Reviewerly/exHarmony huggingface dataset:
Absolutely continuous copulas with prescribed support constructed by differential equations, with an application in toxicology

A new method for constructing absolutely continuous two-dimensional copulas by differential equations is presented. The copulas are symmetric with respect to reflection in the opposite diagonal. The support of the copula density may be prescribed to arbitrary opposite symmetric hypographs of invertible functions, containing the diagonal. The method is applied to toxicological probit modeling, where new compatibility conditions for the probit parameters are derived.
Convolutional neural networks Memory optimization Inference with Splitting Image

In this paper, we propose a method to reduce the memory requirement of Convolutional Neural Networks (CNN) in the inference phase. Before feeding an input image into the CNN model, input image will split evenly to several sub-images and feed them into models respectively and combine the output after a certain layer.
Towards Large Scale Image Retrieval System Using Parallel Frameworks

Recently, the increasing use of mobile devices, such as cameras and smartphones, has resulted in a dramatic increase in the amount of images collected every day. Therefore, retrieving and managing these large volumes of images has become a major challenge in the field of computer vision. One of the solutions for efficiently managing image databases is an Image Content Search (CBIR) system. For this, we introduce in this chapter some fundamental theories of content-based image retrieval for large scale databases using Parallel frameworks. Section 2 and Section 3 presents the basic methods of content-based image retrieval. Then, as the emphasis of this chapter, we introduce in Section 1.2 A content-based image retrieval system for large-scale images databases. After that, we briefly address Big Data, Big Data processing platforms for large scale image retrieval. In Sections 5, 6, 7, and 8. Finally, we draw a conclusion in Section 9.
Art Inside Technology

First, technologies (photography, tape recorder, telephone, radio, television) and, afterwards, neo-technologies (total digitalisation, Internet, mobile phones) have created the conditions for overcoming the arts towards what the author calls “technological sublime”. Within this new dimension, not only the category of art reaches its own decline, but the very aesthetic dimension experiences a profound transformation of its own essence, exercising its ancient function and, yet, at the same time, assuming a novel anthropological character. The author supports his thesis by founding it, as he has always done before, on the works of a certain number of artists, whom he considers as the most meaningful of our age.
Generative Adversarial Network for Identifying Authors of Traced Images

Art forgery is one of the important issues in art, but the general approaches using well-known image datasets are likely to fail to identify real forgeries. This is because the distributions of the well-known image datasets are very different from that of a dataset that includes the pictures created by professional counterfeiters. However, there are few datasets specialized for art forgery detection. In this research, we constructed a dataset for author identification of drawings. To create the drawings, we traced a few original sketches on top of semi-transparent papers. The traced images look more similar to the originals than typical forgery artwork images do, and it implies that it is more difficult to identify the authors of the traced images. We also suggest a new generative adversarial network model for author identification. We approach the problem by maximizing mutual information between an author and his traced artwork image. We compared our results to those obtained by the baseline convolutional neural networks. The experimental results showed that the suggested approach produced more convincing results than other approaches.
Linguistic Classification Using Instance-Based Learning

Girdhar, Rhythm Nayak, Priya S. Prabhu, Shreekanth M.Traditionally, linguists have organized languages of the world as language families, such as Indo-European, Dravidian and Sino-Tibetan. Within the Indo-European family, they have further organized the languages into sub-families such as Germanic, Celtic and Indo-Iranian. They do this by looking at similar-sounding words across languages and the commonality of rules of word formation and sentence construction. In this work, we make use of computational approaches that are more scalable. More importantly, we contest the tree-based structure that language family models follow, which we feel is rather constraining and comes in the way of the natural discovery of relationships between any two languages. For example, the affinity Sanskrit has with Irish, Iranian or English and languages across Indo-European languages is better illustrated using a network model. Similarly, Indian languages have inter-relationships that go beyond the confines of the Indo-Aryan and Dravidian divide. To enable the discovery of inter-relationships between languages, in this paper, we make use of instance-based learning techniques to assign language labels to words. Our approach comprises building a corpus of words and then applying clustering techniques to construct a training set. Following this, the words are vocalized and classified by making use of a custom linguistic distance metric. We have considered seven Indian languages, namely Kannada, Marathi, Punjabi, Hindi, Tamil, Telugu and Sanskrit. We believe our work has the potential to usher in a new era in linguistics in India.
Sistema de cerca inteligente para ocorrências policiais em smart cities

The following work proposes a solution in the form of an algorithm for a systemto assist decisions from public security agencies at the city of Volta Redonda, in the stateof Rio de Janeiro. The evolution of the city of Volta Redonda from an average city intoa smart city is a motivation for the development of the proposed system. Three versionsof an algorithm capable of inputing geolocation data from a police incident and providingthe best police vehicle to handle the incident, as well as the locations where other vehiclesshould be positioned in order to create a blockade around the incident were developed.As a solution, the algorithm that uses the Google Maps API to obtain real-time trafficdata in order to present the best vehicles and blockage points according to the situationof the roads at the time of the system’s execution was selected as the best one.
Online proximal gradient for learning graphs from streaming signals

We leverage proximal gradient iterations to develop an online graph learning algorithm from streaming network data. Our goal is to track the (possibly) time-varying network topology, and effect memory and computational savings by processing the data on-the-fly as they are acquired. The setup entails observations modeled as stationary graph signals generated by local diffusion dynamics on the unknown network. Moreover, we may have a priori information on the presence or absence of a few edges as in the link prediction problem. The stationarity assumption implies that the observations' covariance matrix and the so-called graph shift operator (GSO - a matrix encoding the graph topology) commute under mild requirements. This motivates formulating the topology inference task as an inverse problem, whereby one searches for a (e.g., sparse) GSO that is structurally admissible and approximately commutes with the observations' empirical covariance matrix. For streaming data said covariance can be updated recursively, and we show online proximal gradient iterations can be brought to bear to efficiently track the time-varying solution of the inverse problem with quantifiable guarantees. Specifically, we derive conditions under which the GSO recovery cost is strongly convex and use this property to prove that the online algorithm converges to within a neighborhood of the optimal time-varying batch solution. Preliminary numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network.
MEC S-box based PRESENT Lightweight Cipher for Enhanced Security and Throughput

Cyber Physical Systems (CPS) is essential for the integration of the physical world with the virtual electronic world. The only way to provide security to these constrained environment applications is through Lightweight Cryptography. To provide stronger security, with elevated performance and less power consumption, two PRESENT architectures are proposed in this paper. The first architecture provides the option to choose one of the three MEC S-boxes, whereas the second architecture makes use of a single MEC S-box to provide better security and increased performance for encryption and decryption processes. The Standard S-box is replaced with MEC S-box, which has its own advantages, such as, less power consumption, linear time and constant space complexity. To analyse the various parameters of the proposed architectures, they are synthesized using Xilinx Virtex-7 FPGA, in Xilinx Vivado IDE. With respect to Strict Avalanche Criterion (SAC), the standard usage of S-box, nearly gives 50% SAC whereas, at least two orders of MEC S-boxes used in the proposed architectures give more than 50% SAC for the entire algorithm, thereby increasing the security of the whole process. The results depict that the proposed architecture provides a throughput of 1564.02 Mbps whereas with a lesser power consumption.
Knowledge Management System Design of the Security Command Center in A Financial and Banking Company with Contingency Factors and Sprint Design Methodology

Knowledge is a precious asset in an organization. If it is managed adequately, it affects the organization's business process successfully. The current business processes at PT XYZ's Security Command Center are still deemed ineffective in providing command and service to all employees. This research aims to develop the design and evaluation of a knowledge management system using the Design Sprint approach with a case study in PT XYZ's Security Command Center. This research was conducted using the Fernandez methodology to identify the needs of the organization's KMS features and using the Design Sprint methodology to identify the system requirements of KMS. System mockups are shown to Security Command Center employees to evaluate the system requirement. The results of this research indicate that the Design Sprint methodology carried out in only a short time of 5-days can help obtain the complete system requirements. Besides that, the method also shows that the systems built can be as close as the user's expectation. This study can be adapted to other organizations that need a security command center in order to help the organization's operational activities run smoothly.
Behind the Veil of Decentralization: Analyzing Blockchain Frames and Sponsors in US News

In recent years, “blockchain” has emerged as an industry buzzword, a technology that can solve pressing problems in economic inclusion, democracy, humanitarian aid, sustainability, and supply chain management among other things. Whether effective or not, this little-understood technology is championed as a way to change socio-economic power structures and democratize and decentralize established institutions like the financial system and government. However, these claims are often unsupported. The hype generated in the news and popular discourse about this technology often obscures questions of whether power relations are actually changing and how. Understanding the visible media frames as a proxy for the power contests that went into shaping discourse, this paper critically examines US news media coverage of blockchain between 2013 - 2018. It examines the news frames used to talk about blockchain, the sponsors of these frames, and how these factors have changed over the five-year period. The paper finds that blockchain-related news coverage is framed in six general ways: Disruptive, Harnessing, Skepticism, Community, Understanding, Menace. These frames, especially the disruptive and harnessing frames are mainly sponsored by big banks (like J.P. Morgan), big technology (like IBM and Microsoft), and Silicon Valley investors. Using this data, the paper argues that the narrative of blockchain as a democratizing/decentralizing technology is mainly pushed by these established institutions and that it is a smokescreen to prevent a critical examination of the use of the technology.
Deep Neural Network Based Video Source Recognition

To enable network service providers to better allocate network resources, a variety of traffic identification algorithms have been developed, but few new features with stronger differentiation power are reported, causing unsatisfactory False Positive Rate (FPR) performance in existing methods. To deal with this issue, this paper presents a new method for recognizing streaming video sources in encrypted tunnels based on deep learning (DL). A modified Kolmogorov-Smirnov Discretization (MKSD) algorithm that produces a continuously differentiable histogram curve, is developed to train a deep neural network (DNN) model for optimal histogram bin placement. Experimental validation with simulated real network complexity (traffic combinations in encrypted VPN tunnels) demonstrates that the MKSD algorithm can improve True Positive Rate (TPR) for system identification, while reducing FPR. Compared with existing methods, the proposed method shows higher identification accuracy of video sources in single and multi-stream scenarios.
Ontology Authoring from Relational Database: A Model Based Approach

The ontology authoring is a fundamental task in the Semantic Web. This process enables the domain expert to develop ontologies with the help of dedicated tools. This article presents an approach for building OWL ontologies from relational databases based on Model Driven Engineering (MDE). The proposed approach consists of two phases: (1) Preprocessing phase and (2) Transformation phase. The first one consists of creating an input model from the database which must conform to its meta-model. The second phase takes this model as input and transforms it into an OWL file by executing a set of mapping rules written in Atlas Transformation Language (ATL). The transformation process is done at a higher level of abstraction; it does a matching between the source meta-model elements (Database) and the target meta-model elements (OWL). We have concretized our approach as the DB2OWLOntology tool, and we have evaluated it with a set of databases. The obtained results are encouraging and show the efficiency of the proposed approach.
BSR: A Balanced Framework For Single Image Super Resolution

The reconstruction effect of single image super resolution (SISR) has been greatly improved over the traditional statistic and feature-based methods since the wide application of deep convolutional neural networks (DCNNs). Most recent studies mainly focused on the complexity of the neural network models and the stability of the training process without paying much attention to imbalance problems in the fields of super resolution. In this paper, we study three imbalance effects: sample imbalance, feature imbalance, and object function imbalance. A novel framework, which is called Balanced Super Resolution (BSR), is thus proposed to tackle these issues. Specifically, we propose a random filter sampling algorithm to form balanced training sets during batch training. Meanwhile, a feature mapping group, which is a kind of residual structure, is introduced to forward various groups of low-level information to high-level. A light spatial attention mechanism is also proposed to improve the effectiveness of residual features. Furthermore, we study the object functions in traditional SISR networks and deploy a hybrid L1/L2/Lp structure that favors visually-stable SR output. The proposed design achieves persistently better image quality than state-of-the-art DCNN methods in both subjective and objective measurements.
Sensor Fusion-based Cooperative Human Detection Scheme using MAVs in Indoor Environment

Human detection has been a challenging area of research with its main applications such as rescue, surveillance, and defense. Many researchers have endeavored to improve accuracy in human detection using multimodal sensor fusion, which analyzes and correlates data from multiple sensors. Recently, Micro Aerial Vehicles (MAVs) have been adopted in safety and security domains due to their high mobility, but they could not be equipped with multiple sensors under strict weight limits. Indeed, a MAV with only one sensor has difficulty in detecting human with high accuracy in dynamic environment. In this paper, we propose a novel cooperative human detection scheme based on multimodal sensor fusion. The proposed scheme incorporates multiple modalities including appearance, motion, and voice. We implement a prototype system consisting of multiple MAVs enabling real-time processing on resource-constrained hardware. To verify the feasibility of the proposed scheme, we perform cooperative human detection experiments with multiple MAVs in indoor environment.
ArchPython

Dynamically typed languages provide several resources for developers, such as dynamic invocations and constructs. However, such resources combined with short deadlines, conflicts in requirements, or technical difficulties may increase the number of code decisions that go against the planned architecture, leading to the phenomenon known as architectural erosion. Even though Python is the 3rd most used programming language, there is no tool that allows developers to monitor the architecture of their systems. This is possibly justified by the complexity to infer types since the same variable can assume different types at run time. Facing such challenge, this article proposes ArchPython, the first complete architectural conformance and visualization tool for Python systems. In a nutshell, developers specify the architecture of their systems in a simple and natural way using JSON files and ArchPython takes care of the rest. Automatically, the tool infers types (Jedi + propagation heuristics) and detects architectural violations (divergences, absences, and even alerts). In addition to a JSON-based textual report, the tool also provides two ways of visualizing architectural violations (graph and DSM).
Supporting visual analytics in decision support system

Visual Analytics is an emergent research field, which uses the machinery power to process large data volumes, as well as human reasoning to discover new insights hidden in enterprise data. Decision Support Systems can be enhanced by these features and thus support better decision-making within organizations. However, there is not a clear understanding of ongoing works that investigate the interplay and commonalities of these two topics. In this context, this work reports the results of a systematic mapping study carried out to provide an overview of ongoing studies on the software architecture of the decision support system to support visual analytics features. Study findings clearly showed that even though visual analytics features might be incorporated on software architectures, there is still a need for further research clarifying means of modelling and representing these features on architectural styles.
Can Knowledge Enhance Reading Comprehension? An Integrated Approach with Semantic Lexicon

The machine reading comprehension task (MRC) requires the model to answer thequestions based on a piece of context. Over the past few years, more and more powerful models have been proposed based on various deep learning techniques. The MRC models based on deep learning is powerful and effective; however, most of them are focusing on changing the neural network structure. Apart from improving on the deep learning architectures, word embeddings are also essential elements for question answering systems and should not be neglected. Even a small improvement in word representation can lead to substantial performance differences in question answering task. The proposed approach comprises two modules that specialize the semantic representation of word representation and then pipe them to use in MRC models. Fundamentally, pre-trained vectors are retrofitted based on semantic lexicons (PPDB, WordNet, and FrameNet) beforehand. Our experiments on the Stanford Question Answering Dataset (SQuAD) reveal that integrating both a single and combined lexicon knowledge yields improvements over the only use of pre-trained embeddings.
The Role of Blockchain Technology in E-Govemment Capability: Literature Review

E-government implementation is arduous. When a region has met the requirements to implement e-government, new challenges will arise. The advent of the IT revolution forces governments and businesses to provide more effective and safe online services. Research of Dien Novita [7] revealed that the e-government implementation in South Sumatra still suffers from several problems, and as a result, the public fails to comprehend the usage and benefits of the applied e-government in the province. To overcome this problem, there is a need for new technology to improve efficacy in the use of e-Government. The solution is the utilization of Blockchain technology. Five Blockchain capabilities can be applied to enhance the effectiveness of E-Government. They are Simplification, Digital verification, Data sharing, public security, and low cost. This study suggests a framework or concept of better strategies and policies for the development of sustainable E-Government in the province of South Sumatra.
Cooperative Random Forest for Privacy-Preserving IoT Devices

Recently, various Internet of things (IoT) devices have become widely used in our daily lives and made houses and cities easier to live in. This paper proposes a machine learning scheme to take advantage of IoT devices. The proposed scheme realizes cooperation between devices to improve their performance, rather than learning independently. However, it is difficult to share local data directly because those data may contain private information, such as a picture with a user's face or lifelog data. Therefore, this paper provides a way of preserving privacy in interconnected IoT devices by sharing only learners from each device without sharing the original data directly. The proposed algorithm shares decision trees locally learned at each device and utilizes a random forest as a way of combining them together.
A Fast Method to Detect Collision for Five-axis Machining with GPU

The applications of five-axis machining have been used widely recently. Owing to the expensive cost of machine tools, how to detect the collision in real time has become a critical issue. Indeed, in order to ensure that G-codes will not result in the collision, the developers may use some tools before the stage to process the five-axis machine tool on off line. Moreover, to reduce long execution time on off line, we propose a parallel method to remedy it in this paper. The objective of the proposed approach aims at improving the performance to detect collision in parallel by utilizing the functions of a GPU (Graphics Processing Unit).We address the issue above by inducing six separating axis in plan and 11 separating axis in non-plan for two triangle meshes. Then we propose a parallel approach by implementing a CUDA ( Compute Unified Device Architecture ) program based on a GPU. Finally, with our domain knowledge and experiences, we attempt to optimize the proposed work with loop unrolling and prefetching techniques to improve performance.. The result shows that our work is very efficiently by using the two techniques.