Reviewerly/exHarmony huggingface dataset:
Into the wild: a study in rendered synthetic data and domain adaptation methods

Rendering synthetic imagery from gaming engine environments allows us to create data featuring any number of object orientations, conditions, and lighting variations. This capability is particularly useful in classification tasks, where there is an overwhelming lack of labeled data needed to train state-of-the-art machine learning algorithms. However, the use of synthetic data is not without limit: in the case of imagery, training a deep learning model on purely synthetic data typically yields poor results when applied to real world imagery. Previous work shows that "domain adaptation," mixing real-world and synthetic data, improves performance on a target dataset. In this paper, we train a deep neural network with synthetic imagery, including ordnance and overhead ship imagery and investigate a variety of methods to adapt our model to a dataset of real images.
Robust infrared target tracking using thermal information in Mean-Shift

The basic computational module of the technique is an old pattern recognition procedure: the mean-shift. In case of gray level feature domain, the spatial information of the target is lost when the background brightness histogram is the same as the target histogram. In this paper, we propose a new algorithm that is independent of background contrast by changing features from a conventional brightness based histogram to a temperature- based histogram. The proposed algorithm can track targets robustly regardless to target-background contrast. The experiment results demonstrate that the temperature-based Mean-Shift outperforms comparing with the brightness-based Mean-Shift when track a object with successive background variations.
Verifying Cloud Application for the Interaction Correctness Using SoaML and SPIN

Cloud application is a kind of software implemented using cloud computing technology and deployed in the cloud environment. This paper focuses on guaranteeing the interaction correctness between cloud application and users. In general, the main methods to do this are testing and verification. But in the cloud environment, testing is costly and the operation is extremely difficult, while the verification can avoid these shortcomings, and be suitable for cloud application. In this paper, we use SoaML(Service-Oriented Architecture Modeling Language) to model cloud application, apply hierarchical automaton to formalize ServiceInterface of SoaML, and translate SeviceInterface into PROMELA according to the semantics of automaton; Meanwhile, describe the ServiceContract of SoaML using linear temporal logic (LTL). Both PROMELA and LTL formula are integrated into SPIN model checker for automatic verification of cloud application. Experiment shows that we can verify the correctness of cloud application effectively.
Electronic Citizen Service Delivery, MeeSeva - Telangana State, India

MeeSeva initiated in year 2011 provides smart, citizen centric, ethical, efficient and effective governance leveraging advancements in technology. The initiative involves delivery of government services to citizens & businesses of all strata in a universal and non-discriminatory manner. The core design of MeeSeva was based on the objectives of improved efficiency, transparency and accountability for the government resulting in significantly improved service delivery to citizens. MeeSeva is an interface between government departments and citizens acting as the nodal agency (Electronic Service Delivery) as well as the platform for delivery of G2C services. MeeSeva has redefined the way Government services are delivered to citizens by bringing about 600+ services of 38 departments on a single platform. MeeSeva has taken government to the citizen's doorsteps through various touch points which include:
Methodological Support of Readiness Formation of Future Teachers for Inclusion

The results of higher education are determined by normative documents and are based on the theory of competence approach. They refl ect the readiness&#x0D; of the future teacher to professional activities in the terms of inclusive education.&#x0D; Analytical verifi cation of various scientifi c sources makes it possible to assert that the essence of the various components of the methodological support of the learning process is the subject of scientifi c disputes. But it is worth noting that currently the researchers have not brought a single interpretation of such a concept as “methodological support”, which forces for their own understanding of this designation to refer to it. Methodological support is mainly singled out not just as a pedagogical component, but also as an integral part of the whole educational process. It is characterized by the volume and content of the working programs of disciplines, provides organizational and pedagogical conditions necessary for the formation of the educational environment of the University readiness of future teachers for inclusion. Properly selected and implemented in full methodological support of any process makes it guaranteed to be effective.
Fast multi-modal reuse: co-occurrence pre-trained deep learning models

The purpose of this paper is on the study of data fusion applications in traditional, spatial and aerial video stream applications which addresses the processing of data from multiple sources using co-occurrence information and uses a common semantic metric. Use of co-occurrence information to infer semantic relations between measurements avoids the need to make use of such external information, such as labels. Many of the current Vector Space Models (VSM) do not preserve the co-occurrence information leading to a not so useful similarity metric. We propose a proximity matrix embedding part of the learning metric embedding which has entries showing the relations between co-occurrence frequency observed in input sets. First, we show an implicit spatial sensor proximity matrix calculation using Jaccard similarity for an array of sensor measurements and compare with the state-of-the-art kernel PCA learning from feature space proximity representation; it relates to a k-radius ball of nearest neighbors. Finally, we extend the class co-occurrence boosting of our unsupervised model using pre-trained multi-modal reuse.
Muse and Power: African Women Writers and Digital Infrastructure in World Literature

Summary This article explores how women writers in Nigeria and Tanzania use digital media, drawing parallels between infrastructural enablement and literary worldmaking. It argues that female African writers offer insights into the embodied practices and cultural imaginaries of digitally mediated creativity, which can shed light on the paradoxical entanglements of infrastructure.
An Integrated Error Detection System For POS-tags In Korean

A POS-tagged corpus is valuable data for NLP tasks. However, a large amount of POS-tagged corpus contains errors and errors debase the quality of corpus. To deal with this problem, we adopt Loftsson(2009)'s approach for POS-tagging error detection with the consideration of linguistic features of Korean. We introduce an integration of two methods to detect errors. One is variation n-gram method and another is multiple taggers' disagreement method. Then, we reconsider 'Sejong tagset' that is used in Sejong corpus which is a representative Korean corpus. By measuring error detection accuracy in POS-tagging result per each tagset candidates with our system, we find that combination of two methods can enhance the accuracy of error detection.
Low-Complexity Scalable Architectures for Parallel Computation of Similarity Measures

Processor array architectures have been employed, as an accelerator, to compute similarity distance found in a variety of data mining algorithms. However, most of the proposed architectures in the existing literature are designed in an ad hoc manner without taking into consideration the size and dimensionality of the datasets. Furthermore, data dependencies have not been analyzed, and often, only one design choice is considered for the scheduling and mapping of computational tasks. In this work, we present a systematic methodology to design scalable and area-efficient linear (1-D) processor arrays for the computation of similarity distance matrices. Six possible design options are obtained and analyzed in terms of area and time complexities. The obtained architectures provide us with the flexibility to choose the one that meets hardware constraints for a specific problem size. Comparisons with the previously reported architectures demonstrate that one of the proposed architectures achieves less area and area-delay product besides its scalability to high-dimensional data.
A Deep Attention Network for Chinese Word Segment

Character-level sequence label tagging is the most efficient way to solve unknown words problem for Chinese word segment. But the most widely used model, Conditional Random Fields (CRF), needs a large amount of manual design features. So it is appropriate to combine CRF and neural networks such as recurrent neural network (RNN), which is adopted in many natural language processing (NLP) tasks. However, RNN is rather slow because of the timing dependence between computations and not good at capturing local information of the sentence. In order to solve this problem, we introduce a self-attention mechanism, which completes the calculation between the different positions of the sentence with the same distance, into CWS. And we propose a deep neural network, which combines convolution neural networks and self-attention mechanism. Then, we evaluate the model on the PKU dataset and the MSR dataset. The results show that our model perform much better.
Kinetosis in autonomous driving

Autonomous driving is a current research and development topic. Mostly, the focus is on the technical implementation including artificial intelligence. However, further aspects like ‘social and ethic aspects’, ‘legal aspects’ and ‘ergonomics and physiological requirements’ should be taken into consideration. The latter point includes the kinetosis (travel sickness), which is often neglected, but plays an import role in autonomous driving: While the car is driving autonomously, passengers in the car are often sitting sideways or backwards, which can cause travel sickness (= motion sickness).
Objectivity, realism, and psychometrics

The aim of this paper is raise and address questions regarding the status of objectivity for the generalized latent variable model (GLVM) in psychometric research, given the conceptual, logical and mathematical problems of circularity, conditional independence, and factor indeterminacy, respectively. The question of objectivity for the model is examined with respect to measurement and realist perspectives. Drawing on insights from measurement and systems dynamics literature, a proposal for a conceptual framework is presented, that integrates: i) inference from the best systematisation; and ii) axiomatic set theory. This conceptual framework, which addresses the whole of a research project, invites specification of the expected relations, conditions, and assumptions which are relevant to the implementation of the GLVM. While this does not eliminate the problems for the GLVM, it provides future researchers with maximal objective information in standardized form, supporting minimization of definitional and instrumental uncertainty, in psychological modelling practices.
Parallel image and video self-recovery scheme with high recovery capability

In this paper, a parallel scheme for self-recovery of tampered images and videos is proposed. Designed technique is based on two methods for generating the digest image: halftoning and a block based scheme, additionally, the implementation of an authentication algorithm was carried out using a block-based method. In order to obtain robustness to the tampering coincidence problem, the proposed scheme embeds multiple versions of the recovery watermark. Finally, during the recovery process, an algorithm of inverse halftoning was applied to improve the quality of the reconstructed image. Proposed scheme was implemented in such a way that each process should be highly parallelizable using GPU, multicore processors. Experimental results have shown that novel framework generates a good quality of the watermarked images and the recovered images. The simulation results using parallel architectures have demonstrated the efficiency of the novel technique when it is implemented in a real-time environment.
Localization of the Vortices and Road Sings in Images

An image in one color is a scalar field. The image is associated with its vector gradient field. The analysis of vector fields with the help of single-length vector templates made it possible to indicate in the image such objects as vortices, Road Signs (RS), Facial Features (FF), and so on. The proposed method of indicating local objects using gradient fields turned out to be highly precision and noise-immunity. The presented advantages of the proposed method recommend it as a promising tool in targets designation problems.
Adaptive Preventive and Remedial Measures in Resolving Cloud Quality of Service Violation

Cloud Computing acts as a paradigm to support on-demand computing services, from applications to storage, manage and processing capabilities. One of the major challenges in delivering and accessing cloud applications is the management of Quality of Service (QoS) and cloud service providers are mandated to adhere to Service Level Agreement (SLA) in providing quality cloud services to the users. The agreement matching is important for both parties to ensure satisfaction and expectation level. This proposed work aims to resolve cloud QoS violation with the implementation of adaptive preventive and remedial mechanisms. Preventive measure such as horizontal scaling is used to optimize the performance of a running cloud service in order to prevent the cloud service to downgrade to QoS violation condition. Remedial action on the other hand, is to provide fault tolerance using replication for faulty cloud service to recover from failure incidents or already violation condition. Experimental results have demonstrated the feasibility and effectiveness of applying horizontal scaling in preventing and replication in rectifying cloud QoS violations based on response time and throughput.
LSM

Unified (hybrid field-size) systolic multiplier over GF(2m) (binary extension field) has attracted significant attentions from research communities recently as it can be used in reconfigurable cryptographic processors. In this paper, we present a novel Low-complexity unified Systolic Multiplier (LSM) design strategy to address the above mentioned challenge. First, a novel multiplication algorithm for unified implementation is proposed first with detailed mathematical derivation. Then, an efficient systolic structure is then presented based on the proposed algorithm (with the help of several mapping techniques). Finally, a thorough complexity analysis is given to verify the superior performance of the proposed design (the proposed structure involves better complexities than the newly reported design).
SDR based secure communication system with novel noise modulated transmission

In many military and law-enforcement covert missions, wireless communication links need to remain undetected. In this paper, a novel spectrum spread technique based on noise modulated (NM) transmission is proposed and a feasibility study was conducted. In NM transmissions a reference pseudo-noise signal is generated and is superposed with the information signal, with a time delay. The pseudo-random noise sequence is transmitted separately and used to recover the signal from the noise modulated information signal. The transmitted information signal is noise like making it difficult to detect or decode by an adversary. If an adversary does discover the transmission, decoding is difficult without the pseudo-random noise sequence and time delay between noise and signal. Conventional NM uses polarized antennas to orthogonally transmit the noise modulated information signal and pseudo-noise signal. However, the two polarized antennas are rarely, if ever, completely isolated in practice making signal recovery difficult if not impossible. In this paper a feasibility study was performed on a novel multi-frequency NM scheme for NM communications with a single polarized antenna. A universal software radio peripheral (USRP) software defined radio (SDR) testbed was used to demonstrate that multi-frequency NM transmission masks a QPSK signal from an adversary and the signal can be successfully recovered by a friendly receiver.
Quantification of Histological Neoplastic Cells Using Digital Image Processing

One of the most important steps to determine the appropriate treatment for patients with breast cancer is the assessment of the hormone receptors status, which is done, most of the times, through the methodology of immunohistochemistry (IHQ). This technique allows the identification of cells with positive hormone receptor; therefore, they are effective to the hormone treatment, which when associated to other therapies, increases life expectancy to the patients. The qualitative and quantitative assessment of these receptors is done based on an analogic form; consequently, the results may vary and may be also subjective. With the technology’s advance, it is possible to automate, besides preparing exams, interpreting them, being beneficial to various patients and achieving more straightforward results. To do so, this research paper proposes the development of an imaging-processing tool for digital histological slides, with the quantification of the nuclei of the neoplastic cells in the histological sections. With the proposed method, we achieved reasonable results that were additionally validated by a pathologist, proving the efficiency of the method (about 5% of difference). The main achievement of such method is to be low-cost if compared with newly expensive technological approaches.
Intrinsic Metric Learning With Subspace Representation

The accuracy of classification and retrieval significantly depends on the metric used to compute the similarity between samples. For preserving the geometric structure, the symmetric positive definite (SPD) manifold is introduced into the metric learning problem. However, the SPD constraint is too strict to describe the real data distribution. In this paper, we extend the intrinsic metric learning problem to semi-definite case, by which the data distribution is better described for various classification tasks. First, we formulate the metric learning as a minimization problem to the SPD manifold on subspace, which not only considers to balance the information between inner classes and inter classes by an adaptive tradeoff parameter but also improves the robustness by the low-rank subspaces presentation. Thus, it benefits to design a structure-preserving algorithm on subspace by using the geodesic structure of the SPD subspace. To solve this model, we develop an iterative strategy to update the intrinsic metric and the subspace structure, respectively. Finally, we compare our proposed method with ten state-of-the-art methods on four data sets. The numerical results validate that our method can significantly improve the description of the data distribution, and hence, the performance of the image classification task.
A stream-sensitive distributed approach for configuring cascaded classifier topologies in real-time large-scale stream mining systems

Stream mining systems have received a great deal of attention in recent years. These systems process incoming data streams from different sources and extract high-level semantic features from them. They do this by passing data streams through an ensemble of classifiers. Owing to dynamic changes in characteristics of the data streams, these classifiers need to be configured dynamically to maximize the performance of the system. As a challenge, different data streams from different sources have different specifications from each other. This causes treating all the incoming data streams identically by a common topology configuration to be not appropriate for an optimal stream mining. Hence, an approach is required which allows each data stream to be processed by consideration of its own specifications. In this paper, by implementing a buffer for each source and using a time-sharing solution, we propose a distributed approach to solve the aforementioned problem for cascaded classifier topologies. We first formally define a utility metric which captures both the performance and the delay of a binary filtering classifier system. We then propose our solution for a base case and evolve it step by step until reaching the most general case for cascaded topologies. We finally test and compare our approach with the state-of-the-art solution on a text detection scenario from the incoming video streams to the system.
A deep learning method for fraud detection in financial systems

Credit card fraud has been a significant challenge ever since forgers have been inventing new ways to steal money. Thus, adaptive fraud detection methods are required to counter the forgers. Deep learning methods have been attractive candidates because of their adaptive nature to detect emerging credit card frauds. In this paper, we propose a deep learning method to adaptively detect credit card frauds by using neural networks. We experimentally evaluated our model with Kaggle dataset. The analyses results show that our methods adaptively detect credit card frauds.
Improved Ozone Pollution Prediction Using Extreme Learning Machine with Tribas Regularization Activation Function

Nowadays, increasing ozone (O3) pollution is becoming a global issue. The increasing of these pollutions has a huge negative impact on human health and also to the ecosystem. In order to reduce the risk of high O3 pollution, an accurate O3 forecasting model should be developed, so that a preventive measure can be taken earlier. Therefore, this study proposed an accurate O3 prediction model using improvement Extreme Learning Machine algorithm based on Regularization Activation Function (RAF-ELM). The experiment conducted by investigating RAF-ELM performance use four types of activation function, i.e., sigmoid, sine, tribas, and hardlim. In this study, RAF-ELM uses single hidden layer feedforward neural networks to predict the air quality index for O3 pollutant based on meteorological variables (Temperature and Wind Speed) and other pollutants (NOx, NO, NO2, CO, PM10, SO2, CH4, NMHC, and THC) in Malaysia using O3 hourly time series data collected at Shah Alam station. It has 107,329 instances recorded from the year 1998 to 2010. The input weight and bias for hidden layers are randomly selected, whereas the best neurons’ number of hidden layer is determined from 5 to 20. The number of neurons (11) with regularization (0.8) using tribas activation function showed the best model. The proposed model has obtained better accuracy performance (0.007999 MSE) and better processing time (2.699 s) compared with conventional MPE. It can be concluded that the proposed algorithm can be used as a good prediction technique for time series data.
CISO – CAREER IS SURELY OVER?

Many Chief Information Security Officers (CISO) wonder if what is next for their career. As the cyber field is relatively new, we do not have enough track record to understand the career path of a CISO. This article is about four different CISOs and their views of where they think they are in terms of their career and where they think their career could go.