Reviewerly/exHarmony huggingface dataset:
Voting over a Distributed Ledger: An interdisciplinary perspective

This work discusses the potential of a blockchain based infrastructure for a decentralised online voting platform. When compared to paper based voting, online voting can vastly increase the speed that votes can be counted, expand the overall accessibility of the election system and decrease the cost of turnout. Yet despite these advantages, online voting for political office is subject to fraud at various levels due to its centralised nature. In this paper, we describe a general architecture of a centralised online voting system and detail which areas of such a system are vulnerable to electoral fraud. We then proceed to introduce the key ideas underlying blockchain technology as a decentralised mechanism that can address these problems. We discuss the advantages and weaknesses of the blockchain technology, the protocols the technology uses and what criteria a good blockchain protocol should satisfy (depending on the voting application). We argue that the decentralisation inherent in the blockchain technology could increase the public's trust in national elections, as well as eliminate voter impersonation and double voting. We conclude with a discussion regarding how economists and social scientists can collaborate with the blockchain community in a research agenda on the design of efficient blockchain protocols and new voting systems such as liquid democracy.
Distributed Information Integration in Convolutional Neural Networks

A large body of physiological findings has suggested the vision system understands a scene in terms of its local features such as lines and curves.A highly notable computer algorithm developed that models such behaviour is the Convolutional Neural Network (CNN).Whilst recognising an object in various scales remains trivial for the human vision system, CNNs struggle to achieve the same behaviour.Recent physiological findings are suggesting two new paradigms.Firstly, the visual system uses both local and global features in its recognition function.Secondly, the brain uses a distributed processing architecture to learn information from multiple modalities.In this paper we combine these paradigms and propose a distributed information integration model called D-Net to improve scale-invariant classification of images.We use a CNN to extract local features and, inspired by Google's INCEPTION model, develop a trainable method using filter pyramids to extract global features called Filter Pyramid Convolutions (FPC).D-Net locally processes CNN and FPC features, fuses the outcomes and obtains a global estimate via the central processor.We test D-Net on classification of scaled images on benchmark datasets.Our results show D-Net's potential effectiveness towards classification of scaled images.
Word Sense Disambiguation in Telugu Language Using Knowledge-Based Approach

In NLP, many languages will have many ambiguous words, and finding the correct meaning of an ambiguous word is known as word sense disambiguation. This research article is to develop a WSD system for regional Telugu language. Word sense disambiguation system can be developed using three approaches; in this work, we are using knowledge-based approach, where the accuracy is more than unsupervised approaches. In regional Telugu language, research work on word sense disambiguation is not up to the mark. In English and Hindi languages, word sense disambiguation systems are developed using all the three approaches.
Towards a Cyber-physical Systems Resilience Approach based on Artificial Emotions and Multi-agent Systems

The concept of resilience is popular and studied in different domains like human and social sciences (psychology, psychiatry, sociology etc.) and recently in cognitive science, biology, ecology and computer science. The objective of this article is to present our research that aims to incorporate knowledge from human and social sciences in computer science to solve cyber-physical systems resilience problems. For us humans, emotions are considered as an important process in responding to unanticipated events, for that, emotions are important for our resilience. Our work aims to inspire from emotional processes to create an agent model that increases the resilience of cyber-physical systems. This agent model will integrate individual and collective processes. In addition, one of our principal hypotheses in our research is that the multi-agent paradigm is suitable to integrate emotion-like processes into cyber-physical systems.
Dictionary based Compression Type Classification using a CNN Architecture

As digital devices which are capable of viewing contents easily such as mobile phones and tablet PCs have become widespread, the number of digital crimes using these digital contents also increases. Usually, the data which can be the evidence of crimes is compressed and the header of data is damaged to conceal the contents. Therefore, it is necessary to identify the characteristics of the entire bits of the compressed data to discriminate the compression type not using the header of the data. In this paper, we propose a method for distinguishing 16 dictionary-based compression types. We utilize 5-layered Convolutional Neural Network (CNN) for classification of compression type using Spatial Pyramid Pooling (SPP) layer. We evaluate our proposed method on the Wikileaks Dataset, which is a text file database. The average accuracy of 16 dictionary-based compression algorithms is 99%. We expect that our proposed method will be useful for providing evidence for Digital Forensics.
Predicting Solar Irradiance in Singapore

Solar irradiance is the primary input for all solar energy generation systems. The amount of available solar radiation over time under the local weather conditions helps to decide the optimal location, technology and size of a solar energy project. We study the behaviour of incident solar irradiance on the earth's surface using weather sensors. In this paper, we propose a time-series based technique to forecast the solar irradiance values for shorter lead times of upto 15 minutes. Our experiments are conducted in the tropical region viz. Singapore, which receives a large amount of solar irradiance throughout the year. We benchmark our method with two common forecasting techniques, namely persistence model and average model, and we obtain good prediction performance. We report a root mean square of 147 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for a lead time of 15 minutes.
Color-Guided Depth Map Super-Resolution Using a Dual-Branch Multi-Scale Residual Network with Channel Interaction

We designed an end-to-end dual-branch residual network architecture that inputs a low-resolution (LR) depth map and a corresponding high-resolution (HR) color image separately into the two branches, and outputs an HR depth map through a multi-scale, channel-wise feature extraction, interaction, and upsampling. Each branch of this network contains several residual levels at different scales, and each level comprises multiple residual groups composed of several residual blocks. A short-skip connection in every residual block and a long-skip connection in each residual group or level allow for low-frequency information to be bypassed while the main network focuses on learning high-frequency information. High-frequency information learned by each residual block in the color image branch is input into the corresponding residual block in the depth map branch, and this kind of channel-wise feature supplement and fusion can not only help the depth map branch to alleviate blur in details like edges, but also introduce some depth artifacts to feature maps. To avoid the above introduced artifacts, the channel interaction fuses the feature maps using weights referring to the channel attention mechanism. The parallel multi-scale network architecture with channel interaction for feature guidance is the main contribution of our work and experiments show that our proposed method had a better performance in terms of accuracy compared with other methods.
CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data

Predicting which words are considered hard to understand for a given target population is a vital step in many NLP applications such as text simplification. This task is commonly referred to as Complex Word Identification (CWI). With a few exceptions, previous studies have approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) for a set of target words in a text. This choice is motivated by the fact that all CWI datasets compiled so far have been annotated using a binary annotation scheme. Our paper addresses this limitation by presenting the first English dataset for continuous lexical complexity prediction. We use a 5-point Likert scale scheme to annotate complex words in texts from three sources/domains: the Bible, Europarl, and biomedical texts. This resulted in a corpus of 9,476 sentences each annotated by around 7 annotators.
Psychometric evaluation of the Pinocchio Illusion Questionnaire

Perceived nose elongation resulting from vibratory stimulation to the bicep brachii tendon in the absence of visual input while the finger is touching the nose, known as the Pinocchio Illusion (PI), is used to investigate how afferent signals can contribute to aberrant top-down perception of body representation. The Pinocchio Illusion Questionnaire (PIQ) was developed to empirically quantify PI perception, allowing for external validation of the PI with psychologically relevant phenomenon. The current study (N = 60) examined the PIQ’s test-retest reliability, internal consistency, factor structure, and correlations with self-reported interoceptive awareness and schizotypal traits. The PIQ demonstrated strong test-retest reliability and internal validity; however, a Principal Component Analysis did not yield a latent variable structure that distinguished PI-specific perceptual aberrations from unrelated or contradictory perceptual experiences. Additionally, decreased reports of PI-specific perceptual aberrations during two elicitations of the PI on the PIQ’s open-ended free-response section (percent of sample endorsement = 5% (first elicitation); 8.3% (second elicitation)) compared to its 11-item section (endorsement of PI-specific items ranging 30–53.33% (first)]; 31.67–46.67% (second)) suggest that these responses may be heavily influenced by demand characteristics rather than accurately capturing PI perception. Therefore, further psychometric development of the PIQ and standardization of procedures to elicit the illusion are recommended.
A new method for detecting surface defects on curved reflective optics using normalized reflectivity

Detection of surface defects is critical in quality control of reflective optics. In this note, we propose a new surface defect detection method for reflective optics using the normalized reflectivity, which is calculated from the signal intensity of a chromatic confocal surface profiler. This detection method first scans the surface to acquire signal intensity data and then models the intensity data to calculate the normalized local reflectivity map. The reflectivity map is further processed by threshold segmentation to extract defects from normal areas. Measurement experiments on an Al-coated concave reflector with artificial defects were carried out to demonstrate the feasibility of the method. This detection method can provide existing optical surface profilers with defect detecting capabilities without extra equipment.
Procedure for the transfer of standards into machine-actionability

The aim of this research is to establish clear concepts for working with standards (e.g. ISO) in the context of connected factory. When machines or other output devices need to communicate with each other, e.g., to autonomously perform processes in manufacturing processes, all the necessary information must be accessible to them. Today, however, the required information is documented in standards that are only available in PDF or even paper form. Thus, a prerequisite for using information from standards is that these are automatically provided in output devices. A deeper look leads to three levels of maturity for using standard information in output devices: machine-readable, machine-actionable and machine-interpretable. Starting from a clear definition of these terms an approach is developed to implement machine-actionable standards. It is assumed that not all standards are suitable for machine-actionability and therefore the first step is to classify the standards in order to identify the appropriate standards. The second step describes how information from standards can be modelled to be available in a machine-actionable form. The last step clarifies how the machine-actionable content must be provided afterwards so that they can be used in all output devices with less effort. This research work closes with the validation of the developed approach.
A Comprehensive Study of Clustering Algorithms for Big Data Mining with MapReduce Capability

Big data mining is modern scientific research, which is used by all data related fields such as communication, computer, biology, geographical science, and so on. Basically, big data is related to volume, variety, velocity, variability, value, veracity, and visualization. Data mining technique is related to extract needed information, knowledge and hidden pattern, relations from large datasets with the heterogeneous format of data, which is collected by multiple sources. Data mining have classification, clustering, and association techniques for big data mining. Clustering is one of the approaches for mining, which is used for mine similar types of data, hidden patterns, and related data. All traditional clustering data mining approaches, such as partition, hierarchical, density, grid, and model-based algorithm, works on only high volume or high variety or high velocity. If we Apply the traditional clustering algorithms for big data mining then these algorithms will not work in the proper manner, and they need such clustering algorithms that work under high volume, high variety and high velocity. This paper presents the introduction to big data, big data mining, and traditional clustering algorithms concepts. From a theoretical, practical, and existing research perspective, this paper categorized clustering framework based on volume (dataset size, dimensional data), variety (dataset type, cluster shape), and velocity (scalability, time complexity), and presented a common framework for scalable and speed-up any type of clustering algorithm with MapReduce capability and shown this MapReduce clustering framework with the help of K-means algorithm.
Environment-assisted bosonic quantum communications

Abstract We consider a quantum relay that is used by two parties to perform several continuous-variable protocols of quantum communication, from entanglement distribution (swapping and distillation) to quantum teleportation, and quantum key distribution. The theory of these protocols is suitably extended to a non-Markovian model of decoherence characterized by correlated Gaussian noise in the bosonic environment. In the worst-case scenario where bipartite entanglement is completely lost at the relay, we show that the various protocols can be reactivated by the assistance of classical (separable) correlations in the environment. In fact, above a critical amount, these correlations are able to guarantee the distribution of a weaker form of entanglement (quadripartite), which can be localized by the relay into a stronger form (bipartite) that is exploitable by the parties. Our findings are confirmed by a proof-of-principle experiment where we show, for the first time, that memory effects in the environment can drastically enhance the performance of a quantum relay, well beyond the single-repeater bound for quantum and private communications.
Broadening Label-Based Argumentation Semantics with May-Must Scales

The semantics as to which set of arguments in a given argumentation graph may be acceptable (acceptability semantics) can be characterised in a few different ways. Among them, the labelling-based approach allows for a concise and flexible determination of acceptability statuses of arguments through assignment of a label indicating acceptance, rejection, or undecided to each argument. In this work, we contemplate a way of broadening it by accommodating may- and must- conditions for an argument to be accepted and rejected, as determined by the number(s) of rejected and accepted attacking arguments. We show that the broadened label-based semantics can be used to express more mild indeterminacy than inconsistency for acceptability judgement when, for example, it may be the case that an argument is accepted and when it may also be the case that it is rejected. We identify that finding which conditions a labelling satisfies for every argument can be an undecidable problem, which has an unfavourable implication to semantics. We propose to address this problem by enforcing a labelling to maximally respect the conditions, while keeping the rest that would necessarily cause non-termination labelled undecided.
Autonomous system to control a mobile robot

This paper presents an ongoing effort to control a mobile robot in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this paper, the fuzzy controller is used to tackle the problem of mobile robot autonomous navigation in unstructured environment. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the fuzzified, adaptive inference engine and defuzzification engine. Also number of linguistic labels is optimized for the input of the mobile robot in order to reduce computational time for real-time applications. The proposed fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optimization Embedded Reinforcement Learning

Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training. Each operation has a set of hyper-parameters, which can become irrelevant for the pipeline when the operation is not selected. This gives rise to a hierarchical conditional hyper-parameter space. To optimize this mixed continuous and discrete conditional hierarchical hyper-parameter space, we propose an efficient pipeline search and configuration algorithm which combines the power of Reinforcement Learning and Bayesian Optimization. Empirical results show that our method performs favorably compared to state of the art methods like Auto-sklearn, TPOT, Tree Parzen Window, and Random Search.
Including widespread geometry schemas into Linked Data-based BIM applied to built heritage

A reliable data exchange – often including geometry-related data – between stakeholders is crucial in construction projects. In this regard, data exchange frameworks built on Linked Data principles are very promising for combining disparate data sets. However, existing proposals to combine geometry and Linked Data either demand dedicated applications or support only a limited number of common geometry schemas. If any existing geometry schema could be used in a Linked Data context, error-prone geometry conversions are avoided and stakeholders do not need to invest in new geometry engines. In this paper, the applicability of Resource Description Framework (RDF) literals for including a wide variety of existing geometry schemas is studied and applied in a built heritage context. The uniform linking pattern and related terminology of the Ontology for Managing Geometry are used to implement this approach. Subsequently, the File Ontology for Geometry formats and Geometry Metadata Ontology are developed to ease the reuse of linked geometry descriptions. The effectiveness of the entire data structure is demonstrated in a built heritage case study project. The receiving party is able to create successfully a coordinated view – using a demo web application – on shared, but disparate, RDF data sets containing geometry descriptions.
Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset

With the mass-market adoption of dual-camera mobile phones, leveraging stereo information in computer vision has become increasingly important. Current state-of-the-art methods utilize learning-based algorithms, where the amount and quality of training samples heavily influence results. Existing stereo image datasets are limited either in size or subject variety. Hence, algorithms trained on such datasets do not generalize well to scenarios encountered in mobile photography. We present Holopix50k, a novel in-the-wild stereo image dataset, comprising 49,368 image pairs contributed by users of the Holopix mobile social platform. In this work, we describe our data collection process and statistically compare our dataset to other popular stereo datasets. We experimentally show that using our dataset significantly improves results for tasks such as stereo super-resolution and self-supervised monocular depth estimation. Finally, we showcase practical applications of our dataset to motivate novel works and use cases. The Holopix50k dataset is available at http://github.com/leiainc/holopix50k
Application of a Novel Wavelet Shrinkage Scheme to Partial Discharge Signal Denoising of Large Generators

Partial Discharge (PD) measurements of large generators are extremely affected and hampered by noise, making the denoising of PD signal an inevitable issue. Wavelet shrinkage is the most commonly employed method for PD signal denoising. The appropriate mother wavelet and decomposition level are critically important for the denoising performance. In consideration of the PD signal characteristics of large generators, a novel wavelet shrinkage scheme for PD signal denoising is presented. In the scheme, a scale dependent wavelet selection method is proposed; the core idea is that the optimum wavelet at each scale is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, which correspond to the signal to be reserved and noise to be removed, respectively. In addition, taking into account the influence of mother wavelet at each scale on the decomposition level, an approach for decomposition level determination is put forward based on the energy composition after decomposition at each scale. The application results on the simulated signals with different SNR obtained by combining the various pulses and measured signal on-site show the effectiveness of the proposed scheme. Besides, the denoising results are compared with that of the existing wavelet selection methods and the proposed wavelet selection method shows an obvious advantage.
Application of behavior tree in AI design of MOBA games

In e-sports, MOBA (Multiplayer Online Battle Arena) games occupy an important position. Its competitive features and game mechanics have been recognized and loved by players. Game AI (Artificial Intelligence) generally uses FSM (Finite State Machine) to achieve, but MOBA game players have higher requirements for Game AI. In order to satisfy players' game experience better, Game AI needs to be optimized. By analyzing the characteristics of MOBA games and the advantages of implementing hierarchical logic through behavior tree, this paper studies the role of behavior tree in the decision-making system of MOBA game AI design, so as to help game developers design games that meet the needs of players.