Reviewerly/exHarmony huggingface dataset:
Priorities for Social and Humanities Projects Based on Text Analysis<sup>①</sup>

Abstract Purpose Changes in the world show that the role, importance, and coherence of SSH (social sciences and the humanities) will increase significantly in the coming years. This paper aims to monitor and analyze the evolution (or overlapping) of the SSH thematic pattern through three funding instruments since 2007. Design/methodology/approach The goal of the paper is to check to what extent the EU Framework Program (FP) affects/does not affect research on national level, and to highlight hot topics from a given period with the help of text analysis. Funded project titles and abstracts derived from the EU FP, Slovenian, and Estonian RIS were used. The final analysis and comparisons between different datasets were made based on the 200 most frequent words. After removing punctuation marks, numeric values, articles, prepositions, conjunctions, and auxiliary verbs, 4,854 unique words in ETIS, 4,421 unique words in the Slovenian Research Information System (SICRIS), and 3,950 unique words in FP were identified. Findings Across all funding instruments, about a quarter of the top words constitute half of the word occurrences. The text analysis results show that in the majority of cases words do not overlap between FP and nationally funded projects. In some cases, it may be due to using different vocabulary. There is more overlapping between words in the case of Slovenia (SL) and Estonia (EE) and less in the case of Estonia and EU Framework Programmes (FP). At the same time, overlapping words indicate a wider reach (culture, education, social, history, human, innovation, etc.). In nationally funded projects (bottom-up), it was relatively difficult to observe the change in thematic trends over time. More specific results emerged from the comparison of the different programs throughout FP (top-down). Research limitations Only projects with English titles and abstracts were analyzed. Practical implications The specifics of SSH have to take into account—the one-to-one meaning of terms/words is not as important as, for example, in the exact sciences. Thus, even in co-word analysis, the final content may go unnoticed. Originality/value This was the first attempt to monitor the trends of SSH projects using text analysis. The text analysis of the SSH projects of the two new EU Member States used in the study showed that SSH's thematic coverage is not much affected by the EU Framework Program. Whether this result is field-specific or country-specific should be shown in the following study, which targets SSH projects in the so-called old Member States.
High Resolution Satellite Image Compression using DCT and EZW

Image Compression has played vital role for efficient image storage, retrieval and transmission. In today’s digital world, images are captured with high wavelength camera lens for better quality, but requires more storage space. Comparison is carried out on multi spectral satellite images using DCT and EZW on basis of compression ratio, PSNR and MSE. DCT performs efficiently at medium bit rates giving compression ratio over 99% and low PSNR whereas EZW performs well at low bit rate giving efficient compression ratio. Disadvantage with DCT is that only spatial correlation of the pixels is considered and the neighborhood correlation is neglected, on the other hand, use of larger EZW wavelet functions produced blurred images.
Rogue Device Mitigation in the Internet of Things: A Blockchain-Based Access Control Approach

Recent technological developments in wireless and sensor networks have led to a paradigm shift in interacting with everyday objects, which nurtured the concept of Internet of Things (IoT). However, low-powered nature of IoT devices generally becomes a hindrance that makes them vulnerable to a wide array of attacks. Among these, the emergence of rogue devices is quickly becoming a major security concern. Rogue devices are malicious in nature which typically execute different kinds of cyberattacks by exploiting the weaknesses of access control schemes in IoT environments. Therefore, access control is one of the crucial aspects of an IoT ecosystem that defines an entry point for a device or a user in the network. This paper investigates this issue and presents an access control scheme by integrating an IoT network with blockchain technology, thereby arguing to replace the traditional centralized IoT-server architecture with a decentralized one. The blockchain is used with smart contracts to establish a secure platform for device registration. Due to this reason, the IoT devices are first required to register themselves and access the network via contracts thereafter. Moreover, the contracts host a device registry, the access control list, to grant or deny access to devices. This allows the proposed scheme to authorize registered devices only and block unregistered ones, which facilitates the mitigation of rogue devices. To demonstrate the feasibility and improvements of the proposed scheme, security analysis along with in-depth performance evaluation are conducted, where the obtained results indicate its applicability. A case study is also formulated with a comparative analysis that confirms the superior performance of the proposed scheme for low-powered IoT systems.
Secured IOTA Enabled Crypto-Platform with Discretionary Mining Capabilities and Miner Nomination based on First-Price Sealed Bid Auction Theory

The commercial utilization of cryptocurrency as a digital asset in being more and more sought-after on each successive year. Most of the well-renowned crypto platforms nowadays are devised based on the premise of the concept of blockchain. These cryptocurrencies work as an alternative medium of exchange using cryptography to secure the transactions on a distributed ledger. When using regular blockchain-based digital cryptocurrency most of the time the respected crypto-platforms discourage low valued microtransactions from being executed. Because often, the transaction fee may exceed the value of the product or service that is being purchased. As a result, for this particular reason, micropayment systems using digital crypto platforms remain largely underdeveloped. To solve this complication our thesis model was emanated from the notion of IOTA, which is considered as a minerless crypto-platform where the requisition of miners is disregarded thus enabling users to relish the advantages of microtransactions, however with the inclusion of âDiscretionary miningâ. âDiscretionary Miningâ refers to the hypothesis of the availability of mining capabilities at the discretion of the users.
Surface Fitting Using Dual Quaternion Control Points with Applications in Human Respiratory Modelling

In this paper we present a method for representing surfaces using a set of dual quaternion control points, with the goal of fitting to point clouds. Each control point is defined by a position and radius, which specify the area of the surface it affects, and a dual quaternion defining the transformation it applies. A point is mapped using the surface by a weighted sum of the control points, in a similar method to dual quaternion skinning. A surface is then represented as the transformation of an original surface, such as a unit square plane, using the control points. We demonstrate how we may fit surfaces to point clouds using a modified iterative gradient descent algorithm, adding control points to regions of the surface that are most poorly modelled at the current step. These methods are applied to the problem of representing human breathing by fitting surfaces to a subject’s chest as recorded by an RGB-D (image plus depth) camera and parameterizing the breathing using each control point’s parameters. Variations in the breathing pattern are shown before and after exercise.
Introducing Differential Privacy Mechanisms for Mobile App Analytics of Dynamic Content

Mobile app analytics gathers detailed data about millions of app users. Both customers and governments are becoming increasingly concerned about the privacy implications of such data gathering. Thus, it is highly desirable to design privacy-preserving versions of mobile app analytics. We aim to achieve this goal using differential privacy, a leading algorithm design framework for privacy-preserving data analysis.We apply differential privacy to dynamically-created content that is retrieved from a content server and is displayed to the app user. User interactions with this content are then reported to the app analytics infrastructure. Unlike problems considered in related prior work, such analytics could convey a wealth of sensitive information—for example, about an app user’s political beliefs, dietary choices, health conditions, or travel interests. To provide rigorous privacy protections for this information, we design a differentially-private solution for such data gathering.Our first contribution is a conceptual design for data collection. Since existing approaches cannot be used to solve this problem, we develop a new design to determine how the app gathers data at run time and how it randomizes it to achieve differential privacy. Our second contribution is an instantiation of this design for Android apps that use Google Firebase. This approach keeps privacy logic separate from the app code, and uses code rewriting to automate the introduction and evolution of privacy-related code. Finally, we develop techniques for automated design space characterization. By simulating different execution scenarios and characterizing their privacy/accuracy trade-offs, our analysis provides critical pre-deployment insights to app developers.
Multi-Frame Low-Dose CT Image noise reduction using Adaptive Type-2 Fuzzy filter and Fast-ICA

Decreasing the absorbed dosage by patient in x-ray imaging along with keeping image quality is one of the long-term goals of medical imaging field. Using low-dose images, instead of normal-dose images, can decrease the absorbed dosage; however, it also decreases the image quality due to quantum noise. In this paper, combination of Fast-ICA and adaptive Type-2 Fuzzy filter is utilized for filtering a group of low-dose images. Five different phantoms are used for investigating various effect of denoising, such as retaining slice geometry, high resolution, low-contrast, uniformity and bead geometry regions. Due to few numbers of images (8 images for each phantom), using deep learning method is not practical. The main novelty is attempting to convert the shot noise distribution to salt and pepper and denoising mapped image using fast independent component analysis. Concisely, the average and standard deviation of PSNR and SSIM of the proposed algorithm on five phantoms are 36.0 ± 2.7 dB and 0.83 ± 0.2, respectively, which shows a significant improvement comparing to the similar benchmark methods.
Internet of Art: Exploring Mobility, AR and Connectedness in Geocaching Through a Collaborative Art Experience

This paper views the Internet as a platform that may be used in co-creating art experiences and to support player collaboration. Our research presents an ethnographic study of preschoolers testing an Augmented Reality mobile application that includes connected artworks as part of a Geocaching letterbox. Driven by belief in the power of play to support exploring, learning and development, and the conviction that competencies with digital technologies will be necessary to ensure future literacy skills, play with technologies has become an integral part of educational provision for young children in developed nations. New cultural services, such as location-based, urban experiences should convince players’, educators’, parents’ and the city’s policymakers of their value for playful engagement. Therefore, this paper aims to present a suggestion for how connectedness and collaboration can be supported through the Internet of Art in the context of playing on an augmented geocaching trail enhanced with artworks.
Eye Gaze-based Object Rotation for Head-mounted Displays

Hands-free manipulation of 3D objects has long been a challenge for augmented and virtual reality (AR/VR). While many methods use eye gaze to assist with hand-based manipulations, interfaces cannot yet provide completely gaze-based 6 degree-of-freedom (DoF) manipulations in an efficient manner. To address this problem, we implemented three methods to handle rotations of virtual objects using gaze, including RotBar: a method that maps line-of-sight eye gaze onto per-axis rotations, RotPlane: a method that makes use of orthogonal planes to achieve per-axis angular rotations, and RotBall: a method that combines a traditional arcball with an external ring to handle user-perspective roll manipulations. We validated the efficiency of each method by conducting a user study involving a series of orientation tasks along different axes with each method. Experimental results showed that users could accomplish single-axis orientation tasks with RotBar and RotPlane significantly faster and more accurate than RotBall. On the other hand for multi-axis orientation tasks, RotBall significantly outperformed RotBar and RotPlane in terms of speed and accuracy.
PRINCE under Differential Fault Attack

Fault analysis is one of the most studied physical attacks primarily due to the inherent ease of implementation. This work investigates integral and differential fault analysis attacks on the well-known lightweight block-cipher PRINCE. The work begins by identifying new integral properties of PRINCE which are not restricted to be symmetric around the middle rounds. The work also identifies new slow diffusion trails on the cipher. Both properties are exploited to mount practical integral and differential fault attacks on PRINCE that uniquely recover the key. The integral fault attack has a time complexity of 236 and 220 with 15 nibble faults in round 8.5 and 9.5 respectively while the slow diffusion differential fault attack works with 4 bit-faults in the 10th round with a complexity of 222. Finally, the fact that the faults can be injected very close to the middle rounds forms one of the interesting aspects of this work and adds to the state-of-the-art on contemporary results on PRINCE available in the literature. Moreover, a 3-D visualization model of PRINCE state has also been proposed in this work which can be used to extend or improve existing attacks on PRINCE.
Design of Data Acquisition Platform for Industrial Internet of Things

Industrial Internet of Things platform is to create a service system based on a large number of data collection and analysis to support the extensive connection, flexible supply and efficient configuration of manufacturing resources based on the needs of manufacturing industry digitization, networking and intelligence. Based on the analysis of industrial Internet of things platform and SCADA, this paper designs the data acquisition platform of industrial Internet of things. The overall structure of the platform includes the infrastructure, the corresponding communication mode and the necessary software. The hardware part includes sensors, gateway modules, etc.; according to the communication protocol, data interconnection is realized; the software part mainly uses VS2017 and SQL Server to realize real-time data display, historical data curve, over limit alarm and other functions, and the data is visualized by using JavaScript language through Echarts.
An Explicit Feedback Recommendation Algorithm Based on Subjective and Objective Evaluation Transformation Model

With the development of the Internet, the information in the network is growing explosively, and the information surplus brings a great burden to people in screening and selecting information. Collaborative filtering recommendation can effectively realize information filtering. There are many applications in various scenarios. Currently, collaborative filtering recommendation methods can be roughly divided into two types: explicit feedback based recommendation and implicit feedback based recommendation. In recent years, algorithms for implicit feedback recommendation have developed rapidly, and many excellent algorithms have emerged, the performance of recommendation algorithm based on explicit feedback still needs to be improved. This paper analyzes and models human cognitive behavior, and proposes a subjective and objective evaluation transformation model. Based on this model, a recommendation algorithm based on explicit feedback is proposed. Simulation results show that the algorithm has good performance in recommendation accuracy.
Blockchain-mediated Collaboration of Citizens in Open Government Processes

This work explores how sustainable citizen collaboration to foster Open Government can be achieved by means of Blockchain-based solution. The technical feasibility and economic viability of a set of extensions of CKAN tool, bringing together Internet of People (IoP) related technologies such as Blockchain and crowdsourcing, to address sustainability in Open Government Portals is analysed. For that, a use case validation is performed, and the costs of its deployment assessed. The aim is to show how IoP promoting technologies enhance Public Administration (PA) and citizen collaboration to meet common interest objectives.
Confidence-Aware Adversarial Learning for Self-supervised Semantic Matching

In this paper, we aim to address the challenging task of semantic matching where matching ambiguity is difficult to resolve even with learned deep features. We tackle this problem by taking into account the confidence in predictions and develop a novel refinement strategy to correct partial matching errors. Specifically, we introduce a Confidence-Aware Semantic Matching Network (CAMNet) which instantiates two key ideas of our approach. First, we propose to estimate a dense confidence map for a matching prediction through self-supervised learning. Second, based on the estimated confidence, we refine initial predictions by propagating reliable matching to the rest of locations on the image plane. In addition, we develop a new hybrid loss in which we integrate a semantic alignment loss with a confidence loss, and an adversarial loss that measures the quality of semantic correspondence. We are the first that exploit confidence during refinement to improve semantic matching accuracy and develop an end-to-end self-supervised adversarial learning procedure for the entire matching network. We evaluate our method on two public benchmarks, on which we achieve top performance over the prior state of the art. We will release our source code at https://github.com/ShuaiyiHuang/CAMNet.
Design and optimization of TS firefly algorithm based on the nonhomogeneous linear polygonal T‐S fuzzy system

The core idea of the fuzzy system is to avoid the accurate mathematical model and imitate human brain to achieve fuzzy reasoning, it can not only convert language information into a systematic program of nonlinear mapping, but also process complex data information through fuzzy rules. In this paper, we first introduce the mathematical model of the nonhomogeneous linear polygonal Takagi–Sugeno (T-S) fuzzy system based on the ordered representation of polygonal fuzzy number and its linear operation, and the expression of the adjustment parameters of the consequent linear part of the T-S fuzzy system is given by the ordered representation. Second, the relative brightness and attraction formula of firefly are used to update the particle positions of possible solutions, thus achieving global optimization of all tuning parameters (particles). Finally, the TS firefly algorithm (TSFA) is designed for some adjustment parameters of the consequent part of the nonhomogeneous linear polygonal T-S fuzzy system, and the effectiveness of the algorithm is illustrated by a simulation example.
Multi-view and Multi-label Method with Three-Way Decision-Based Clustering

In real-world multi-view multi-label clustering and some classification tasks, instances and the corresponding clusters has at least three kinds of relationships, belong-to definitely, not belong-to definitely, and uncertain. Some learning machines consider only two of them, for example, belong-to definitely and not belong-to definitely. Moreover, three-way decision-based clustering (TDC) strategy is a good method to make the belongingness of instances to a cluster depend on the probabilities of uncertain instances belonging to core regions. Thus in our work, we take the notion of classical multi-view multi-label learning machines as the basic and introduce TDC so as to develop a multi-view and multi-label method with three-way decision-based clustering (MVML-TDC) and consider the relationships between instances and clusters. Experimental results validate that MVML-TDC achieves a better average performance and an acceptable running time.
Self Attention based multi branch Network for Person Re-Identification

Recent progress in the field of person re-identification have shown promising improvement by designing neural networks to learn most discriminative features representations. Some efforts utilize similar parts from different locations to learn better representation with the help of soft attention, while others search for part based learning methods to enhance consecutive regions relationships in the learned features. However, only few attempts have been made to learn non-local similar parts directly for the person re-identification problem. In this paper, we propose a novel self attention based multi branch(classifier) network to directly model long-range dependencies in the learned features. Multi classifiers assist the model to learn discriminative features while self attention module encourages the learning to be independent of the feature map locations. Spectral normalization is applied in the whole network to improve the training dynamics and for the better convergence of the model. Experimental results on two benchmark datasets have shown the robustness of the proposed work.
Evaluating Models for a Higher Education Course Recommender System Using State Exam Results

When young people approach the end of their schooling, they are faced with a plethora of often daunting decisions, including whether to go to University or other further education institution, and what further education course is most suitable for them. In this paper, we propose using result data obtained from the Colombian Saber 11/T&T/Pro state exams as input for a higher education course recommender system. We compare five different recommender models by analyzing precision, recall, ROC curves and prediction error. Our findings are that user-based collaborative filtering, and the model that recommends the most popular courses, are the ones that perform best. We note that while the context of this work is in Colombia, most other countries have similar or equivalent state exams. It can therefore be expected that our research findings can be more generally applied to other contexts. As further work, we hope to deploy this recommender system as a mobile telephone application for young people to use to help them choose higher education courses.