for producing a fine initial clustering is also discussed, making the whole process of subspace clustering parameter free. The comparison of the proposed algorithm's performance with that of the existing state-of-the-art techniques in synthetic and real data sets shows the significance of the proposed method.
DGM: A Dynamic and Revocable Group Merkle Signature

Group signatures are considered as one of the most prominent cryptographic primitives to ensure privacy. In essence, group signatures ensure the authenticity of messages while the author of the message remains anonymous. In this study, we propose a dynamic post-quantum group signature (GS) extending the static G-Merkle group signature (PQCRYPTO 2018). In particular, our dynamic G-Merkle (DGM) allows new users to join the group at any time. Similar to G-Merkle scheme, our DGM only involves symmetric primitives and makes use of a One-Time Signature scheme (OTS). Each member of the group receives a certain amount of OTS key pairs and can ask the Manager $$\mathcal {M}$$ for more if needed. Our DGM also provides an innovative way of signing revocation by employing Symmetric Puncturable Encryption (SPE) recently appeared in (ACM CCS 2018). DGM provides a significantly smaller signature size than other GSs based on symmetric primitives and also reduces the influence of the number of group members on the signature size and on the limitations of the application of G-Merkle.
Effects of Gain and Index of Difficulty on Mouse Movement Time and Fitts’ Law

The mouse, being the major means of inputting and controlling data on a computer, should beset right for best performance. Mouse gain, defined as the ratio of the movement distance on the computer screen to the movement distance of the mouse, controls the movement time when both speed and accuracy are important. Reported optimum gain values vary widely from about 2 to 15. An experiment having a targeting task was used to test 16 participants using a wide range of gain (1.2, 2.4, 14.5, and 38.9). An optimum gain of 2.4 was found for a display movement amplitude (A) of 100 mm, whereas the optimum gain was 14.5 for amplitudes of 200 and 400 mm. Depending on the gain and the movement amplitude, Fitts' law should be modified to accurately model movement time (MT) in the form of MT = A + b(ID) - c(A*ID). Only at low gains could a critical index of difficulty be determined, that being about 3.5.
Seeing Beyond Salience and Guidance: The Role of Bias and Decision in Visual Search

Visual search is a popular tool for studying a range of questions about perception and attention, thanks to the ease with which the basic paradigm can be controlled and manipulated. While often thought of as a sub-field of vision science, search tasks are significantly more complex than most other perceptual tasks, with strategy and decision playing an essential, but neglected, role. In this review, we briefly describe some of the important theoretical advances about perception and attention that have been gained from studying visual search within the signal detection and guided search frameworks. Under most circumstances, search also involves executing a series of eye movements. We argue that understanding the contribution of biases, routines and strategies to visual search performance over multiple fixations will lead to new insights about these decision-related processes and how they interact with perception and attention. We also highlight the neglected potential for variability, both within and between searchers, to contribute to our understanding of visual search. The exciting challenge will be to account for variations in search performance caused by these numerous factors and their interactions. We conclude the review with some recommendations for ways future research can tackle these challenges to move the field forward.
Comparative Study Between Cryptographic and Hybrid Techniques for Implementation of Security in Cloud Computing

Cloud computing depicts the latest technology in the field of information communication technology. Cloud computing technology offers the user with an endless list of services which they can avail, and these services range from hardware and software and infrastructure to resource utilization. Cloud computing allows us to access online software application, data storage, and processing power of system from anywhere and at anytime. Cloud computing supports the organization to increase their capacity remotely without creating own infrastructure, platform, purchasing new licensed software that is required for automation of various processes. There are a number of dominant parameters like energy dissipation, resource allocation, virtualization, and security that a user needs to keep in mind while selecting cloud computing services. Of the dominant parameters, security as a parameter in cloud holds a special concern and is one of the biggest challenges in cloud computing as far as data transfer process and data storage process are a concern as it happens through the Internet. In this research work, the authors have presented a comparative study between cryptographic and hybrid techniques for security concerns of the cloud computing paradigm. Through the results obtained from the comparative study of these techniques, it is observed that the effectiveness of the hybrid techniques is better in terms of execution of the algorithm than the cryptographic techniques (common public- and private-key cryptography) of security implementation in cloud computing.
Affine Scalarization of Two-Dimensional Utility Using the Pareto Front

Cloud computing promises flexibility, and allows applications to dynamically scale or change configuration in response to demand. Autonomic deployment is the best way to manage such applications, and the deployment decisions should aim to optimize the application owner's utility. In general this leads to multi-objective deployment decisions over multiple utility dimensions. Such problems are typically managed by forming a scalar utility as a weighted combination of various objective dimensions. However, then the maximum utility is not only depending on the utility dimensions, but also on the weights used in the scalarization. This paper proposes an approach that has the potential to reduce the number of possible deployment configurations to consider, namely the ones with least sensitivity to the weights used in the scalarization and demonstrates this approach for a small industrial application for the bi-criterion case, which is of practical importance as many real Cloud deployments aim to simultaneously minimizing the deployment cost utility dimension and maximizing the application performance utility dimension.
Model-based Hyperparameter Optimization of Convolutional Neural Networks for Information Extraction from Cancer Pathology Reports on HPC

Finding optimal hyperparameters is necessary to identify the best performing deep learning models but the process is costly. In this paper, we applied model-based optimization, also known as Bayesian optimization, using the CANDLE framework implemented on a High-Performance Computing environment. As a use case we selected information extraction from cancer pathology reports using a multi-task convolutional neural network, and hierarchical convolutional attention network to be optimized. We utilized a synthesized text corpus of 8,000 training cases and 2,000 validation cases with four types of clinical task labels including primary cancer site, laterality, behavior, and histological grade. We demonstrated that hyperparameter optimization using the CANDLE framework is a feasible approach with respect to both scalability and clinical task performance.
Paillier Cryptosystem based Mean Value Computation for Encrypted Domain Image Processing Operations

Due to its large storage facility and high-end computing capability, cloud computing has received great attention as a huge amount of personal multimedia data and computationally expensive tasks can be outsourced to the cloud. However, the cloud being third-party semi-trusted, is prone to information leakage, raising privacy risks. Signal processing in the encrypted domain has emerged as a new research paradigm on privacy-preserving processing over outsourced data by semi-trusted cloud. In this article, we propose a solution for non-integer mean value computation in the homomorphic encrypted domain without any interactive protocol between the client and the service provider. Using the proposed solution, various image processing operations, such as local smoothing filter, un-sharp masking, and histogram equalization, can be performed in the encrypted domain at the cloud server without any privacy concerns. Our experimental results from standard test images reveal that these image processing operations can be performed without pre-processing, without client-server interactive protocol, and without any error between the encrypted domain and the plain domain.
Small Object Detection Networks Based on Classification-Oriented Super-Resolution GAN for UAV Aerial Imagery

Small object detection is challenging for lack of discriminative information compared to medium, large objects. In this paper, we proposed small object detection networks based on classification-oriented super-resolution generative adversarial networks (CSRGAN) and validate them on public UAV aerial imagery benchmark. Through appending classification branch and introducing classification loss to typical SRGAN, generator of CSRGAN is trained to reconstruct realistic super-resolved (SR) images with classification-oriented discriminative features from low resolution images while discriminator is trained to predict true categories and distinguish generated SR images from original ones. Besides, VGG19 based feature-level content loss is applied to recover clearer and sharper contours in SR images, which is critical to object classification. Experiment results prove the classification-oriented enhancing effect of CSRGAN and the positive function of VGG19 based feature-level content loss.
Fast Exploration Using Multirotors: Analysis, Planning, and Experimentation

This work presents a system and approach for the rapid exploration of unknown environments using aerial robots. High-speed flight with multirotor air vehicles is challenging due to limited sensing range, use of onboard computation, and constrained dynamics. For robots operating in unknown environments, the control system must guarantee collision-free operation, and for exploration tasks, the system should also select sensing actions to maximize information gain with respect to the environment. To this end, we present a motion primitive-based, receding-horizon planning approach that maximizes information gain, accounts for platform dynamics, and ensures safe operation. Analysis of motions parallel and perpendicular to frontiers given constraints on sensing and dynamics leads to bounds on safe velocities for exploration. This analysis and the bounds obtained inform the design of the motion primitive approach. Simulation experiments in a complex 3D environment demonstrate the utility of the motion primitive actions for rapid exploration and provide a comparison to a reduced motion primitive library that is appropriate for online planning. Experimental results on a hexarotor robot with the reduced library demonstrate rapid exploration at speeds above 2.25 m/s under a varying clutter in an outdoor environment which is comparable to and exceeding the existing state-of-the-art results.
A Concept of Smart Medical Autonomous Distributed System for Diagnostics Based on Machine Learning Technology

Telemedicine is a promising direction in the development of medical technologies for the interaction of patients with doctors at a distance. In this paper, we consider the use of telemedicine technologies for the development of smart medical autonomous technology. An example of a smart medical autonomous distributed system for diagnostics is also discussed. To develop this system for medical image analysis we review several processing methods and machine learning algorithms. Some examples of medical system processing results are presented.
Recruit Until It Fails

Distinguishing identities is useful for several applications such as automated grocery or personalized recommendations. Unfortunately, several recent proposals for identification systems are evaluated using poor recruitment practices. We discovered that 23 out of 30 surveyed systems used datasets with 20 participants or less. Those studies achieved an average classification accuracy of 93%. We show that the classifier performance is misleading when the participant count is small. This is because the finite precision of measurements creates upper limits on the number of users that can be distinguished. To demonstrate why classifier performance is misleading, we used publicly available datasets. The data was collected from human subjects. We created five systems with at least 20 participants each. In three cases we achieved accuracies greater than 90% by merely applying readily available machine learning software packages, often with default parameters. For datasets where we had sufficient participants, we evaluated how the performance degrades as the number of participants increases. One of the systems built suffered a drop in accuracy that was over 35% as the participant count increased from 20 to 250. We argue that data from small participant count datasets do not adequately explore variations. Systems trained on such limited data are likely to incorrectly identify users when the user base increases beyond what was tested. We conclude by explaining generalizable reasons for this issue and provide insights on how to conduct more robust system analysis and design.
Siamese Tracking with Adaptive Template-Updating Strategy

Recently, we combined a contour-detection network and a fully convolutional Siamese tracking network to initialize a new start-up of vehicle tracking by clicking on the target, generating a contour proposal template instead of using a fixed bounding box. Tests on the OTB100 and Defense Advanced Research Projects Agency (DARPA) datasets proved that our method outperformed the state of the art and effectively solved the partial-occlusion problem. However, the current Siamese tracking method uses the target in the first frame as a template during the whole tracking period, and leads to the failed tracking of target deformation. In this paper, we propose a new template-update method and reconstruct the whole tracking process with a template-updating module. To be specific, the innovative adaptive template-updating module is comprised of a neural contour-detection network and a target-detection network. Experiment results on the DARPA dataset prove that our new tracking algorithm with the template-updating strategy prominently improved tracking accuracy regarding the deformation condition.
Proactivizer: Transforming Existing Verification Tools into Efficient Solutions for Runtime Security Enforcement

Security verification plays a vital role in providing users the needed security assurance in many applications. However, applying existing verification tools for runtime security enforcement may suffer from a common limitation, i.e., causing significant delay to user requests. The key reason to this limitation is that these tools are not specifically designed for runtime enforcement, especially in a dynamic and large-scale environment like clouds. In this paper, we address this issue by proposing a proactive framework, namely, Proactivizer, to transform existing verification tools into efficient solutions for runtime security enforcement. Our main idea is to leverage existing verification tools as black boxes and to proactively trigger the verification process based on dependency relationships among the events. As a proof of concept, we apply Proactivizer to several existing verification tools and integrate it with OpenStack, a popular cloud platform. We perform extensive experiments in both simulated and real cloud environments and the results demonstrate the effectiveness of Proactivizer in reducing the response time significantly (e.g., within 9 ms to verify a cloud of 100,000 VMs and up to 99.9% reduction in response time).
Laser dot projection videogrammetry for vibration measurement and control of a piezoelectric flexible cantilever plate

A novel full-field non-contact vibration measurement method is investigated for a piezoelectric flexible cantilever plate, using laser dot-projection videogrammetry. Several laser dots are projected as marks for visual system. The three-dimensional coordinates of each mark are monitored by a binocular stereo vision system, composed of two cameras. After image processing, the vibration signal of the first bending mode is obtained. For the first torsional mode, two laser displacement sensors are utilized to detect the vibration. To suppress the residual vibration quickly, an adaptive nonlinear controller and a fuzzy tuning nonlinear controller are designed and adopted. To verify the investigated measurement and control methods, experiments are conducted by using a piezoelectric flexible cantilever plate experimental platform. The experimental results demonstrate that the investigated laser dot projection measurement method is effective for first bending vibration detection. Furthermore, compared with PD control, the adopted nonlinear algorithms have better damping performance, especially for small amplitude residual vibration.
Position independent activity recognition using shallow neural architecture and empirical modeling

The goal of the SHL recognition challenge 2019 is to recognize transportation modalities in a sensor placement independent manner. In this paper, the performance of shallow neural networks is benchmarked by Team Orion in such a manner on the dataset provided in the challenge, using 156 handcrafted temporal and spectral features per sensor through the application of parallel processing and out-of-memory architecture. Using scaled conjugate gradient back-propagation (SCGB) algorithm, combining classes 7 and 8 and taking 5000 frames of bag-hips-torso data from validation set, classification accuracy of 87.2% was obtained on the validation dataset of the same labels for a shallow two-layer feed-forward network. 71% accuracy was obtained on the validation set of classes 7 and 8 via transfer of 2500 frames using another shallow neural network of similar architecture. Using empirically observed variable based transfer of 7088 frames from hand validation data to training dataset, 77.5% accuracy was obtained on hand validation data for classes 1 to 7/8, and 70% classification accuracy of classes 7 and 8 via transfer of 1809 frames from hand validation data. The results illustrate how carefully crafted features coupled with empirical transfer of labeled knowledge and combination of problematic classes can tune a neural classifier to work in a new feature space.
Construction of the equation of fractals structure based on the rvachev r-functions theories

Abstract The article is devoted to the construction of the equation of fractal complex structures based on the theories of Rvachev R-functions (RFM), the application of the recursion procedure. Using the equation of objects elementary geometries and constructive means of the method of R-functions R0: R-conjunctions, R-disjunctions and R-reflections, various fractal equations are constructed. Based on these equations, specifying the number of iteration and the angle of inclination various pre-fractals are generated