Reviewerly/exHarmony huggingface dataset:
Image-Based Automated Hit Detection and Score Calculation on a Steel Dartboard

This paper presents an approach for implementing an automated hit detection and score calculation system for a steel dartboard using a standard webcam. First, the rectilinear field separations of the dartboard are described mathematically by means of line slopes and are than stored. These slopes serve as a basis for later score calculation. In addition, thrown darts have to be detected and the pixel at which the dart cuts the dartboard has to be determined. When this information is known, a comparison is made using the line slopes, allowing the field number of the hit to be detected. The decision for single, double or triple hit is made by evaluating the defined colors on the dartboard. All these functions are then packaged in a Matlab GUI.
La linguistica dello sviluppo sociale

The present paper aims at setting up the features that are necessary to reach a social development based on linguistics or how to turn language into an instrument for social development. There are studies in this regard; however a coherent transdisciplinary overview supported by a proper discourse is missing, as is a proper terminology. This paper envisages to fill this gap. The stakes are high, as is the potential development of many European areas characterized by multilingualism. Social Development Linguistics (SDL) is a transdisciplinary approach to social sciences: from linguistics (sociolinguistics) to anthropology (linguistic anthropology), law (language rights), economy (economics of languages) and history.
Modification and Optimization of Pollards's Factorization $\rho$-Method by Means of Recursive Algorithm of Number Calculation Factorization

Investigations of cryptographic algorithms for today are very actually in connection with cybernetic attacks threat and necessity of information protection at the enterprises of various levels including the strategic appointment. The project implementation of John Pollard's factorization ρ-method in the programming language C++ is presented, which works faster than the standard algorithm by 27%. It can facilitate greatly the deciphering operation and cryptographic analysis of various ciphers such as RSA cipher.
Cross-Platforming Web-Application of Electronic On-line Voting System on the Elections of Any Level

This paper presents the practical implementation of the electronic voting Web-system. Analysis of the developed algorithm was carried out, which was taken as basis in practical implementation of software product. Developed software product can be used for electronic voting at the elections of any level.
Model of Sentiment Analysis with Deep Learning in Social Network Environment

Currently, the digital environment such as social network needs real-time and adaptive security model. Deep learning is becoming increasingly popular for various applications. In this research, we proposed a Dynamic Deep Learning algorithm, dubbed Dynamic Convolutional Neural Networks (CNN). Different from common CNN, it assigns similar signal parts to the same CNN channel and solves signal alignment. Therefore, it can better deal with the problem of data noise, alignment, and other data variations. We achieve an increase in CNN graph’s performance with dynamic k-max pooling model with a benchmark dataset for sentiment analysis.
An Efficient Antiphishing Method to Secure eConsume

In this paper, an antiphishing method, called resource request based phishing discovery (RRPD), has been discussed. By analyzing the resources request characteristics of phishing websites, this method can be used on both the client and server sides. On the client side, client RRPD can be used by the web browser for phishing sites detection. On the server side, server RRPD based on the domain name system dataflow can discover the suspicious phishing sites by analyzing a small amount of web content, which saves bandwidth and computing resources to the utmost. Experimental results demonstrate the effectiveness of the proposed methods.
Vertelas - Automated User-Controlled Vertical Elasticity in Existing Commercial Clouds

Cloud computing has become ubiquitous as a way for users to obtain scalable services. Commercial Infrastructure-as-a-Service (IaaS) providers offer pay-as-you-go Virtual Machines (VM) to users to run their applications. One of the main benefits of using IaaS is the ability to increase or decrease the size of a VM (amount of RAM, cpu, etc.) on demand, based on requirement. This approach is known as vertical elasticity, and results in reduced costs over the lifetime of an application for the user. However, most IaaS providers including Amazon and Google, do not offer vertical elasticity out-of-the-box. Instead, they offer horizontal elasticity, which is the ability to vary the number of VMs based on requirement. In a myriad of scenarios such as non-parallelizable applications and scientific computations, vertical elasticity instead of horizontal elasticity is a must to reduce costs. This paper proposes a framework called Vertelas, that users can utilize within their current cloud based VMs to achieve vertical elasticity and save costs. Vertelas automatically predicts the required resources, and moves the running application to smaller or larger VMs. By utilizing Linux container migrations, no modifications or support on the part of the service providers is required, making the approach provider-agnostic. Experimental evaluations on a real world IaaS provider (AWS), with real world load traces, demonstrate the substantial reductions in costs as opposed to running the application inside a single, fixed-size VM.
Categorising AWS Common Crawl Dataset using MapReduce

Keeping track of websites connected to the Web is an impossible task given the amplitude and fluctuation of new sites being created and those going offline. In this paper we took the task to create a directory by categorising the websites using MapReduce. The dataset about the different websites are collected from the Amazon Web Service (AWS) Common Crawl dataset. Datasets are released at the end of each month and it contains information about all websites that are live during that month. After mining and translating the dataset, each URL are categorised based on its keywords and hosted country, the URLs are then placed in categories such as Art, Education, Shopping etc... After successfully assigning a category to each URL from the dataset, a new categorised dataset is updated with the new URL objects to be uploaded on the cloud. The data about each URL object is presented through a website whereby users are allowed to browse the data by both their categories and directory based domains. In order to evaluate the performance of the categorisation process, three techniques have been used which are Java 8 Streams, Multi-Threading and MapReduce. It has been observed that MapReduce was the most performing in terms of execution time, resource usage on Hadoop Cluster of seven nodes. 23576 (90.3%) URLs from an AWS datasets were successfully categorised and 5767(22.8%) non-English websites were successfully translated.
Cookie Based Protocol to Defend Malicious Browser Extensions

All popular browsers support browser extensions. They are small software module for customizing web browsers. It provides extra features like user interface modifications, ad blocking, cookie management and so on. As features increase, security becomes more difficult. The impact of malicious browser extensions is also enormous. More than 1 million Chrome users got affected by extensions from Chrome store itself. [1] The risk further increases with offline extension installations. The privileges browser extensions have, pave the path for many kinds of attacks. Replay attack and session hijacking are two of these attacks we are dealing here. Here we propose a defence system based on dynamic encrypted cookies to defend these attacks. We use cookies as token for continuous authentication, which protects entire communication. Static cookies are prone for session hijacking, and therefore we use dynamic cookies which are sealed with encryption. It also protects from replay attack by changing itself, making previous message obsolete. This essentially solves both of the problems.
Elementary Iterated Revision and the Levi Identity

Recent work has considered the problem of extending to the case of iterated belief change the so-called ‘Harper Identity’ (HI), which defines single-shot contraction in terms of single-shot revision. The present paper considers the prospects of providing a similar extension of the Levi Identity (LI), in which the direction of definition runs the other way. We restrict our attention here to the three classic iterated revision operators–natural, restrained and lexicographic, for which we provide here the first collective characterisation in the literature, under the appellation of ‘elementary’ operators. We consider two prima facie plausible ways of extending (LI). The first proposal involves the use of the rational closure operator to offer a ‘reductive’ account of iterated revision in terms of iterated contraction. The second, which doesn’t commit to reductionism, was put forward some years ago by Nayak et al.. We establish that, for elementary revision operators and under mild assumptions regarding contraction, Nayak’s proposal is equivalent to a new set of postulates formalising the claim that contraction by $$\lnot A$$ should be considered to be a kind of ‘mild’ revision by A. We then show that these, in turn, under slightly weaker assumptions, jointly amount to the conjunction of a pair of constraints on the extension of (HI) that were recently proposed in the literature. Finally, we consider the consequences of endorsing both suggestions and show that this would yield an identification of rational revision with natural revision. We close the paper by discussing the general prospects for defining iterated revision in terms of iterated contraction.
CompactNets: Compact Hierarchical Compositional Networks for Visual Recognition

CNN-based models currently provide state-of-the-art performance in image categorization tasks. While these methods are powerful in terms of representational capacity, they are generally not conceived with explicit means to control complexity. This might lead to scenarios where resources are used in a non-optimal manner, increasing the number of unspecialized or repeated neurons, and overfitting to data. In this work we propose CompactNets, a new approach to visual recognition that learns a hierarchy of shared, discriminative, specialized, and compact representations. CompactNets naturally capture the notion of compositional compactness, a characterization of complexity in compositional models, consisting on using the smallest number of patterns to build a suitable visual representation. We employ a structural regularizer with group-sparse terms in the objective function, that induces on each layer, an efficient and effective use of elements from the layer below. In particular, this allows groups of top-level features to be specialized based on category information. We evaluate CompactNets on the ILSVRC12 dataset, obtaining compact representations and competitive performance, using an order of magnitude less parameters than common CNN-based approaches. We show that CompactNets are able to outperform other group-sparse-based approaches, in terms of performance and compactness. Finally, transfer-learning experiments on small-scale datasets demonstrate high generalization power, providing remarkable categorization performance with respect to alternative approaches.
The use of Qualitative Data Analysis Software in Brazilian Educational Papers

Software packages that support data analysis in a scientific research can be understood as a mean to assisting the researcher in the treatment, organization and analysis phases, with the main objective of collaborate to the development of research or projects. These tools stand out and are sought by allowing data analysis to be done in a variety of formats (audio, video, image, text)providing collaborative work (two or more research working on the same research project)and the searched data. Considering this reality this article aims to carry out a bibliographic survey in journals evaluated with the Qualis A1, about which software packages the scientific publications - especially in the Brazilian scenario - have chosen to explore in their scientific analyzes in the field of Education. This research presents itself as a qualitative study with its bibliographical and documentary analysis. The dimensions of this work are delimited in the understanding of the reality from the use of software that the researcher chooses to work in i)qualitative, quantitative or mixed, and ii)which software packages could be identified in a researched universe of 121 journals were consulted of which 13 were selected for this study.
Evolution of entanglement in quantum neural network

Abstract This study concerns with the evolution of entanglement in a quantum neural network (QNN) model that is locally in contact with data environments. As a valuable resource, duration of entanglement in quantum systems is extremely important. Therefore, the effect of various initial states on the occurrence or decay of entanglement are examined in the presence of information reservoirs. In this study, central spin model was investigated as a quantum version of neural networks inspired by biological models. The architecture of the model is based on a central spin system with two nodes where the nodes are coupled to independent spin baths. Numerical results show that initial state preparation has a profound effect on the fate of entanglement. The results show that the entanglement lifetime can be adjusted by engineering the reservoir states as well as the initial states of the system of interest. The results can be used to improve the performance of the formation or distribution of entanglement in realistic communication network states.
Demo Abstract: LASK: A Distributed Service Discovery Platform on Edge Computing Environments

We present the LASK protocol and its platform implementation that supports distributed k-Nearest Service Discovery. LASK achieves scalable and locality-aware name-based service discovery and routing for the target nodes avoiding redundant lookup message exchanges across the edge networks.
Investigating the Effectiveness of Client-Side Search/Browse Without a Network Connection

Search and browse, incorporating elements of information retrieval and database operations, are core services in most digital repository toolkits. These are often implemented using a server-side index, such as that produced by Apache SOLR. However, sometimes a small collection needs to be static and portable, or stored client-side. It is proposed that, in these instances, browser-based search and browse is possible, using standard facilities within the browser. This was implemented and evaluated for varying behaviours and collection sizes. The results show that it was possible to achieve fast performance for typical queries on small- to medium-sized collections.
A Web Client Secure Storage Approach in Multi-Cloud Environment

Cloud computing has seen the fastest adoption due to its tremendous benefits to business organizations and individuals. However, ensuring the security level is essential as users often store sensitive information with Cloud storage providers who lack transparency within their policies. Dealing with single Cloud providers is becoming less popular with customers due to the risk of malicious insiders in a single Cloud. In this work, we propose a Web client application supporting secure storage approach primarily to guarantee confidentiality and integrity issues concerning data stored on a multi-Cloud environment. Hence, we present and discuss our Web application framework and its implementation. Besides, we evaluate this proposal by conducting several experiments for the Web application in a real multi-Cloud environment scenario.
A Hybrid Self-constrained Genetic Algorithm (HSGA) for Digital Image Denoising Based on PSNR Improvement

The problem of denoising of images can be traced back to ancient times. Feature preservation remains an integral part of camera manufacturers around the world and drastic improvements have been achieved of late for the same. This work proposes a novel mathematical solution to the problem of image denoising. Images have been denoised using genetic algorithm evolutionary programming based on a self-constrained equational concept. A sample image is added with five different types of noise (Table 1) and they are denoised using existing filters (Table 2) and proposed algorithm (Table 3). The performance for different parametric functions has been compared using Peak Signal-to-Noise Ratio (PSNR) values in decibels. Consistent improvement is noted for five different noise models and compared with existing filters and the results are tabulated and graphically depicted (Figs. 4, 5, 6 and 7).
From Unstructured Data to Narrative Abstractive Summaries (Invited Talk).

To provide with easy and optimal access to digital information, narrative summaries must have a coherent and natural structure. Depending on how a summary is produced, a distinction can be made between extractive and abstractive summaries. Using an abstractive summarization approach, the relevant information (e.g., who? what?, when?, where?,...) could be fused together, leading to the generation of one or more new sentences. However, in order to do this it is necessary to obtain and process the temporal information in a text. A very effective way is the generation of timelines starting from multiple documents so that the generation of summaries is supported by the generated timeline, without losing the relevant temporal information of the texts. In this proposal, a enriched timeline is generated automatically, and the process of generating abstractive summaries is presented using this timeline as a basis [Barros et al., 2019]. Finally, potential applications of the automatic timeline generation would be presented, as for example its application to Fake News detection.
A Robust Watermarking Scheme for Encrypted JPEG Bitstreams with Format-Compliant Encryption

JPEG is a very popular image compression standard in Internet. To simultaneously provide image content protection and copyright protection, in this paper, we provide a robust watermarking scheme for encrypted JPEG bitstreams. The JPEG bitstream is enciphered with a format-compliant encryption to produce another JPEG bitstream with meaningless image content. The watermark embedder can directly embed the message into the encrypted JPEG stream by modifying the quantized DC coefficients without performing decryption or decompression in advanced. As for the watermark extraction, anyone who has the embedding key can extract the watermark from the encrypted watermarked JPEG bitstream and the decrypted watermarked JPEG bitstream. All the watermark embedding and extraction procedures are performed in the compressed domain. We conduct the experiments to verify the effectiveness of the proposed JPEG encryption and watermarking algorithms. Our experimental results also show that the decrypted watermarked images have high visual fidelity, and the watermarking scheme is robust against some common watermarking attacks.
Automatic Identification and Analysis of Basketball Plays: NBA On-Ball Screens

In this study, we present methodologies to process big data in complex multimedia forms for sports analytics. Sports analytics is the management and analysis of data collected from sports games to quantify past results and predict future outcomes. In basketball, the on-ball screen is a dynamic offensive strategy that involves the movement of multiple players and the ball to create an effective shot attempt. It is a fundamental play employed by all teams in the National Basketball Association (NBA), the highest level of competition for basketball. The analysis of a basketball team and its strategies must first include the identification of such plays. With the presence of sophisticated data collection and analysis tools, this paper proposes methodologies to extract, transform, and analyze player motion-tracking data in NBA games to automatically identify the presence of on-ball screens, with 90% sensitivity, an 8% improvement on existing literature.
Natural Language Processing and Futures Studies

Because the input for Futures Studies is to a very high degree formulated as written words and texts, methods which automate the processing of texts can substantially help Futures Studies. At Shaping Tomorrow, we have developed a software system using Natural Language Processing (NLP), a subfield of Artificial Intelligence, which automatically analyzes publicly available texts and extracts future-relevant data from theses texts. This process can be used to study the futures. This article discusses this software system, explains how it works with a detailed example, and shows real-life applications and visualizations of the resulting data. The current state of this method is just the first step; a number of technological improvements and their possible benefits are explained. The implications of using this software system for the field of Futures Studies are mostly positive, but there are also a number of caveats.
VLSI Architecture Design of 9/7 Discrete Wavelet Transform for Image Processing

In image processing, transform coding de-correlates images to pre-condition them for efficient compression. In this work, we propose VLSI architecture design of a hardware-efficient 9/7 Discrete Wavelet Transform (DWT). The architecture takes advantage of Canonical Signed Digit (CSD) and Distributed Arithmetic (DA) to represent and optimally distribute co-efficients to reduce the number of adder and shift registers. In addition, the co-efficient multiplication also exploits the horizontal and vertical redundancy in the architecture to reduce the hardware computational complexity. The result is a filter-based design, exploiting hardware path of architecture using CSD coefficients, which finds minimum realization. The proposed architecture is simulated using Verilog Hardware Description Language (HDL). A comparison with other architectures of 9/7 DWT shows a 18.75% reduction in hardware. The result is a hardware-efficient architecture, which provides a low-power solution for image and signal processing applications.
Towards a Maturity Model for Cloud Service Customizing

In last years, more and more cloud providers are making great efforts to offer personalized services that fully match customers’ needs. However, it is not always easy because of the maturity level that requires their customization capabilities in order to design, implement, operate and improve personalized services. Furthermore, there is a lack of tools to help cloud providers to understand what is the current maturity level of their customization processes and the path to improve them. In this context, this work presents our progress for building a maturity model for the customization of cloud services. The model is proposed from a literature review in the area and two interviews to industry experts. The model, that includes two dimensions (customization capabilities and maturity levels) aims at helping researchers to develop new contributions in this research area and practitioners to develop their customization capabilities.