Reviewerly/exHarmony huggingface dataset:
Methodology for massive configuration of OAuth 2.0 tokens in large IoT scenarios

The evolution of technologies in the sensors/actuators field, together with the improvement in mobile communications, has allowed the growth and consolidation of the Internet of Things concept. Among the current challenges in the scenarios based on this paradigm, it is possible to highlight those related to security and access control to information generated or managed by the devices in the environment. This work is focused on designing architecture elements and the accompanying methodology needed to use the OAuth 2.0 authorization framework in large Internet of Things scenarios. OAuth 2.0 is a de facto standard in Internet-based Web applications. To achieve this, we propose using a Resource Service able to manage requests from devices and an encryption scheme based on public-key cryptography that would allow the massive secure distribution of OAuth access tokens. The proposed method would also enable easy and secure interactions between the resource owners and their devices. We have validated our proposal through its implementation, deployment, and testing in various devices with different processing resources.
Fourier-Based Automatic Transformation between Mapping Shapes—Cadastral and Land Registry Applications

Sometimes it is necessary to know the transformation to apply to a mapping shape in order to locate its true place. Such an operation can be computed if a corresponding reference object exists and we can identify corresponding points in both shapes. Nevertheless our approach does not need to match any corresponding point beforehand. The method proposed defines a polygon in the frequency domain—two periodic functions are derived from a polygonal or polygon. According to the theory of elliptic Fourier descriptors those two periodic functions can be expressed by Fourier expansions. The transformation can be computed using the coefficients of the harmonics from the corresponding shapes without taking into account where each polygon vertex is placed in the spatial domain. The transformation parameters will be derived by a least squares approach. The geomatics and geosciences applications of this method go from photogrammetry, geographic information system, computer vision, to cadaster and real estates.
Image Aesthetic Assessment using Deep Learning for Automated Classification of Images into Appealing or Not-Appealing

Automated Image Aesthetic Assessment has been challenging to implement due to varied perceptions of people. This paper aims to tackle the matter and achieve better accuracy by adopting a deep learning neural network approach to perform image aesthetic classification. This research work presents a deep convolutional neural network framework that programmatically extracts high and low ranking features of an image and differentiates the dataset for analyzing areas of concern. Our model performs Image recognition using TensorFlow and Keras. A high-level network is employed to train and classify images. Additionally, the proposed model employs color contrast, depth of field, and rule of thirds to further improve the aesthetic performance of the model. This also uses GrabCut algorithm for interactive foreground extraction using OpenCV (Open Source Computer Vision Library). Our dataset, comprising 6000 images, is compiled from a range of sources online(Pinterest, Google, Flickr, Kaggle, Flickr) to make it as diverse as possible. Our experiments demonstrate that compared to traditional handcrafted models our Deep Convolutional Neural Network model yields significantly better categorization correctness (accuracy) of 73.27%. Thus, the Deep Learning Model helps exclusively to boost the performance of Aesthetic Assessment.
Detecting Security Vulnerabilities in Website using Python

On the current website, there are many undeniable conditions and there is the existence of new plot holes. If data link is normally extracted on each of the websites, it becomes difficult to evaluate each vulnerability, with tolls such as XS S, SQLI, and other such existing tools for vulnerability assessment. Integrated testing criteria for vulnerabilities are met. In addition, the response should be automated and systematic. The primary value of vulnerability Buffer will be made of predefined and self-formatted code written in python, and the software is automated to send reports to their respective users. The vulnerabilities are tried to be classified as accessible. OWASP is the main resource for developing and validating web security processes.
Work-in-Progress—Assisting AR, VR and Hologram Learning Experience through MOOC

This work-in-progress paper is part of an ongoing research endeavour intended to examine the integration of various learning technologies - namely Virtual Reality, Augmented Reality and Hologram. By using the development process of MOOC as a case study, the research attempts to unravel some factors and hurdles in building a cohesive and seamless learning experience across these technologies. While the usual approach is to amend these experiential technologies to fit within the technical requirements and pedagogical premise of MOOC, this research project attempts to invert this process by building a MOOC that tries to accommodate the needs of each technology.
Inference and Energy Efficient Design of Deep Neural Networks for Embedded Devices

Deep/Convolutional Neural Networks (DNNs/CNNs) are deployed on resource-constraint embedded devices in order to serve popular computer vision applications. However, DNNs have increased computing requirements and battery-operated devices suffer to deliver acceptable performance. In this paper, we present an efficient design of DNNs for edge devices that performs a DNN architectural search. Our method finds alternative designs of DNNs that have lower energy consumption and inference time than ResNet reference networks. Experimental results show up to 78.82% reduction in energy consumption and 35.71% in inference time, while training up to 95.67% fewer networks. As a trade-off, our approach compromises the user Quality of Service up to 2% compared to the reference networks.
Entropy as a Service: A Lightweight Random Number Generator for Decentralized IoT Applications

Cryptographic Pseudorandom Number Generators (CPRNG) play a very crucial role in Internet of Things (IoT) security. Cryptographic protocols require random numbers for nonces, salts, and key generation. However, developing secure and lightweight CPRNG is strenuous. Insecure source of randomness can evolve in vulnerabilities and can jeopardize security mechanisms. As the number of IoT devices are expected to exceed over billions, the demand for distributed CPRNG increases. Manually configuring random numbers in large numbers of IoT devices is practically challenging and insecure. In this paper, we propose a cryptographically secure pseudorandom number generator based on sensor data as source of randomness. The appealing characteristic of a sensor data based random number generator is that sensor can possibly generate infinite data. Thus, having longer period and perhaps higher entropy. We also present proof-of-concept of potential usage of sensor data as a source of randomness. Furthermore, the mechanism is evaluated with the NIST statistical suite.
Privacy and security constraints for code contributions

Summary In collaborative software development, developers submit their contributions to repositories that are used to integrate code from various collaborators. To avoid privacy and security issues, code contributions are often reviewed before integration. Although careful manual code review can detect such issues, it might be time‐consuming, expensive, and error‐prone. Automatic analysis tools can also detect privacy and security issues, but they often demand significant developer effort, or are domain specific, considering fixed framework specific vulnerability sources and sinks. To reduce these problems, in this paper we propose the Salvum policy language to support the specification of constraints that help to protect sensitive information from being inadvertently accessed by specific code contributions. We implement a tool that automatically checks Salvum policies for systems of different technical domains. We also investigate whether Salvum can find policy violations for a number of open‐source projects. We find evidence that Salvum helps to detect violations even for well‐supported and highly active projects. Moreover, our tool helps to find 80 violations in benchmark projects.
Work-in-Progress—Developing Criteria for Virtual Reality Courses Based on Virtual World Experiences

After ten years hosting classes, presentations, and social gatherings in Virtual Worlds, members of the Virtual Center for Archives and Records Administration (VCARA) virtual worlds interest group turned their attention to the opportunities afforded by Virtual Reality. A survey was developed to identify criteria to be used when evaluating virtual reality solutions for classes, training programs, presentations, and social activities. Fifty-eight educators, who have successfully navigated the virtual world environment, responded to this survey. This work-in-progress paper presents the methodology used to gather the information, a summary and discussion of the findings, and plans for future activities and further research.
Swings and Roundabouts: Attention-Structure Interaction Effect in Deep Semantic Matching

In the context of deep learning models for semantic matching problems, we propose a novel Multi-View Progressive Attention (MV-PA) mechanism general enough to operate on various linguistic structures of text. More importantly, we study the interaction effect between explicit linguistic structures (e.g., linear, constituency, and dependency) and implicit structures elicited by attention mechanisms. Empirical results on multiple datasets demonstrate salient patterns of substitutability between the two families of structures (explicit and implicit). Our findings not only provide intellectual foundations for the popular use of “linear LSTM + attention” architectures in NLP/QA research, but also have implications in other modalities and domains.
The Naturalist’s Workshop: Virtual Reality Interaction with a Natural Science Educational Collection

For experiential educators who utilize or maintain physical collections, The Naturalist's Workshop is an exemplar virtual reality platform to interact with digitized collections in an intuitive and playful way. The Naturalist's Workshop is a purpose-developed application for the Oculus Quest standalone virtual reality headset for use by museum visitors on the floor of the North Carolina Museum of Natural Sciences under the supervision of a volunteer attendant. Within the application, museum visitors are seated at a virtual desk. Using their hand controllers and head-mounted display, they explore drawers containing botanical specimens and tools-of-the-trade of a naturalist. While exploring, the participant can receive new information about any specimen by dropping it into a virtual examination tray. 360-degree photography and three-dimensionally scanned specimens are used to allow user-motivated, immersive experience of botanical metadata such as specimen collection coordinates.
Towards Personalized Game-Based Learning in Anti-Phishing Education

As anti-phishing games emerge as a scalable, motivational and effective approach to anti-phishing education for non-professional end-users, problems arise due to the missing relevance of a games' content and context. If a game presents examples unknown or without relevance to the user, the learning potential is limited as users have no point of reference. To provide more meaningful, relevant game content to users, we propose a personalization pipeline for data collection, content generation and delivery for anti-phishing learning games.
Lineament Assessment of Aynak Copper mine using Remote Sensing approach

Abstract This research developed a new approach to extract the lineaments using Landsat, QuickBird and ALOS PALSAR satellite remotely sensed data in Aynak copper mine which is located in Kabul tectonic block and the Tethyan Copper Belt of Afghanistan. Lineaments and trench directions are not relatively studied in the study area which will bring obstacles in exploration and extraction of the mine. This research aims to extract the lineaments for studying the better trench direction in the study area. Lineaments such as faults and joints are extremely important structures that not only show the surface structure but also give us important knowledge about the subsurface of the earth. Afghanistan is a part of the Alpine-Himalayan orogenic belt which suffers from high tectonic movements in some areas, especially north-eastern and central parts which leads to the occurrence of different lineaments and unconformities. Lineaments can be surveyed and mapped using field geological survey or using satellite remotely sensed data. Satellite remote sensing data costs less and takes less time compare to field geological survey for lineament extraction. This research used Landsat 8 natural color with 2, 3, 4 band composition, QuickBird 1m resolution, and ALOS PALSAR 12.5 m satellite imagery to detect and map the lineaments in the Aynak copper mine area in Afghanistan. Geomatica software was used for lineament extraction using the Manual technique. Then the rose diagrams of the lineaments were plotted using Polar Plots extension of ArcGIS. Next, the rose diagrams derived from the two mentioned lineament extraction techniques were compared and evaluated. Global Mapper software was used for the extraction of lineaments using Visual technique and to map the rivers and ridges in the Aynak area of interest for validation of the results. Finally, the best orientation for trenches in Aynak copper mine was derived. This study found that faults and joints in the study area have a NE-SW trend and directions. This study will contribute to a better understanding of the trench direction and mineral exploration in Aynak copper mine.
LogicKit: bringing logic programming to swift

A new trend in programming languages is to merge multiple paradigms, rather than focusing on one as it was customary in the past. Most modern languages provide native support for imperative and functional programming, object-orientation and even concurrency. The typical mechanism used to blend heterogeneous language concepts is to rely on functions. Functions are a very well understood, usually offer an excellent abstraction over diverging models of computation, but are unsuitable to interface logic programming, unfortunately. Embedding a full logic programming language into a host is also unsatisfactory, as it impedes the ability to make data flow from one paradigm to the other.
Entity Thematic Similarity Measurement for Personal Explainable Searching Services in the Edge Environment

Currently, search engines are widely used to address the information overload problem. Different from the existing client-and-sever-based frameworks, edge computing (EC) technology can provide a new architecture for personalized searching services. The issue of how to measure the similarities among entities by using the context information generated by user behavior in the edge environment is vital in the task of entity-related personal searching. To analyze and measure the similarities among entities, existing methods are mainly based on either the textual content or relationships unilaterally, and the results usually have a fixed degree of similarity. However, the similarities among entities depend on the set of properties that belong to the entities. This approach should be used in determining the similarity or dissimilarity associated with the surrounding context. To address this limitation, we propose a novel semantic augmentation method with a double attention mechanism. The method refers to a dynamic representation learning process that maps an entity to a real number vector in semantic space. In this article, different from the existing similarity measurement methods, we propose a thematic similarity measure approach to analyze the connotation and denotation similarities among entities. The experimental results show that the double attention mechanism leads to a significant improvement in the entity thematic similarity measurement tasks. The model can make a separation among the entities from different domains effectively. In addition, it can take similar entities that are closer in the same domain. It also shows excellent performance on the task of entity thematic similarity, which makes the recommendation results more explainable.
Wildcard: spreadsheet-driven customization of web applications

Many Web applications do not meet the precise needs of their users. Browser extensions offer a way to customize web applications, but most people do not have the programming skills to implement their own extensions.
Strong functional pearl: Harper’s regular-expression matcher in Cedille

This paper describes an implementation of Harper's continuation-based regular-expression matcher as a strong functional program in Cedille; i.e., Cedille statically confirms termination of the program on all inputs. The approach uses neither dependent types nor termination proofs. Instead, a particular interface dubbed a recursion universe is provided by Cedille, and the language ensures that all programs written against this interface terminate. Standard polymorphic typing is all that is needed to check the code against the interface. This answers a challenge posed by Bove, Krauss, and Sozeau.
Internet of Things Forensics HoneyNetCloud Investigation Model

The internetwork of uniquely distinguishable embedded computing devices capable to send, collect and process data they gather from various other environments is the Internet of Things. Because of the excessive development of the IoT devices in the internet space, where many devices have become extra smart by having the ability to connect global space with numerous smart features, IoT devices are the prime aim of cyberbullying for the attackers. Even though there are a few generic top-down models that forensic specialists follow for the investigation, most of the models have their weaknesses to the credit. In this article, a novel model is presented for the IoT forensics named HoneyNetCloud Investigation Model (HIM). HIM make use of the honeypots for attracting the hackers and recording the behaviours and intentions of the hackers. The attack types are classified by the algorithm based on the DST - Dempster Shafer Theory. Various experimentations were tried out and IoT honeypot attacks for one year was captured as part of the study. The intention of this study is deriving a method by developing a fresh model to handle the investigation situations in IoT network for the forensic analysts and investigators.
P-lsGOF: A parallel learning-selection-based global optimization framework

When optimizing complicated engineering design problems, the search spaces are usually extremely nonlinear, leading to the great difficulty of finding optima. To deal with this challenge, this paper introduces a parallel learning-selection-based global optimization framework (P-lsGOF), which can divide the global search space to numbers of sub-spaces along the variables learned from the principal component analysis. The core search algorithm, named memory-based adaptive differential evolution algorithm (MADE), is parallel implemented in all sub-spaces. MADE is an adaptive differential evolution algorithm with the selective memory supplement and shielding of successful control parameters. The efficiency of MADE on CEC2017 unconstrained problems and CEC2011 real-world problems is illustrated by comparing with recently published state-of-the-art variants of success-history based adaptative differential evolution algorithm with linear population size reduction (L-SHADE) The performance of P-lsGOF on CEC2011 problems shows that the optimized results by individually conducting MADE can be further improved.
Framework for Contemporary Inquiry-based Augmented Reality Learning

The aim of this discussion paper is to propose a framework to apply contemporary learning approach in using augmented reality (AR) for inquiry-based learning. Our literature review shows that AR is mainly used in learning on a very simplistic level and the potential of AR has not been opened. Therefore, we propose a new framework for applying contemporary inquiry-based learning using augmented reality. This framework specifies how the affordances and features of AR could be applied in instructional design according to the five general phases of inquiry-based learning and how the principles and goals of the contemporary learning approach are supported in this learning approach.
TREND: Transferability-Based Robust ENsemble Design

Deep learning models hold state-of-the-art performance in many fields, but their vulnerability to adversarial examples poses a threat to their ubiquitous deployment in practical settings. Additionally, adversarial inputs generated on one classifier have been shown to transfer to other classifiers trained on similar data, which makes the attacks possible even if model parameters are not revealed to the adversary. This property of transferability has not yet been systematically studied, leading to a gap in our understanding of robustness of neural networks to adversarial inputs. In this work, we study the effect of network architecture, optimizer, input, weight, and activation quantization on transferability of adversarial samples. We also study the transferability of different attacks. Our experiments reveal that transferability is significantly hampered by input quantization and architectural mismatch between source and target, and the choice of optimizer turns out to be critical. We observe that transferability is architecture-dependent for both weight and activation quantized models. To quantify transferability, we use simple metric and demonstrate the utility of the metric in designing a methodology to build ensembles with improved adversarial robustness. When attacking ensembles we observe that “gradient domination” by a single ensemble member model hampers existing attacks. To combat this we propose a new state-of-the-art ensemble attack. We compare the proposed attack with existing attack techniques to show its effectiveness. Finally, we show that an ensemble consisting of carefully chosen diverse networks achieves better adversarial robustness than would otherwise be possible with a single network. The source code for this work has been made available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/purdue-nrl/TREND</uri> .
Research Data Management Status of Science and Technology Research Institutes in Korea

Recent advances in digital technology and the data-driven science paradigm has led to a proliferation of research data, which are becoming more important in scholarly communications. The sharing and reuse of research data can play a key role in enhancing the reusability and reproducibility of research, and data from publicly funded projects are assumed to be public goods. This is seen as a movement of open science and, more specifically, open research data. Many countries, such as the USA, UK, and Australia, are pushing ahead with implementing policies and infrastructure for open research data. In this paper, we present survey results pertaining to the creation, management, and utilization of data for researchers from government-funded research institutes of science and technology in Korea. We then introduce recent regulations stipulating a mandated data management plan for national R&amp;D projects and on-going efforts to realize open research data in Korea.