Reviewerly/exHarmony huggingface dataset:
Image Restoration using Plug-and-Play CNN MAP Denoisers

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type.These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP denoising optimization.We present the first end-to-end approach to MAP estimation for image denoising using deep neural networks.We show that our method is guaranteed to minimize the MAP denoising objective, which is then used in an optimization algorithm for generic image restoration.We provide theoretical analysis of our approach and show the quantitative performance of our method in several experiments.Our experimental results show that the proposed method can achieve 70x faster performance compared to the state-of-the-art, while maintaining the theoretical perspective of MAP.
Digital Literacy Concepts and Measurement

An innovative development based on the use of modern media and communication technologies requires a certain level of competence in how to use such technologies. These competencies are united by the concept of “information literacy”, proposed by Paul Gilster in 1997. The tradition of studying digital literacy in Russia is the subject of the following chapter. The different approaches to understanding digital literacy are as follows: ICT, psychological and pedagogical, media and information and industrial approaches. Special attention is paid to the four-component digital literacy model, proposed in the framework of the project by ROCIT and the Higher School of Economics. This model is based on two substantial oppositions: firstly, the opposition “technical-technological/socio-humanitarian” and, secondly, the opposition “opportunities/threats”. It was used to construct the Index of Digital Literacy in the Russian Regions, measured since 2015. The results of a series of media literacy measuring surveys by the ZIRCON Group from 2009–2016 are also presented.
Overview of Collaborative Filtering Recommendation Algorithms

Abstract In this paper, the key technologies of collaborative filtering recommendation algorithm and the bottleneck in its development are summarized, the problems of different technologies are analyzed. Besides, the application prospect of collaborative filtering technology is prospected.
Finding summaries to obtain event phrases from streaming Microblogs using Word Co-occurrence Network

Event detection from social media data has gained much momentum in research community. Existing event detection techniques are not scalable over less frequently discussed events. To handle this, BArank was proposed to identify keyphrases from topic specific Twitter data. Another concern for event detection from Microblogs is handling streaming data. Real-time Tweets are handled using Hadoop platform, or by referring old and new corpus which are complex and computationally expensive. However, handling streaming data depends upon the event detection technique. In this research work, different parameters are proposed to obtain discrete set of Tweets. The resulting phrases from BArank are ranked using Multi-Criteria Decision Making approach of Analytical Hierarchical Process (AHP) to identify events. Experimental results have proved high effectiveness of the proposed approach for FA Cup dataset with improved keyword-precision and keyword-recall without affecting topic-recall.
Hand Gesture Recognition with Ensemble Time-Frequency Signatures Using Enhanced Deep Convolutional Neural Network

Hand gesture recognition using radar has been widely applied to control electronic appliances, military appliances and so on. In this paper, we investigate the feasibility of recognizing hand gestures using fused multiple time-frequency signatures, which ensembles micro-Doppler signatures, range-time signatures and angle-time signatures on spectrograms, with an Enhanced Deep Convolutional Neural Network (EDCNN). Several typical gestures included Tick, Double pushing, Rotating clockwise, and Rotating counterclockwise, were measured using Mm-wave radar and their spectrograms investigated. Therein EDCNN was employed to classify the spectrograms, with 80% of the data utilized for training and the remaining 20% for validation. Simulation said that the classification accuracy of the proposed method was found to be 96.2%.
A Multi-waveform Radio Receiver, an Example of Machine Learning Enabled Radio Architecture and Design

We describe a machine learning enabled architecture and design for a multi-waveform radio receiver in the pursuit of a truly cognitive radio with more functionality and adaptability than current software defined radio implementations. This machine learning approach brings closer to reality the vision of cognitive radios proposed by Joseph Mitola III and Gerald Q. Maguire, Jr. Cognitive radios make decisions about their communications regime about where (in spectrum), and how (waveform parameters) to transmit and receive information [1]. And, such radios should be able to self-optimize their communications to most efficiently maximize data capacity in power and spectrum constrained environments. To achieve these goals, the software in the radio must control more of the functionality, including functions in the physical layer. Building on Tim O'Shea's and Jakob Hoydis' ideas [2], we have developed a generalized architecture, in which the physical layer functions: Frequency correction, timing correction, demodulation, and bit error correction are performed by an artificial neural network capable of processing several signal types and waveforms.
Multiple Interactive Attention Networks for Aspect-Based Sentiment Classification

Aspect-Based (also known as aspect-level) Sentiment Classification (ABSC) aims at determining the sentimental tendency of a particular target in a sentence. With the successful application of the attention network in multiple fields, attention-based ABSC has aroused great interest. However, most of the previous methods are difficult to parallelize, insufficiently obtain, and fuse the interactive information. In this paper, we proposed a Multiple Interactive Attention Network (MIN). First, we used the Bidirectional Encoder Representations from Transformers (BERT) model to pre-process the data. Then, we used the partial transformer to obtain a hidden state in parallel. Finally, we took the target word and the context word as the core to obtain and fuse the interactive information. Experimental results on the different datasets showed that our model was much more effective.
An Enhancement of Malay Social Media Text Normalization for Lexicon-Based Sentiment Analysis

Nowadays, most Malaysians use social media such as Twitter to express their opinions toward any latest issues publicly. However, user individuality and creativity of language create huge volumes of noisy words which become unsuitable as dataset for any Natural Language Processing applications such as sentiment analysis due to the irregularity of the language featured. Thus, it is important to convert these noisy words into their standard forms. Currently, there are limited studies to normalize the noisy words for Malay language. Hence, the aim of this study is to propose an enhancement of Malay social media text normalization for lexicon-based sentiment analysis. This normalizer comprises six main modules: (1) advanced tokenization, (2) Malay/English token detection, (3) lexical rules, (4) noisy token replacement, (5) n-gram, and (6) detokenization. The evaluation has been conducted and the findings show that 83.55% achieved in Precision and 84.61% in Recall.
Issues of Quality Assessing of Stochastic Transformations Results

The paper considers methods for generating numerical sequences using deterministic algorithms and their properties. The requirements for various types of pseudorandom sequence generators are presented. Issues of the research of numerical sequences by statistical and graphical methods are considered. An approach to assessing the quality of stochastic transformations results used for information security is described.
Densely-Sampled Light Field Reconstruction

In this chapter, we motivate the use of densely-sampled light fields as the representation which can bring the required density of light rays for the correct recreation of 3D visual cues such as focus and continuous parallax and can serve as an intermediary between light field sensing and light field display. We consider the problem of reconstructing such a representation from few camera views and approach it in a sparsification framework. More specifically, we demonstrate that the light field is well structured in the set of so-called epipolar images and can be sparsely represented by a dictionary of directional and multi-scale atoms called shearlets. We present the corresponding regularization method, along with its main algorithm and speed-accelerating modifications. Finally, we illustrate its applicability for the cases of holographic stereograms and light field compression.
Domain Specific Fusion of Unstructured Text for Situation Understanding (Poster)

This paper presents the initial design and the current and envisaged functionalities of a novel tool for information extraction and reasoning from open source data (OSD), namely, the Open Source Information Collection, Analysis and Reasoning (OSCAR). It has the ability to ingest and process vast amount of OSD to provide situation understanding and decision support about domain specific situations. The data are pre-filtered using a custom created knowledge base (KB) while the information is extracted using the Rule Based Information Extraction (RuBIE), a Natural Language Processing (NLP) and tagging tool. The extracted information is subsequently clustered and transformed into a relation graph of entities of interest. This proof of concept is presented in the context of a use case based on the social crisis in Venezuela in 2019.
Handling Semantic Inconsistencies in Commonsense Knowledge for Autonomous Service Robots

The necessary amount of commonsense knowledge for autonomous domestic service robots is enormous and avoiding inconsistencies is consequently almost impossible. This is especially true for inconsistencies on a semantic level, that is, inconsistencies as interpreted by human beings where the properties of a concept may contradict each other. In contrast to Boolean algebra (¬A and A), these inconsistencies will not lead to a trivial solution like false, since they are not captured in a logic formalism. Instead, they will result in semantic contradictions when interpreting the solution. In order to tackle this problem, we propose a way to handle semantic inconsistencies, by utilising a commonsense knowledge database and implementing a dynamic knowledge base that is capable of resolving contradicting parts of knowledge. The applied non-monotonic reasoning mechanism is Answer Set Programming, and ConceptNet5 is the commonsense knowledge database. Finally, we evaluate the performance of our approach and show its advantages in a complete household scenario.
Distributed PV generation estimation using multi‐rate and event‐driven Kalman kriging filter

The ever-growing penetration of cost-effective photovoltaic (PV) panels within the distribution grid requires a robust and efficient method for PV system monitoring. Especially, the geographical proximity of PV panels can play an important role in lowering the dimension of measurements required for full system observability. Furthermore, the direct impact of variable cloud formation and uncertain propagation necessitates the development and validation of a spatiotemporal model. Accordingly, this study presents the modelling and validation of the spatiotemporal variability of solar power indices at 1 minute resolution for the scale of a residential neighbourhood. The spatiotemporal model is then applied to a Multi-Rate and Event-DRIven Kalman Kriging (MREDRIKK) filter to dynamically estimate behind-the-meter PV generation. The Kriging step exploits spatial correlations to estimate PV power output at locations from where measurements are unobserved. The multi-rate feature of the MREDRIKK filter enables the sampling of measurements at a rate much lower than the temporal dynamics of the associated states. A comprehensive study is undertaken to investigate the effect of multi-rate and event-driven measurement updates on the performance of the MREDRIKK filter. In addition, the superior performance of MREDRIKK filter is represented as compared to the persistence method irrespective of the observation size.
Incorporating a Virtual Reality Environment in the Teaching of Analysis of Software Requirements

This Work in Progress paper in the Innovative Practice Category presents an approach to incorporate the use of a virtual reality environment in the teaching of analysis of software requirements through modeling. Common models include use case diagrams, state charts, data flow diagrams, etc., all serving their own role in defining different perspectives of a system. These different perspectives of the same system can be a powerful technique to facilitate the analysis of requirements about the system. Using a virtual reality environment allows for the linking of multiple perspectives from the separate analysis models to be viewed in three dimensions. This approach can be particularly beneficial to students in learning how to analyze models by providing a mechanism to examine the visual relationship between the perspectives. Using this approach allows the student to view, create, manipulate, and alter models within the native three-dimensional space, which can provide an indepth experience better suited for learning. The virtual reality environment also allows models to be nested to provide better abstractions and layered to provide a level of complexity impossible to replicate in the traditional two-dimensional space. This paper explores a prototype virtual reality environment to facilitate the use of software requirement models for undergraduate software engineering students. As part of this work, students will be exposed to the tool and tasked with using the virtual reality environment to analyze the models of systems. Assessment of the use of the approach will be conducted and results will be presented including feedback from the students, benefits found, insights and potential ways to evolve the approach for other areas such as design or testing.
Batch Active Learning With Two-Stage Sampling

Due to its effectiveness in training precise model using significant fewer labeled instances, active learning has been widely researched and applied. In order to reduce the time complexity of active learning so that the oracle need not wait for the algorithm to provide instance in labeling, we proposed a new active learning method, which leverages batch sampling and direct boundary annotation with a two-stage sampling strategy. In the first stage sampling, the initial seed, which determines the location of boundary annotation, is selected with reject sampling based on the clustering structure of instances to ensure the initial seeds can approximate the distribution of data and with high diversity. In the second stage sampling, by treating the instance sampling as the selection of representative in a local region and maximizing the rewards that can get from selecting a instance as the new representative, we proposed a novel mechanism to maintain local representativeness and diversity of query instances. Compared with the conventional pool-based active learning method, our proposed method does not need to train the model in each iteration, which reduces the amount of calculation and time consumption. The experimental results in three public datasets show that the proposed method has comparable performance with the uncertainty-based active learning methods, which proves that the sampling mechanism in our method is effective. It performs well without retraining the model in each iteration and does not rely on the precision of the model.
A Study on the Uncertainty of a Laser Triangulator Considering System Covariances

A laser triangulation system, which is composed of a camera and a laser, calculates distances between objects intersected by the laser plane. Even though there are commercial triangulation systems, developing a new system allows the design to be adapted to the needs, in addition to allowing dimensions or processing times to be optimized; however the disadvantage is that the real accuracy is not known. The aim of the research is to identify and discuss the relevance of the most significant error sources in laser triangulator systems, predicting their error contribution to the final joint measurement accuracy. Two main phases are considered in this study, namely the calibration and measurement processes. The main error sources are identified and characterized throughout both phases, and a synthetic error propagation methodology is proposed to study the measurement accuracy. As a novelty in uncertainty analysis, the present approach encompasses the covariances of correlated system variables, characterizing both phases for a laser triangulator. An experimental methodology is adopted to evaluate the measurement accuracy in a laser triangulator, comparing it with the values obtained with the synthetic error propagation methodology. The relevance of each error source is discussed, as well as the accuracy of the error propagation. A linearity value of 40 µm and maximum error of 0.6 mm are observed for a 100 mm measuring range, with the camera calibration phase being the main error contributor.
A Domain Model for Personalized Monitoring System Based on Context-Aware Data Fusion

Monitoring systems have been applied in many fields, but it is still challenging to design a monitoring system to deal with personalization of monitoring and unpredictability of events. We propose a domain model that is useful in designing real personalized monitoring system based on context-aware data fusion in the initial stage of the design phase of the development cycle. This domain model enables a clear understanding of the PMS domain and the specific vocabulary that comes with its facilities discussions among practitioners in the field. The domain model provides a conceptual perspective that shows all the important concepts which are needs to handle with personalization of monitoring and unpredictability of events in the domain of monitoring systems. Our approach is illustrated through domain modelling of a case.
Facial Expression Recognition using Facial Features Detection using the Fusion of Classifiers: In a real-time scenario

For the communication, there are two modes of communication i.e. verbal and non-verbal. The speech is the medium of verbal communication whenever the gestures and the facial expressions are the medium of non-verbal communication. In our daily social life, the facial expressions play an important role to convey some information between the speaker and the listener. The facial expressions can not only change the flow of conversation but also provide some contextual information to the listener, therefore, the listener can easily sense the actual state of the convener. Facial expression express human's inner emotional states. In this paper, an approach has been proposed to detect the specific region of the face which consist of very high information for emotion recognition. In this work, the approach is to detect some particular region of the face instead to detect all facial parts. Therefore this approach is faster than the existing approaches for facial expression recognition. For the classification, a fusion approach has been used and achieved a good recognition rate with the combination of HOG and LBP features.
Overview of Mining Software Repositories

Mining of software repositories (MSR) provides opportunities to enhance software engineering activities and to analyze violations to prevent it. Software engineering quality and security challenges can be solved by analyzing unstructured information from various artifacts of software development. This paper describes current challenges and applications in the field. This work also proposes an overview of the directions of mining software repositories and describes the methods and techniques that are used in this area.
Fault-Tolerant Logical Hadamard Gates Implementation in Reed-Muller Quantum Codes

We investigate how to implement fault-tolerant logical Hadamard gates in Reed-Muller quantum codes(RMQCs) using the gauge-fixing method. During the realization, we consider the influence of random single-qubit errors by performing the error-detecting measurements. Moreover, some error-detecting stabilizers are simplified by the existing syndromes. Then we identify the errors and modify the syndromes, and refer to the modified syndromes to select the fix operations, and finally perform the error-correcting and fix operations together. Further, we establish a graph model for the RMQCs and exhibit a progress of how to find the fix operations for the unsatisfied stabilizers. We simulate the progress of finding corresponding fix operations for 31-quibt and 63-qubit RMQCs and the whole process of realizing logical Hadamard gate with random single-qubit errors for 15-qubit and 31-quibt RMQCs. Results show that correct fix operations can be obtained and fault-tolerant logical Hadamard gates can be realized as expected. With the implementation of the logical Hadamard gate, a universal fault-tolerant gate set is achieved in single RMQC.
Accelerating convolutional neural network training using ProMoD backpropagation algorithm

Convolutional neural networks (CNNs) play an important role in image recognition applications. Fast training of image recognition systems is a crucial point, because the system should be trained for each new image class. These networks are trained using lengthy calculations. Focus of engineering is on obtaining a fast, but stable optimisation method. Momentum technique which is used in backpropagation algorithms is like a proportional–integral (PI) controller that is widely employed in automatic control systems. It takes the integral of past errors and helps reaching the training targets. Proportional + momentum + derivative (ProMoD) method adds gradient of update matrices to the training process and builds an optimiser such as the widely used PI–derivative controller. The method accelerates the movement toward the target accuracy levels. This is achieved by doing bigger corrections in the beginning using the differences in the calculated update matrices. In this research, ProMoD method is tested on image recognition applications and CNNs. Modified national institute of standards and technology database (MNIST) and Fashion-MNIST datasets are used for evaluating the performance. Experimental results showed that ProMoD might perform much faster in training of CNNs and consume proportionally less power with respect to the momentum and stochastic gradient descent (SGD) techniques.