Reviewerly/exHarmony huggingface dataset:
An Evaluation of Objective Image Quality Assessment for Thermal Infrared Video Tone Mapping

State-of-the-art thermal infrared cameras produce high quality images with a bit depth of up to 16 bits per pixel (bpp). Inpractice, the data often reach a bit depth of 14 bpp, which cannot be displayed naively to a standard monitor that is limited to 8 bpp. Therefore, the dynamic range of these images has to be compressed. This can be done with an operator called tone mapping. There are many methods available for tone mapping, but the quality of the results can be extremely different. In this paper, we discuss and evaluate image quality assessment measures for tone mapping taken from the literature using thermal infrared videos. The usefulness of the measures is analyzed and effectively demonstrated by utilizing various reference Tone Mapping Operators (TMOs) based on traditional algorithm engineering on the one hand and deep learning on the other hand. We conclude that the chosen measures can objectively assess the quality of TMOs in thermal infrared videos.
Proposal of New Activation Function in Deep Image Prior

We propose a new activation function and verify its performance in a deep image prior network. The new activation function is RSwish, which is Swish with a random slope for x &lt; 0 assuming that the input value of the activation function is x . In addition, we manipulate the probability of the random number and observe the effect. As a result, we found that RSwish can perform better than Swish by manipulating probabilities according to the degree of color change in the image. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Enhancing the Interactive Visualisation of a Data Preparation Tool from in-Memory Fitting to Big Data Sets

In order to derive reliable insights or make evidence-based decisions, the starting point is to assess and meet a minimum quality of data, either by those that publish the data (preferably) or alternatively by those that prepare data for analysis and develop specific analytics. Much of the (open) data shared by governments and different institutions, or crowdsourced, is in tabular format, and the amount and size of it is increasing rapidly. This paper presents the challenges faced and the solutions adopted while evolving the web-based graphical user interface (GUI) of a tabular data preparation tool from in-memory fitting to Big Data sets. Traditional standalone processing and rendering solutions are no longer usable in a Big Data context. We report on the approach adopted to asynchronously pre-compute the visualisations required for the tool, in addition to the applied visualisation aggregation strategies. The implementation of this approach has allowed us to overcome web-browsers’ client-side data handling limitations and to avoid information overload when using granular information charts from our existing in-memory data preparation tool with Big Data sets. The developed solution provides the user with an acceptable GUI interaction time.
Minimal completion of <i>I</i> × <i>I</i> doubly substochastic matrices

For an n×n doubly substochastic matrix A, the sub-defect of A is the smallest integer k so that by adding k rows and columns, A becomes a doubly stochastic matrix. In this paper, we introduce the notion of sub-defect for an arbitrary I×I doubly substochastic matrix A=[aij]i,j∈I by using cardinal numbers and then show that any doubly substochastic matrix can be turned into a doubly stochastic matrix by adding some rows and columns. Then we show that for such a matrix the sub-defect of A, denoted by sd(A), is a cardinal number that is between the 0,1,…, and card(I). We will characterize all finitely valued sub-defect matrices. Also, the sub-defect of all summable doubly substochastic matrices will be obtained.
Sentiment analysis of film reviews based on CNN-BLSTM-Attention

Abstract In order to accurately analyse the emotional tendency of film reviews, help investors make decisions and improve the quality of works, an optimized CNN-BLSTM-Attention sentiment analysis model was designed. The CNN model has a strong ability to capture the local correlation of spatial or temporal structures. The RNN model can either process sequences of any length or capture long-range dependencies, but it is easy to cause the problem of gradient disappearance. The CNN-BLSTM-Attention sentiment analysis model designed in this paper, which combines the advantages of CNN and RNN, is more accurate when being used to analyze the sentiment characteristics of texts. The experimental results show that the accuracy of the CNN-BLSTM-Attention after optimization model is better than that of CNN and RNN models in the experiment, which proves the effectiveness of the analysis method in this paper and can provide some significance for the optimization of related sentiment analysis models.
Modeling and Analysis of Heterogeneous Traffic Networks with Anarchists and Socialist Traffic

In this paper, we consider a heterogeneous traffic network with multiple users classes which differ considerably in their path selection objective. In particular, we consider two classes of users: ones who seek to minimize social cost (socialists) and the ones with typical greedy objective (anarchists) which leads to a heterogeneous game termed as HetGame. The paper proposes an analytical framework to derive optimal/equilibrium flow in such a heterogeneous game along with a method for the same. The paper considers multiple examples of different networks to derive the optimal traffic assignment. We introduce two metrics: price of α anarchy and price of good behavior to evaluate the impact of anarchists and implications of central directives. Finally, the proposed algorithm is implemented for a real traffic network to derive insights.
Internet Traffic Categories Demand Prediction to Support Dynamic QoS

Categorized Internet protocol, and demand prediction based on usage behavior can offer substantial benefits to dynamic Quality of Service (QoS) for internet service providers (ISPs). The critical requirement for dynamic QoS is to classify and predict network traffic in the next control time interval. Traffic prediction, with the aid of traffic categories can utilize the network resources more efficiently and support Dynamic QoS to function appropriately. This paper proposes an internet traffic categories demand prediction framework using machine learning techniques to support dynamic QoS. In this framework, three algorithms are implemented at transformation stage to feed into machine learning algorithms and develops efficient prediction model to predict internet network traffic demand. Experimental results show that prediction accuracy of the model is 98.97% and is efficient and suitable to support real-world network traffic prediction.
Toward robust multitype and orientation detection of vessels in maritime surveillance

Reliable multitype and orientation vessel detection is of vital importance for maritime surveillance. We develop three separate convolutional neural network (CNN) models for high-performance single-class vessel detection and then multiclass vessel-type/orientation detection. We also propose a modular combined network, which enhances the multiclass operation. The initial three models provide reliable F1 scores of 85%, 82%, and 76%, respectively. In addition, the modular combined approach improves the F1 scores for the multitype and orientation vessel detection by 2% and 3%, respectively. The training and testing were done on a dataset, including the multitype/orientation annotations, covering 31,078 vessel labels (10 vessel types and 5 orientations), which is offered to public access.
State Channel Based Energy Trading Scheme in Smart Grid

With the development of smart grids, the energy trading market has gradually expanded. However, during the trading process, security and privacy issues have hampered the development of the market. In order to solve these problems, scholars have proposed a variety of solutions using blockchain technology to effectively solve security and privacy issues. However, the blockchain technology itself has poor scalability and insufficient throughput, which still hinders its effective application. To this end, we propose a state channel-based energy trading scheme, which uses state channel technology to effectively increase the throughput of the blockchain.
Engaging the Crowd

Social media has become increasingly important as a space for both organized and ad-hoc activism. As social movement organizations have shifted many aspects of their communication online, bystander populations - citizens who may be somewhat interested in a movement but are largely uninvolved in its activities - have begun to play a more significant role in the achievement of movement aims. In this paper, we examine bystander targeting on social media by social movement organizations in India. Through interviews, observation, and participation, we investigate ways that activists themselves conceptualize, carry out, and reflect upon their own messaging strategies in online spaces, and the perceived success and failure of these strategies. We discuss the limitations of social media as a space in which to achieve movement aims, particularly in a class-segregated context. Our work illustrates the tension faced by activists between prioritizing short and long-term movement goals in the digital sphere.
Other classification examples

In this chapter we will present how real-world classification problems can be solved from scratch by machine learning techniques. A great deal of current interest among the research community involves the use of machine learning techniques for recognition, classification, and forecasting. Several classification examples, including intrusion detection, phishing website detection, credit card fraud detection, spam e-mail detection, and credit scoring, are employed to find abnormalities characterized by data variations. Machine learning techniques can automate anomaly detection by constructing decision surfaces to classify these patterns. Moreover, image data classification such as character recognition with deep learning technique is a new trend. The aim of this chapter is to present how to design an efficient Python ecosystem to carry out real-time data analysis. Toward the end of every section, suitable Python machine learning algorithms will be illustrated by an example. Most of the examples are taken from Python–scikit-learn library (https://scikit-learn.org/stable/), TensorFlow (https://www.tensorflow.org/), and Keras (https://keras.io/), and then adapted.
Modeling of mixed data for Poisson prediction

The paper deals with the task of modeling mixed continuous Gaussian and discrete Poisson data observed on a multimodal system. The proposed solution is based on recursive algorithms of Bayesian mixture estimation. The main contributions of the approach are: (i) the use of the discretized information of normal variables in the form of their clusters in order to keep the one-pass recursive estimation methodology and (ii) the prediction of the multimodal Poisson variable. Experiments with simulated and real data are presented.
Why It Is Sufficient to Have Real-Valued Amplitudes in Quantum Computing

In the last decades, a lot of attention has been placed on quantum algorithms—algorithms that will run on future quantum computers. In principle, quantum systems can use any complex-valued amplitudes. However, in practice, quantum algorithms only use real-valued amplitudes. In this paper, we provide a simple explanation for this empirical fact.
Inferring Temporal Compositions of Actions Using Probabilistic Automata

This paper presents a framework to recognize temporal compositions of atomic actions in videos. Specifically, we propose to express temporal compositions of actions as semantic regular expressions and derive an inference framework using probabilistic automata to recognize complex actions as satisfying these expressions on the input video features. Our approach is different from existing works that either predict long-range complex activities as unordered sets of atomic actions, or retrieve videos using natural language sentences. Instead, the proposed approach allows recognizing complex fine-grained activities using only pretrained action classifiers, without requiring any additional data, annotations or neural network training. To evaluate the potential of our approach, we provide experiments on synthetic datasets and challenging real action recognition datasets, such as MultiTHUMOS and Charades. We conclude that the proposed approach can extend state-of-the-art primitive action classifiers to vastly more complex activities without large performance degradation.
The FOUnt ontologies for quantities, units, and the physical world

Quantities and units of measure provide an important means by which intelligent agents interact with the physical world. Although multiple ontologies for quantities and units of measure have been proposed within the Applied Ontology community, they often incorporate questionable ontological commitm ents. Quantities are combined using notions of dimensional analysis that often conflate the combination of units with algebraic operations on real numbers. In this paper, we present an alternative approach that shifts the focus to the connection between kinds of measurements associated with a unit and the physical objects and processes that are being measured. One of the key features of this approach is that it makes minimal ontological commitments with respect to the TUpper upper ontology – the only new classes that are introduced are the classes for kinds of measures and associated units. We propose axiomatizations of the intended semantics for combining measurements, and the correct axiomatization of the relationship between the quantities and units of measure and the existing upper ontology.
External Thread Measurement Based on ResUnet and HMM

There are many methods of thread measurement. In addition, these thread measurement methods require manual segmentation of regions of interest (threaded area) and These methods are easily disturbed by the environment (e.g. dust, iron filings, oil stains, etc.), resulting in inaccurate measurement results. This paper proposes an external thread measurement method based on ResUnet and hidden Markov model (HMM). First, we propose a ResUnet-based thread edge recognition method that omits the process of calibrating the threaded area and identify the thread edge in a complex environment. Secondly, we use HMM to classify the thread edge points so that the threaded parts can be placed at any angle during the measurement, simplifying the measurement steps and calculating the thread parameters based on the classification results. Finally, we evaluated our method using our own dataset, and the results showed that the difference between the measured value and the standard value is within 0.01 mm.
Mobile Applications in Mandarin Pedagogical Practice

Abstract Teaching is a complicated and comprehensive task. Teachers may spend many years of practice to learn how to teach well, and most of us will not have learned enough. It would be a continuous process to be a good teacher. Instead of keeping on learning what we do not know about teaching the subject, it is better to know what the real problems are happening in the class, reflect what is the cause and come out with an action plan as nobody starts well at teaching. Furthermore, in fulfilling the worldwide demand for Mandarin language acquisition occasioned with the advance affordances of technology, most of the researchers focused on technology impact than the pedagogical aspects. And there is still too little action research regarding teaching pedagogy that has been done on enhancing Mandarin learning, especially in the local context. Thus, it is the right time the researcher cum instructor examines Mandarin pedagogical practice through individual action research to examine to what extent technology-enhanced Mandarin instruction could impact teacher’s pedagogical practice besides assisting students’ learning. A total of fourteen Mandarin non-native learners have participated in the study. The triangulation method was used to ensure the trustworthiness of this qualitative study. The data was collected through students’ diaries, reflective journals, focus group interviews and oral assessment. Then they were analyzed through content and thematic analysis to ascertain the themes regarding teacher pedagogical practice which influenced smartphone usage in Mandarin learning. The results revealed that the human aspect was the most crucial factor in ensuring the success of technology integration instruction. The smartphone was found as a promising tool in teaching Mandarin however it is only effective when teachers use it as a pedagogical tool effectively.
Fitting Penalized Logistic Regression Models Using QR Factorization

The paper presents improvement of a commonly used learning algorithm for logistic regression. In the direct approach Newton method needs inversion of Hessian, what is cubic with respect to the number of attributes. We study a special case when the number of samples m is smaller than the number of attributes n, and we prove that using previously computed QR factorization of the data matrix, Hessian inversion in each step can be performed significantly faster, that is $$\mathcal {O}\left( m^3\right) $$ or $$\mathcal {O}\left( m^2n\right) $$ instead of $$\mathcal {O}\left( n^3\right) $$ in the ordinary Newton optimization case. We show formally that it can be adopted very effectively to $$\ell ^2$$ penalized logistic regression and also, not so effectively but still competitively, for certain types of sparse penalty terms. This approach can be especially interesting for a large number of attributes and relatively small number of samples, what takes place in the so-called extreme learning. We present a comparison of our approach with commonly used learning tools.
Aggregative Adversarial Network for Still-to-Video Face Recognition

Aiming to tackle the problems of blur, noise, pose variations and occlusion in still-to-video face recognition, a novel aggregative adversarial network is proposed to aggregate each sequence of video frames into a still frontal image with high-quality and without occlusion. The network is composed of three modules. The aggregation module and the discriminator module form a competitive relationship which is capable of improving visual quality of synthesized images. The discriminator module distinguishes the synthesized images true or fake, and meanwhile predict its identity in adversarial ways. A perceptual loss combining with the recognition loss is introduced in the recognition module to guarantee the synthesized images produce discriminative features for recognition. Experimental results prove that the recognition accuracy and computational complexity of the proposed network is significantly superior to the state-of-the-arts.
Hybrid Lion Swarm Optimization Algorithm for Solving Traveling Salesman Problem

Abstract In view of Traveling Salesman Problem (TSP) is a NP hard problem, Lion Swarm Optimization (LSO) Algorithm is a new swarm intelligence optimization algorithm. Therefore, in this paper, it uses improved hybrid LSO algorithm to solve TSP. Because the basic LSO algorithm is mainly used to solve the continuous problem, and the TSP is a discrete combinatorial optimization problem, this paper improves the LSO algorithm, introduces the greedy strategy, and combines with the Population Competitive Search (PCS) algorithm in the later stage of the search. As a result, the convergence speed is accelerated and the search efficiency is improved. The classic TSP example is tested, and the simulation results show that the algorithm proposed in this paper has strong stability and is an effective method for solving TSP.
Remote Attestation based Software Integrity of IoT devices

Internet of Things is the new paradigm towards which the world is moving today. As these devices proliferate, security issues at these scales become more and more intimidating. Traditional approach like an antivirus does not work well with these devices and there is a need to look for a more trusted solution. For a device with reasonable computational power, we use a software trusted platform module for the cryptographic operations. In this paper, we have developed a model to remotely attest to the integrity of the processes running in the device. We have also explored the various features of the TPM (Trusted Platform Module) to gain insight into its working and also to ascertain those which can make this process better. This model depends on the server and the TPM to behave as roots of trust for this model. The client computes the HMAC (Hashed Message Authentication Code) values and appends a nonce and sends these values periodically to the server via asymmetric encryption. The HMAC values are verified by the server by comparing with its known good values (KGV) and the trustworthiness of the process is determined and accordingly an authorization response is sent.
Photon Subtraction-Induced Plug-and-Play Scheme for Enhancing Continuous-Variable Quantum Key Distribution with Discrete Modulation

Establishing high-rate secure communications is a potential application of continuous-variable quantum key distribution (CVQKD) but still challenging for the long-distance transmission technology compatible with modern optical communication systems. Here, we propose a photon subtraction-induced plug-and-play scheme for enhancing CVQKD with discrete-modulation (DM), avoiding the traditional loopholes opened by the transmission of local oscillator. A photon subtraction operation is involved in the plug-and-play scheme for detection while resisting the extra untrusted source noise of the DM-CVQKD system. We analyze the relationship between secret key rate, channel losses, and untrusted source noise. The simulation result shows that the photon-subtracted scheme enhances the performance in terms of the maximal transmission distance and make up for the deficiency of the original system effectively. Furthermore, we demonstrate the influence of finite-size effect on the secret key rate which is close to the practical implementation.
“All celebrities and sports on top”

Designing for interacting with data-driven approaches is a new challenge that PD will have to address. This paper presents a case of prototyping for automation of editors’ manual curating of the online front-page of a large newspaper. The editors make decisions about the presentation and placement of article teasers on the front-page. A new data-driven tool, which automates curating the front-page based on quantitative rankings, is about to be introduced. We have developed a prototype to discuss with the editors how they want support for carrying out their judgment-based decisions for a front-page with a good mix of news topics. We present concepts for discussing how manual tasks that interact with data-driven automation can be designed to be meaningful for people in their work.
Image Based 3D Posture Matching in Real Time for Stone Tool Assembly

Through the study of stone tools in Japan, archaeologists can gain insights into the behaviors of Paleolithic people. By joining materials, stone tools can be reassembled into their original form. This assembly is a crucial and unavoidable process in archeology because it is essential for analyzing the method of how to make a stone tool. Through trial and error, archaeologists attempt to recreate and map the structures of joining materials. When pieces of joining material are investigated after it is disassembled, archaeologists must reassemble them. Therefore, the identification of stone tools and visualization of their joining order is crucial for assembling easily. In this paper, we propose a faster and more efficient approach to identifying target stone tools using stable posture estimation and matching. The proposed method is applied to real objects and offers a novel approach to identification and reassembly in archeology.