Reviewerly/exHarmony huggingface dataset:
Big Data Analytics for Maintaining Transportation Systems

Big Data Analytics (BDA) is becoming a research focus in transportation systems, which can be seen from many projects within the world. By using sensor and Internet of Things (IoT) technology in transportation system, huge amount of data is been generated from different sources. This data can be integrated, analyzed and visualized for efficient and effective decision-making for maintaining transportation systems. The key challenges that exist in managing Big Data are the designing of the systems, which would be able to handle huge amount of data efficiently and effectively and to filter the most significant information from all the collected data. This chapter will draw attention towards the present scenario and future projections of big data in transportation systems. It also presents big data tools and techniques and then presents one brief case study of BDA in each type of transportation system. In this chapter, a broad overview of Big Data definitions, its history, present, and future prospects are briefed. Several tools and technologies especially for transportation are pointed out for maintaining transportation systems. At the end of the chapter, a definitive case studies on each transportation area is demonstrated.
Over-the-Shoulder Attack Resistant Graphical Authentication Schemes Impact on Working Memory

Alphanumeric passwords are the most commonly employed authentication scheme. However, technical security requirements often make alphanumeric authentication difficult to use. Researchers have developed graphical authentication schemes to help strike a balance between security requirements and usability. However, replacing characters with pictures has introduced both negative (security vulnerabilities) and positive (memorability benefits) outcomes. We are aware of the noteworthy long-term memory advantages of graphical passcodes, but little is known about the impact on users’ limited working memory resources. Authentication is always a secondary task, which probably consumes working memory. This pilot study examines the impact graphical authentication schemes (Convex-Hull Click; Use Your Illusion; What You See is Where you Enter) have on working memory (Verbal; Spatial; Central Executive). Our findings suggest that graphical authentication schemes impact on working memory varies. This work shows that further investigation is needed to understand the complex relationship between scheme design and working memory.
A Case-Based Reasoning Approach for Facilitating Online Discussions

In online discussion platforms, human facilitators are introduced in order to facilitate the discussions to proceed smoothly and build consensus efficiently. However, problems such as human bias and scalability are becoming critical with increasing sophistication of these online discussion platforms. In order to address these problems, online discussion facilitation support becomes more and more essential. Towards this end, in this paper, a novel case-based reasoning (CBR) based online discussion facilitation support approach, which consists of a case definition method and a case retrieval algorithm, is proposed to support online facilitation in large-scale discussion environments. The proposed approach models the online discussions using the issue based information system (IBIS) discussion style, where complex problems are modelled as a conversation amongst several stockholders. In the proposed approach, discussion cases are generated and retrieved based upon the structure features of their discussions. The experimental results show the proposed discussion case generation approach is able to reflect more precise discussion features than those approaches that are based only on the quantitative features, and the ability of the proposed case retrieval algorithm to retrieve the most similar case from the case base.
Learning Visual Dictionaries from Class-Specific Superpixel Segmentation

Visual dictionaries (Bag of Visual Words - BoVW) can be a very powerful technique for image description whenever exists a reduced number of training images, being an attractive alternative to deep learning techniques. Nevertheless, models for BoVW learning are usually unsupervised and rely on the same set of visual words for all images in the training set. We present a method that works with small supervised training sets. It first generates superpixels from multiple images of a same class, for interest point detection, and then builds one visual dictionary per class. We show that the detected interest points can be more relevant than the traditional ones (e.g., grid sampling) in the context of a given application—the classification of intestinal parasite images. The study uses three image datasets, with a total of 15 different species of parasites, and a diverse class, namely impurity, which makes the problem difficult with examples similar to all the remaining classes of parasites.
New Environments for the Evaluation of Smart Living Solutions

In recent years, the evolution of the technology and its adoption by the citizens has made possible the inclusion of new tools in the innovation processes. These tools have allowed the development of better products and services and the reduction of the time to market of these solutions for the benefit of all end users. In this context, the concept of Living Lab has emerged, as an open innovation ecosystem where stakeholders, including professionals, developers and end users, can cooperate on solutions to address relevant challenges using co-creation and evaluation methodologies. Living Labs can support and enhance the innovation process throughout the different phases of the value chain, and they can also act as the connectors between the needs (the users) and the offer (the suppliers). This chapter presents the Living Labs as the novel instruments for evaluating, assessing and validating innovative products, solutions or services in the particular domain of smart living environments.
Ultrasound Image Denoising Based on Fuzzy Logic

With the improvement of medical conditions, doctors can diagnose patients with ultrasound images, but the ultrasound images currently formed always have a certain proportion of speckle noise, which will interfere with the doctor's diagnosis. Therefore, the research on the removal of noise has certain significance. In this paper, we propose an image denoising model based on fuzzy logic. Firstly, the model assigns weights to the image by means of the fuzzy membership function, obtains the required weight table, and then traverses the entire image. The role of the image is pre-smoothed, then a 5x5 convolution template is defined according to Grunwald-Letnikov (G-L), and the resulting smooth image is finally convolved with the template to obtain the final denoised image. By comparing the evaluation index with the current mainstream algorithm model, the validity of the model is objectively reflected.
Recaspia: Recognizing Carrying Actions in Single Images Using Privileged Information

Many approaches for action recognition focus on general actions, such as "running" or "walking". This work presents a method for recognizing carrying actions in single images, by utilizing privileged information, such as annotation, available only during training, following the learning using privileged information paradigm. In addition, we introduce a dataset for carrying actions, formed using images extracted from YouTube videos depicting several scenarios. We accompany the dataset with a variety of different annotation types that include human pose, object and scene attributes. The experimental results demonstrate that our method, boosted sample averaged F1 score performance by 15.4% and 4.15%, respectively, in the validation and testing partitions of our dataset, when compared to an end-to-end CNN model, trained only with the observable information.
Parallel DES with Modified Mode of Operation

In this paper, we tried toChaudhari,Kinjal model parallel version of Data Encryption Standard (DES) by means of the modified mode of operation. Block size specifications followed by DES havePrajapati,Payal been adopted too. Design configuration has shown serialized intermediate key generation and parallelized operations on plaintext blocks, which indicate faster encryption as compared to the standard one. We have performed various statistical tests suggested in the literature for conforming security of the proposed model. Also, case studies on potential vulnerabilities have been shown. Comparative analysis with the standard DES has revealed that the proposed parallel DES is more secure due to inter-block data dependency of keys and serialized decryption besides faster encryption. We have concluded our work with future directives.
Linguistic Resources Construction: Towards Disfluency Processing in Spontaneous Tunisian Dialect Speech

The Tunisian Dialect (TD) is an under-resourced language which lacks both corpora and Natural Language Processing (NLP) tools despite being increasingly used in spoken and written forms. In this paper, we presented our endeavour to build linguistic resources for TD in order to process disfluencies. First, we created the Disfluencies Corpus from Tunisian Arabic Transcriptions (DisCoTAT), which is a set of manual transcriptions with several disfluency phenomena. Also, we constructed the Tunisian Dialect Wordnet (TD-WordNet) from existing TD lexicons to annotate words with morpho-syntactic tags. Then, we developed the Disfluency Annotation Tool (DisAnT) in order to annotate DisCoTAT. DisAnT provides two levels of annotation: morpho-syntactic tagging and disfluency annotation.
Estimating Physical Activity Intensity And Energy Expenditure Using Computer Vision On Videos

Estimating physical activity (PA) intensity and energy expenditure (EE) is a problem that typically requires the use of wearable sensors such as a heart rate monitor, or accelerometer. We investigate the accuracy of a computer vision system using videos recorded from a pair of wearable video glasses to estimate PA strength and EE automatically using age, gender, speed, and activity cues. Age and gender are obtained using the Deep EXpectation network, while activity is estimated from joint angles and movement speed. We also present results on a study of 50 participants performing four different activities while measuring corresponding features of interest such as height, weight, age, sex, and ground truth EE and PA strength data collected via accelerometer. We present both the results of each computer vision subsystem and overall accuracy of the PA strength estimation (89.5%) and the average EE difference (1.96 kCal/min).
Extraction of Knowledge and Processing of the Patent Array

In modern society, science and technology are developing with great speed. This leads to a massive accumulation of scientific and technical texts, their data structures are becoming more complex. There is a problem: how to deal with the growing volume of scientific and technical texts, as well as accurately determine their technical content, technical links, and trends. Traditional text analysis methods are based on the analysis of words and phrases to describe technological development (for example, the TFIDF method). One of the difficult problems that have arisen is how to integrate natural language processing, semantic analysis, and analysis of technology development trends, and how to perform a further in-depth study of the text to find hidden information about the development of technologies. Often, the development of technology is so fast that standardized technical terms are absent within even one technology, which makes it difficult to analyze textual information based on keywords. Therefore, in the framework of this study, it is proposed to use the machine learning method for SAO structures to solve the above problems. SAO (subject, action, object) structures are semantic structures that most accurately represent the semantic information of their textual data.
Spherical Panoramic Photo Shooting and Virtual Reality Demonstration of a Crime Scene

The relevance of the research topic is associated with a small number of existing innovations that are used at the stage of inspecting the scene during the investigation of crimes. Most of the new scientific methods and tools have been developed and are used in the examination of evidence in the framework of forensic examination, for example: DNA analysis, computer image analysis, forensic facial reconstruction, etc. There is a time lag in the scientific support of the photo shooting of the crime scene, the evidence found, their evaluation and visual demonstration in court, which negatively affects the speed and quality of the criminal investigation. The article provides a comprehensive analysis of the capabilities of spherical photo technologies of a crime scene and building a visual step-by-step illustration using virtual reality devices. The authors have developed guidelines for capturing the scene of the incident with modern panoramic photo and video cameras, as well as visualizing the captured information using virtual reality devices in crime investigation activities. The use of methods of fixing a crime scene with panoramic photo and video technologies, as well as visualization of the captured information using virtual reality devices will be interpreting and presenting evidence, as well as improve the objectivity and quality of criminal investigation.
Nonlinear representation of the confidence region of orbits determined on short arcs

The total number of active satellites, rocket bodies, and debris larger than 10 cm is currently about 20,000. If all resident space objects larger than 1 cm are considered, this number increases to an estimate of 700,000 objects. The next generation of sensors will be able to detect small-size objects, producing millions of observations per day. However, due to observability constraints, long gaps between observations will be likely to occur, especially for small objects. As a consequence, when acquiring observations on a single arc, an accurate determination of the space object orbit and the associated uncertainty is required. This work aims to revisit the classical least squares method by studying the effect of nonlinearities in the mapping between observations and state. For this purpose, high-order Taylor expansions enabled by differential algebra are exploited. In particular, an arbitrary-order least squares solver is implemented using the high-order expansion of the residuals with respect to the state. Typical approximations of differential correction methods are then avoided. Finally, the confidence region of the solution is accurately characterized with a nonlinear approach, taking advantage of these expansions. The properties and performance of the proposed methods are assessed using optical observations of objects in LEO, HEO, and GEO.
Is Public Cloud Computing Adoption Strategically the Way to Go for All the Enterprises?

As cloud computing is moving from hype to reality, certain best practices and broad trends are emerging when it comes to private cloud vs. public cloud deployment debate. Cost, control and capabilities are key factors in decision making of enterprises on whether to migrate their existing applications on public or private cloud infrastructure. Over the first decade of cloud computing adoption, most of the enterprises began with over-conservatism towards public cloud, based mainly on concerns of steady state workload cost and control. The majority of enterprises have now settled in to middle ground approach with hybrid cloud based on their expectation of cost benefits and sustained control from private infrastructure for some of their applications. For new applications development, public cloud is increasingly favored for allocation of applications. Private cloud platforms e.g. VMware and Openstack have become easier to use and deploy. The private cloud platforms have closed the gap in ease of rapid application development as container platforms and cloud management have advanced in offering orchestration and capabilities competitive with public cloud services. However, public cloud platforms are suited for firms willing to sacrifice long-term application portability for less operational workload and short-term quicker development. The enterprises are keeping many of their applications on private infrastructure for security, compliance or auditing reasons. The enterprises should not oversimplify their cost considerations while identifying the applications that can run on either public or private cloud as this may leave risks of a lot of cost-saving opportunities on table. The key to cost analysis is to determine first, how well one can profile infrastructure capacity needs and second, how efficiently one can operate their private infrastructure.
Hybrid Route Recommender System for Smarter Logistics

The condition of road surface has a significant role in land transportation. Due to poor road conditions, the logistics and supply chain industry face a drastic loss in their business. Unmaintained roads can cause damage to goods and accidents. The existing routing techniques do not consider factors like shock, temperature and tilt of goods etc. but these factors have to be considered for the logistics and supply chain industry. This paper proposes a recommender system which target management of goods in logistics. A 3 axis accelerometer is used to measure the road surface conditions. The pothole location is obtained using Global Positioning System (GPS). Using these details a hybrid recommender system is built. Hybrid recommender system combines multiple recommendation techniques to develop an effective recommender system. Here content-based and collaborative-based techniques is combined to build a hybrid recommender system. One of the popular Multiple Criteria Decision Making (MCDM) method, The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for content based filtering and normalised Euclidean distance and KNN algorithm is used for collaborative filtering. The best route recommended by the system will be displayed to the user using a map application.
Regulating Blockchain Smart Contracts with Agent-Based Markets

The fast pace in blockchain development introduced the concept of Smart Contract as a way to deploy digital contracts as pieces of code on the blockchain network. Smart contracts differ from the traditional ones in many aspects related to their automated execution. Still, there are many open issues, such as legal frameworks and regulatory aspects. The study of agreement technologies and agent-based market systems have already dealt with some of these issues and can help us discuss solutions for the future. In this paper, we present an overview of the agreement pipeline, considering regulation-enabled systems and compare the implementation of auction agreements purely on the blockchain with a hybrid approach where markets are active agents that can ensure regulation outside the contractual phase and contract execution runs on blockchain. We have compared the cost of failing negotiations, that is always charged in the blockchain case.
Cloud VM Provisioning Using Analytical Performance Models

When an application deployed in the cloud faces changing workload, the services of the application need scaling up or down in response. The services run on Virtual Machines (VM) or container instances. Application Providers (APs) decide on how the applications are scaled through VM provisioning and through the placement of the services on those VMs. Various drivers guide this decision making. Application performance and cost are two such drivers. In this paper, we answer the question of how APs can meet the performance constraints of their applications while minimizing the cost of the running VMs. A VM provisioning problem is formulated which expects to meet mean response time constraints and minimize the cost, where VM-types having different cost rates are used. The proposed solution is based on genetic algorithm and bottleneck strength value. For the case study, a decision maker is implemented for a web application. The proposed solution is compared against an exhaustive search, a simple genetic algorithm and a random search. It is shown that our solution is able meet response time constraints with near optimal minimization of cost. The solution also results in better cost than random search and the plain genetic algorithm solution at the expense of slightly longer runtime.
Public Cloud Kubernetes Storage Performance Analysis

Public cloud solutions offer a lot of possibilities for application configuration. The well-chosen architecture and configuration are important for the correct and efficient application run and affect performance and also user friendliness. If application runs on top of Kubernetes cluster on public cloud solutions (e.g. Amazon Elastic Container Service for Kubernetes, Google Kubernetes Engine, Azure Kubernetes Service) you have to, among other things, also decide what data storage to use and this decision should be based on the workload type and requirements for other application and storage parameters. It is needed to consider whether your application needs to store data and, in case of failure, data are still needed (stateful application) or data are not needed in the future with a new container (stateless application). The storage is connected to containers via volumes and there are a lot of types of Volumes which can be mounted to containers. There can be used volumes for block storage through dynamic provisioning but only a few technologies for public cloud solutions, which can be used. However, it is not easy to say which technology is the best or to use some simple test to get this result or a similar one. The main analysis and tests have been done on Microsoft's Azure Kubernetes Service where have been tested these storage back-ends: AKS native storage class, AWS cloud volume mapped into the instance (via Azure hostPath with attached Azure managed disk), OpenEBS, Portworx, Gluster with Heketi and Ceph with Rook.
Abstract Retrieval over Wikipedia Articles Using Neural Network

In this article, we propose a neural network model to create a Wikipedia article summarization for each query to allow users to find summary of the topic without going through the whole content in the article. Often, Wikipedia returns the articles related to a search query that makes obvious finding the relevant topic for the user. Text summarization is generated by extracting all those important sentences that are most significant in its topics and have a strong match in its content. Experimentally, each sentence in the article content is encoded as a set of features and presented as an input to the network. The proposed neural network is trained using a set of randomly selected typical articles from Wikipedia. The network output is then used to predict the sentences as a summary of content from the searched query. The results showed that the proposed approach is robust and efficient at finding relevant summaries for most searched queries. Evaluation of the proposal yields accuracy scores of 0.10317 in ROUGE-N and 0.13998 in ROUGE–L.
Identification Of Buildings In Street Images Using Map Information

In this paper, we propose a method that identifies the buildings in a street image by accurately associating their locations between a map and an image. Building identification is beneficial to the use of images for detailed navigation. Given that a GPS-based camera pose (location and direction) embedded in EXIF of the image is available, we propose a method to improve the camera pose by using a depth map estimated from the image and the depth information taken from the map. By improving the camera pose of the image, we can identify the building in the image by accurate registration of the building on the map and that in the image. In our experiments, we use Google Street View images with their camera poses and demonstrate significant accuracy improvement in building identification.