Reviewerly/exHarmony huggingface dataset:
Semantic tagging and the Nordic tradition of everyman’s rights

Abstract This article uses semantic tagging to analyse the Nordic concept of everyman’s rights (a right of public access to nature) in protocols of the Finnish parliament. In the analysis, we use a novel tool, a lexical semantic tagger for Finnish (Finnish-language Semantic Tagger), which is used to tag key discussions about everyman’s rights in the Finnish parliament. The article has two contributions as follows: first, it presents a method that combines semantic tagging and similarity analysis of corpora (keyness) for studying the formation of political concepts in large textual data. Secondly, it sheds light on the Nordic access rights and the underlying customary everyman’s rights. Despite its central role in public debate, the history of the concept has not been well researched. Our analysis shows that the legislative context could be clearly detected with our approach, and that the method allowed us to describe shifts in the meaning of everyman’s rights in the legislative discussion.
Adversarial Attacks on Optimization based Planners

Trajectory planning is a key piece in the algorithmic architecture of a robot. Trajectory planners typically use iterative optimization schemes for generating smooth trajectories that avoid collisions and are optimal for tracking given the robot’s physical specifications. Starting from an initial estimate, the planners iteratively refine the solution so as to satisfy the desired constraints. In this paper, we show that such iterative optimization based planners can be vulnerable to adversarial attacks that force the planner either to fail completely, or significantly increase the time required to find a solution. The key insight here is that an adversary in the environment can directly affect the optimization cost function of a planner. We demonstrate how the adversary can adjust its own state configurations to result in poorly conditioned eigenstructure of the objective leading to failures. We apply our method against two state of the art trajectory planners and demonstrate that an adversary can consistently exploit certain weaknesses of an iterative optimization scheme.
A Residual Dynamic Graph Convolutional Network for Multi-label Text Classification

Recent studies often utilize the Graph Convolutional Network (GCN) to learn label dependencies features for the multi-label text classification (MLTC) task. However, constructing the static label graph according to the pairwise co-occurrence from training datasets may degrade the generalizability of the model. In addition, GCN-based methods suffer from the problem of over-smoothing. To this end, we propose a Residual Dynamic Graph Convolutional Network Model (RDGCN) (https://github.com/ilove-Moretz/RDGCN.git) which adopts a label attention mechanism to learn the label-specific representations and then constructs a dynamic label graph for each given instance. Furthermore, we devise a residual connection to alleviate the over-smoothing problem. To verify the effectiveness of our model, we conduct comprehensive experiments on two benchmark datasets. The experimental results show the superiority of our proposed model.
Toward intraoperative endomicroscopy with a GPU-accelerated deformable video mosaicking algorithm

Due to the limited field of view (FOV), the probe-based confocal laser endomicroscopy (pCLE) imaging system remains challenging to be widely used in clinic. Existing video mosaicking approaches are usually troubled by poor real-time capability and sensitivity to tissue deformations and intensity fluctuations. In this paper, a novel pCLE mosaicking algorithm that simultaneously implements rigid probe motion tracking and inter-frame tissue deformation correction is proposed. The sum of conditional variance (SCV) metric with low calculational complexity and intensity variation invariance is embedded into the reconstruction of pCLE mosaics. We also deduce the derivatives of the SCV metric with respect to transformation variables to enhance the robustness of numerical solutions. Moreover, a parallel acceleration mechanism that maximally exploits the resources of graphics processing unit (GPU) is designed to boost mosaicking efficiency. Experiments on lens tissue paper and swine tissue highlight the excellent mosaicking accuracy of the proposed method in this field. It is also verified that the mosaic-king rate of the proposed method exceeds 14 frames per second (fps), which can keep up with the acquisition rate of most pCLE imaging systems (8 fps~12 fps). Benefiting from finer mosaics and faster speed, the proposed algorithm has high potential to achieve large and accurate mosaicking for intraoperative endomicroscopy.
Wavelet-Based Block Low-Rank Representations for Hyperspectral Denoising

This paper presents a wavelet-based block low-rank representations (WBBLRR) denoising method for hyperspectral images (HSIs). WBBLRR uses 3-D wavelet transformation to decompose HSI into different blocks, where each block utilizes a low-rank representations model to obtain the denoised block, and then uses inverse 3-D wavelet transformation for all the denoised blocks to obtain the denoised HSI. The proposed method is evaluated by using both simulated and real hyperspectral datasets.
Automatic inspection and analysis of digital waveform images by means of convolutional neural networks

Abstract Analyzing seismic data to get information about earthquakes has always been a major task for seismologists and, more in general, for geophysicists. Recently, thanks to the technological development of observation systems, more and more data are available to perform such tasks. However, this data “grow up” makes “human possibility” of data processing more complex in terms of required efforts and time demanding. That is why new technological approaches such as artificial intelligence are becoming very popular and more and more exploited. In this paper, we explore the possibility of interpreting seismic waveform segments by means of pre-trained deep learning. More specifically, we apply convolutional networks to seismological waveforms recorded at local or regional distances without any pre-elaboration or filtering. We show that such an approach can be very successful in determining if an earthquake is “included” in the seismic wave image and in estimating the distance between the earthquake epicenter and the recording station.
Improved entropy bounds for parity filtered self-timed ring based random number generators

• True random number generators are often preferred in high security applications. • AIS31 evaluation criteria require stochastic model to test unpredictability of TRNG. • We study self-timed ring based TRNG and the related stochastic model. • We improve entropy bounds for such TRNGs. • Our results can lower hardware requirements and stimulate further research. We improve entropy bounds for a self-timed ring based true random number generator, taking the timing of the reference clock signals into account. The models we discuss encompass both perfect and jittered reference clocks. Importantly, our novel analysis of jittered reference clocks can be used to study how robust the improved entropy bounds are. We use parity filters as post-processing blocks and improve results on the required minimal parity filter size to obtain a given target entropy. In addition, we see in numerical experiments that these models are robust in the sense that the minimal required size of a parity filter to exceed a given entropy bound does not change when weakening the assumption on the reference clock; i.e. when considering jittered instead of perfect reference clocks.
Resonant networks

Thesaurus definitions map out resonance in terms such as “richness or significance, especially in evoking an association or strong emotion” and “oscillation induced in a physical system when it is affected by another system that is itself oscillating at the right frequency”. More than a technical term, resonance refers to instances of being affected: to connectivity and contact between objects, ideas, and people as they move one another. As a dynamic relation of varying intensities and speeds where the affective and the emotional stick and cohere, resonance gives shape to online connections and disconnections, proximities and distances between both human and non-human bodies. This contribution explores the affordances of the notion of resonance in analyzing the networked circulation of data and the affective intensities that it entails. More specifically, it deploys resonance in unpacking the notions of virality and memes in ways that both detach them from biological premises and make it possible to account for their power to affect the people engaging with them.
Intelligent Decision Support System

A phishing attack is one of the most common forms of cybercrime worldwide. In recent years, phishing attacks have continued to escalate in severity, frequency and impact. Globally, the attacks cause billions of dollars of losses each year. Cybercriminals use phishing for various illicit activities such as personal identity theft and fraud, and to perpetrate sophisticated corporate-level attacks against financial institutions, healthcare providers, government agencies and businesses. Several solutions using various methodologies have been proposed in the literature to counter web-phishing threats. This research work adopts a novel strategy to the detection and prevention of website phishing attacks, with a practical implementation through development towards a browser toolbar add-in. The IPDS is shown to be highly effective both in the detection of phishing attacks and in the identification of fake websites. Experimental results show that approach using the CNN + LSTM has a 93.28% accuracy with an average detection time of 25 seconds, whilst the approach has a slightly lower accuracy. These times are within typical times for loading a web page which makes toolbar integration into a browser a practical option for website phishing detection in real time. The results of this development are compared with previous work and demonstrate both better or similar detection performance. This is the first work that considers how best to integrate images, text and frames in a hybrid feature-based solution for a phishing detection scheme.
Visual Language Based Succinct Zero-Shot Object Detection

On account of a large scale of dataset need to be annotated to train the deep learning based modern object detection model, zero-shot object detection has become an important research field which aims to simultaneously localize and recognize unseen objects that are not observed during training. In order to improve the performance of zero-shot object detection, recent state of the art methods tend to make complicated modifications to the modern object detectors in terms of the model structure, loss function and training process. They always take the simple modification as a baseline, and think it is worse than more complicated methods. In contrast, we find that simple modification can achieve better performance. Considering that the redundant modification may increase the risk of over-fitting in seen classes and reduce generalization performance on unseen classes, we propose a visual language based succinct zero-shot object detection framework, which only replaces the classification branch in the modern object detector with a lightweight visual-language network. Since zero-shot object detection is a classic multi-modal learning protocol which consists of a visual feature space and a language space, our visual-language network learns the visual language alignment from the image and language data of seen classes and transfers this alignment to detect unseen objects. Following the Occam's razor principle that "Entities should not be multiplied unnecessarily", extensive experimental results show that our succinct framework can suppress all existing zero-shot object detection methods on several benchmarks and gets the new state-of-the-art.
Two-stage pyramidal convolutional neural networks for image colorization

The development of colorization algorithms through deep learning has become the current research trend. These algorithms colorize grayscale images automatically and quickly, but the colors produced are usually subdued and have low saturation. This research addresses this issue of existing algorithms by presenting a two-stage convolutional neural network (CNN) structure with the first and second stages being a chroma map generation network and a refinement network, respectively. To begin, we convert the color space of an image from RGB to HSV to predict its low-resolution chroma components and therefore reduce the computational complexity. Following that, the first-stage output is zoomed in and its detail is enhanced with a pyramidal CNN, resulting in a colorized image. Experiments show that, while using fewer parameters, our methodology produces results with more realistic color and higher saturation than existing methods.
Blockchain Based Authentication Framework for Kerberos Enabled Hadoop Clusters

With the emergence of big data, data security is one of the most widely discussed topics nowadays among the research world and security experts. Kerberos Protocol is relied upon by the big data systems like Apache Hadoop for user identification and authentication. Drawbacks like single point of failure, replay attacks, time synchronization problems are yet to be dealt with appropriately in the Kerberized Hadoop clusters. This paper proposes a novel approach based on blockchain technology, digital signatures and threshold ElGamal Cryptosystem to address the problem of single point of failure.
Resolution of Minimal Solutions for Max-Lukasiewicz Fuzzy Relation Equation by Fuzzy Neural Network

Fuzzy relation equation (FRE) was introduced by Sanchez in 1976, which was extensively applied in fuzzy logic, approximate reasoning, intelligent control, artificial intelligence and so on. It is well known that solving the minimal solutions of FRE is a NP-hard problem because of its computational complexity. Many scholars have studied some algorithms to solve the minimal solutions of fuzzy relation equation. Considering that max-lukasiewicz composition operator is widely used, in this paper, we investigate the resolution of minimal solutions for max-lukasiewicz fuzzy relation equation based on fuzzy neural network and propose a novel algorithm. Through continuous downward iteration and conditional constraints, all minimum solutions of FRE are finally obtained. Both mathematical conclusion and simulation experiment show that our algorithm is effective and valid.
Implementation of Neuro-fuzzy Control Systems

Neuro-fuzzy approach for implementing control systems is considered. Neuro-fuzzy systems are a tool for a development of trainable control systems with high interpretability. These systems can be trained to work in new conditions. There is a possibility to analyze the actions, which implement the control. Examples of neuro-fuzzy control applications are presented: virtual assistant and automatic calibration system.
Design of Hydraulic Component Teaching Subsystem Based on Augmented Reality

In order to solve the problems of poor visualization and time/place limitation in traditional “hydraulic transmission” classroom teaching, a novel hydraulic components teaching subsystem based on augmented reality (AR) technology is developed, which can be used in mobile phone. Based on Android system, with the help of Vuforia tools, 3Ds Max, Unity 3D, SolidWorks and other software and technologies, the teaching subsystem has achieved a good AR effect. It has the main functions of identification of mark pictures, virtual interactive control, virtual assembly and disassembly, audio/video explanation, self examination and evaluation, etc, which is helpful for students to improve their interest in learning, to master the structure and working principle of hydraulic components, and to learn anytime and anywhere. The system provides support for the novel teaching method research of “hydraulic transmission”. It also provides a reference for the development of AR teaching system for other similar courses.
Optimization of rewrite theories by equational partial evaluation

In this paper, we develop an automated optimization framework for rewrite theories that supports sorts, subsort overloading, equations and algebraic axioms with free/non-free constructors, and rewrite rules modeling concurrent system transitions whose state structure is defined by means of the equations. The main idea of the framework is to make the system computations more efficient by partially evaluating the equations to the specific calls that are required by the transition rules. This can be particularly useful for automatically optimizing rewrite theories that contain overly general equational theories which perform unnecessary and costly computations involving pattern matching and/or unification modulo equations and axioms. The transformation is based on a suitable unfolding operator parameter that relies on the symbolic operational engine of Maude's equational theories, called folding variant narrowing, together with a generic abstraction operator. Depending on the properties of the rewrite theory, the unfolding and abstraction operators must be fine-tuned to achieve the biggest optimization possible while ensuring termination and total correctness of the transformation. We formalize two instances of our scheme for the case when the rewrite theory either has an infinite number of most general variants or a finite number of most general variants. Finally, we discuss some experimental results which demonstrate that the proposed optimization technique pays off in practice.
Model Compression Based on YOLOv3 Object Detection Algorithm from the Perspective of UAV

Recently, unmanned aerial vehicles are widely used in surveillance, aerial photography, power grid line inspections and other places. In order to deploy the YOLOv3 [1] algorithm on drones, it is necessary to adopt the YOLOv3 algorithm with fewer parameters and a simpler structure. This paper implements the model compression of YOLOv3 based on methods such as sparseness, pruning, and knowledge distillation. This paper implements the sparseness of the network by adding L1 regular expressions on the convolutional layer. After that, redundant channels and layers are removed through channel pruning and layer pruning. After sparse and pruning, the mAP lost a lot. By using knowledge distillation after pruning, it attempts to recover mAP lost in sparseness and pruning. By this method, the YOLOv3 algorithm can be deployed on embedded platforms such as RK3399pro. We evaluate the model on the visdrone2019 dataset. The experimental results show that after model compression, YOLOv3 is more suitable for deployment on embedded platforms.
TypeBoard: Identifying Unintentional Touch on Pressure-Sensitive Touchscreen Keyboards

Text input is essential in tablet computer interaction. However, tablet software keyboards face the problem of misrecognizing unintentional touch, which affects efficiency and usability [29, 49]. In this paper, we proposed TypeBoard, a pressure-sensitive touchscreen keyboard that prevents unintentional touches. The TypeBoard allows users to rest their fingers on the touchscreen, which changes the user behavior: on average, users generate 40.83 unintentional touches every 100 keystrokes. The TypeBoard prevents unintentional touch with an accuracy of 98.88%. A typing study showed that the TypeBoard reduced fatigue (p < 0.005) and typing errors (p < 0.01), and improved the touchscreen keyboard' typing speed by 11.78% (p < 0.005). As users could touch the screen without triggering responses, we added tactile landmarks on the TypeBoard, allowing users to locate the keys by the sense of touch. This feature further improves the typing speed, outperforming the ordinary tablet keyboard by 21.19% (p < 0.001). Results show that pressure-sensitive touchscreen keyboards can prevent unintentional touch, improving usability from many aspects, such as avoiding fatigue, reducing errors, and mediating touch typing on tablets.
Research on UAV Indoor Path Planning Algorithm Based on Global Subdivision Grids

UAV (Unmanned Aircraft Vehicle) could undertake rescue, traffic, reconnaissance in indoor environment. As the key of indoor autonomous flight and intelligent flight, UAV indoor path planning has great research significance. UAV path planning is to design an optimal feasible path in flight airspace given the UAV related constraints and cost functions. As a special kind of airspace environment, the building environment has complex internal environment and dense obstacles, so the UAV path planning is required to complete the operation quickly and effectively, and ensure the global optimization of the path. Based on the global three-dimensional subdivision reference grid system, this paper will establish the environment model of the spatial grid inside the building, and develop a new path planning algorithm based on the indoor spatial grid. Ultimately, the UAV will complete the path planning task quickly and efficiently inside the building.
Game Illusionization: A Workflow for Applying Optical Illusions to Video Games

Optical illusions have been brought into recent video games to enhance gaming experiences. However, a large corpus of optical illusions remains unused, while few games incorporate illusions seamlessly. To mitigate the gap, we propose a workflow to guide game designers in applying optical illusions to their video games, i.e., in making more illusion games. In particular, our workflow consists of 5 stages: (1) choosing a game object, (2) searching for a matching illusion, (3) selecting an illusion mechanic, (4) integrating the selected illusion into the game, and (5) optionally revealing the illusion. To facilitate our workflow, we provide a tag database with 163 illusions that are labeled by their in-game visual elements and desired effects. We also provide example editing interfaces of 6 illusions for game designers. We walk through our workflow and showcase 6 resulting illusion games. We implemented these 6 games (with and without illusion) and conducted a 12-participant study to gain a preliminary understanding of how illusions enhance gaming experiences. To evaluate our workflow, we invited 6 game designers and 6 experienced players to follow our workflow and design their own illusion games, where 3 experienced game designers completed 2-week in-depth developments. We report their games, qualitative feedback and discuss reflection on our workflow, database and editing interfaces.
The politics of piracy: political ideology and the usage of pirated online media

Abstract There is a lack of clarity in information systems research on which factors lead people to use or not use technologies of varying degrees of perceived legality. To address this gap, we use arguments from the information systems and political ideology literatures to theorize on the influence of individuals’ political ideologies on online media piracy. Specifically, we hypothesize that individuals with a more conservative ideology, and thus lower openness to experience and higher conscientiousness, generally engage in less online media piracy. We further hypothesize that this effect is stronger for online piracy technology that is legally ambiguous. Using clickstream data from 3873 individuals in the U.S., we find that this effect in fact exists only for online media piracy technologies that are perceived as legally ambiguous. Specifically, more conservative individuals, who typically have lower ambiguity intolerance, use (legal but ambiguously perceived) pirated streaming websites less, while there is no difference for the (clearly illegal) use of pirated file sharing websites.