Reviewerly/exHarmony huggingface dataset:
Thin Cloud Removal for Single RGB Aerial Image

Abstract Acquired above variable clouds, aerial images contain the components of ground reflection and cloud effect. Due to the non‐uniformity, clouds in aerial images are even harder to remove than haze in terrestrial images. This paper proposes a divide‐and‐conquer scheme to remove the thin translucent clouds in a single RGB aerial image. Based on colour attenuation prior, we design a kind of veiling metric that indicates the local concentration of clouds effectively. By this metric, an aerial image containing thickness‐varied clouds is segmented into multiple regions. Each region is veiled by clouds of nearly‐equal concentration, and hence subject to common assumptions, such as boundary constraint on transmission. The atmospheric light in each region is estimated by the modified local colour‐line model and composed into a spatially‐varying airlight map for the entire image. Then scene transmission is estimated and further refined by a weighted ‐norm based contextual regularization. Finally, we recover ground reflection via the atmospheric scattering model. We verify our cloud removal method on a number of aerial images containing thin clouds and compare our results with classical single‐image dehazing methods and the state‐of‐the‐art learning‐based declouding method, respectively.
Mitigating Effect of Dictionary Matching Errors in Distantly Supervised Named Entity Recognition

Named entity recognition (NER) is a fundamental technique that brings basic semantic awareness to natural language processing applications and services. Since we need a large amount of training data to train a custom NER model, distant supervision that leverages named entity dictionaries is expected to be a promising approach to train NER models quickly. However, dictionary matching causes a considerable number of errors that deteriorates both precision and recall of the final NER models, and we need to mitigate its effect. In this study, we particularly aim at improving precision of NER models by accounting for dictionary matching errors. Experimental results show that the proposed method can achieve an improvement of precisions especially under poor dictionary performance conditions.
Who Owns Bitcoin? Private Law Facing the Blockchain

Blockchain, or “distributed ledger” technology, has been devised as an alternative to the law of finance. While it has become clear by now that regulation in the public interest is necessary, for example to avoid money laundering, drug dealing or tax evasion, the particularly thorny issues of private law have been less discussed. These include, for instance, the right to reverse an erroneous transfer, the ownership of stolen coins and the effects of succession or bankruptcy of a bitcoin holder. All of these questions require answers from a legal perspective because the technology ignores them.Particular difficulties arise when one tries to apply a property analysis to the blockchain. Surprisingly, it is far from clear how virtual currencies and other crypto assets are transferred and acquired. The traditional requirements posed by private law, such as an agreement between the parties and the transfer of possession, are incompatible with the technology. Moreover, the idea of a "void" or "null" transfer is hard to reconcile with the immutability that characterizes the blockchain.Before any such questions can be answered, it is necessary to determine the law governing blockchain transfers and assets. This is the point where conflict of laws, or “private international law”, comes into play. Conflicts lawyers are used to submitting legal relations to the law of the country with the most significant connection. But seemingly insurmountable problems occur because decentralized ledgers with no physical connecting factors do not lend themselves to this type of “localization” exercise.The issue of this paper therefore is: How can blockchain be squared with traditional categories of private law, including private international law? The proposal made herein avoids the recourse to a newly fashioned "lex digitalis" or "lex cryptographica". Rather, it is suggested that the problems can be solved by using existing national laws, supplemented by an international text. At the same time, the results produced by DLT should also be accepted as legally protected and corrected only where necessary under the applicable national rules. In this way, a symbiosis between private law and innovative technology can be created.
Transformer-PSS: A High-Efficiency Prosodic Speech Synthesis Model based on Transformer

Much attention has been given to prosodic speech synthesis with the progress of human-computer interaction and automatic content generation. However, one of its disadvantages is high computational complexity which makes it hard to train and inference. Most of the popular models, such as GST-Tacotron, use a recursive decoder to generate the Mel spectrum frame-by-frame which might be the main bottleneck restraining their calculation speed. In this paper, we propose Transformer-PSS, a high-efficiency prosodic speech synthesis model that employs the transformer architecture to generate Mel spectrum in a non-autoregressive way. Experiments show that our model is 4.6x faster than popular models and have better generative quality on objective evaluations.
IEEE Quantum Week 2020 Tutorial Abstracts

The shortage of skilled labor is one of the quantum computing sector's greatest challenges. The week-long Tutorials program at the IEEE International Conference on Quantum Computing and Engineering (QCE20) includes sixteen engaging tutorials by leading experts aimed squarely at workforce development and training considerations. These tutorials are ideally suited to develop quantum champions for industry, academia, government, and build expertise for emerging quantum ecosystems. Virtual IEEE Quantum Week will cover a broad range of topics in quantum computing and technologies including a lineup of fantastic hands-on tutorials on programming and applications.
Pertinence of Emotional Education within the Ordinary Curriculum: Design and Assessment of a Regional Free Elective Subject on Emotional Intelligence

This research work explores the results of the implementation and assessment of a regional free elective subject on emotional intelligence. This subject was introduced as a pilot experience and imparted within the ordinary curriculum of the first grade of compulsory secondary education (7th grade) in a public secondary school located in Castilla y León (Spain). Its assessment was carried out through a quasi-experimental study with a control group, whose participants were 33 students aged 12-13, 23 of which were in the control group, with 10 students in the experimental group. The subject was composed of 58 50-minute sessions. The results show a statistically significant improvement (p<.05) in the variables related to mood and general emotional intelligence, and also high degrees of satisfaction on the part of both students and their families.
An Adaptive Video Streaming Framework for Peer-To-Peer 5G Networks: Paving the Road to 5G-IMS

The IMS architecture presents many disadvantages which are centralized control, low efficiency, and low scalability in terms of core network equipment compared to network infrastructures using Cloud Computing. Cloud Computing is a new information technology paradigm, offering dynamically scalable resources, often through virtual machines and accessible as services on the Internet. IMS migration to the cloud can improve the performance of the IMS infrastructure. Video streaming in the IMS architecture of 5G technology is proposed for use in high-quality multimedia applications, the use of adaptive streaming stream is used to adapt the quality for a heterogeneous network of equipment. Knowing that 5G technology is based on cloud computing techniques and virtualization, coexistence between IMS and Peer to Peer is possible with the BitTorrent protocol which offers a solution compatible with 5G technology, by offering BitTorrent Sync a solution based on Cloud. Use of a video streaming adaptation with an architecture based on 5G technology for both to distribute the load between the elements of the P2P IMS architecture, decrease the response time for the procedures (User authentication, time required for “a Peers joined the network”, time to download songs (Reduce latency in the network).
A Clustering-Based Method for Automatic Educational Video Recommendation Using Deep Face-Features of Lecturers

Discovering and accessing specific content within educational video bases is a challenging task, mainly because of the abundance of video content and its diversity. Recommender systems are often used to enhance the ability to find and select content. But, recommendation mechanisms, especially those based on textual information, exhibit some limitations, such as being error-prone to manually created keywords or due to imprecise speech recognition. This paper presents a method for generating educational video recommendations using deep face-features of lecturers without identifying them. More precisely, we use an unsupervised face clustering mechanism to create relations among the videos based on the lecturer's presence. Then, for a selected educational video taken as a reference, we recommend the ones where the presence of the same lecturers is detected. Moreover, we rank these recommended videos based on the amount of time the referenced lecturers were present. For this task, we achieved an mAP value of 99.165%.
Finite Blockchain Games

This paper studies the dynamic construction of a blockchain by competitive miners. In contrast to the literature, we assume a finite time horizon. Moreover, miners are rewarded for blocks that eventually become part of the longest chain. It is shown that popular mining strategies such as adherence to conservative mining or to the longest-chain rule constitute pure-strategy Nash equilibria. However, these equilibria are not subgame perfect.
Curriculum system design for intelligent construction based on VR technology

Abstract With the emergence of AI, IOT, 5G technology, cloud computing and other terms, the digital era has significantly arrived, the national universities cultivate new engineering complex talents who adapt to the new era, which will bring great significance to promote technological innovation in the construction industry, and most institutions combine their own positioning to carry out professional reform in order to actively embrace industry changes. The purpose of this article is to explore the construction of intelligent construction professional curriculum system based on VR technology under the new situation, with the aim of innovating the layout mode of intelligent construction professional curriculum system, improving the training effect of new engineering intelligent construction talents, adapting to the development of information technology, and promoting the innovation of engineering construction technology.
An overview of the advantages of cloud computing and online IDE

The article discusses cloud computing and their impact on the field of software development, and analyzes several issues of developers that can be solved using the online IDE. Usage of cloud computing in the enterprise is not new, and it is not difficult in terms of implementation. That is why it is gaining popularity. First, due to the large number of technologies that allow you to optimize internal processes. Secondly, due to the large number of giant companies and small businesses that use these technologies. Cloud computing is very interesting in financial terms. After all, they allow not spending money on building and supporting the infrastructure. One can also not worry about risks, such as, for example, equipment that affects the system, weather conditions, and so on. It takes on all these moments. Evolving of architectural solutions also increases the impact on cloud technology. A service-oriented approach to software development is becoming increasingly popular. It is less and less possible to see the usual thin client and a single server-monolith. Clients are becoming more complex, attracting part of the business logic, servers are divided into parts, each of which is responsible for a particular part of the subject area, and in some cases may not know about the existence of others. The IDE, which requires developers for programming, also has analogs on the web platform. The online IDE has its advantages for solving some tasks and has become increasingly popular lately. Users of the online IDE can create, run, and customize software that works with a simple browser. The main goal of this study is to determine the main advantages of cloud technologies in the application development process, analyze the segment of online IDE. Based on these data to identify the main situations that determine their use, predict further development, and identify principles and technologies used in this area.
Self-adjusting Information Fusion Wiener Filter for the Multisensor Signal Systems with Correlated Noise

With the development of information fusion and filtering theory, we can find a lot of research achievements on target tracking and signal processing. For autoregressive (AR) signal system with multisensor and correlated white noises, if its noise variance and model parameter are unknown or not given, a self-adjusting fused Wiener filter is proposed. The solution to the problem is to substitute estimated values of the unkown elements for real values. The next thing to do is to prove the convergence performance in realization of the self-adjusting fuser obtained above. The DESA (dynamic error system analysis) is made to prove the asymptotic optimality of the self-adjusting fused Wiener filter and checked by a simulation example.
On the Redundancy of D-Ary Fano Codes

We study the redundancy of D-ary Fano source codes. We show that a novel splitting criterion allows to prove a bound on the redundancy of the resulting code which sharpens the guarantee provided by Shannon’s classical result for the case of an optimal code.In particular we show that, for any \(D \ge 2\) and for every source distribution \(\mathbf{p} = p_1, \dots , p_n,\) there is a D-ary Fano code that satisfies the redundancy bound $$\begin{aligned} \overline{L} - H_D(\mathbf{p}) \le 1- p_{\min }, \end{aligned}$$ (1)where, \(\overline{L}\) denotes the average codeword length, \(p_{\min } = \min _i p_i,\) and \(H_D(\mathbf{p}) = -\sum _{i=1}^n p_i \log _D(p_i)\) is the D-ary entropy of the source.The existence of D-ary Fano codes achieving such a bound had been conjectured in [ISIT2015], where, however, the construction proposed achieves the bound only for \(D = 2, 3,4.\) In [ISIT2020], a novel construction was proposed leading to the proof that the redundancy bound in (1) above also holds for \(D=5\) (and some other special cases). This result was attained by a dynamic programming based algorithm with time complexity O(Dn) (per node of the codetree).Here, besides proving that the redundancy bound in (1) can be achieved, unconditionally, for every \(D > 3,\) we also significantly improve the time complexity of the algorithm building a D-ary Fano code tree achieving such a bound: We show that, for every \(D \ge 4,\) a D-ary Fano code tree satisfying (1) can be constructed by an efficient greedy procedure that has complexity \(O(D\log _2 n)\) per node of the codetree (i.e., improving from linear time to logarithmic time in n).
LIFADER: Light Face Detection and Recognition approach for people tracking

Video surveillance is an inescapable tool in the recent modern world due to it importance in securing persons and goods. This gives the possibility to analyze information by processing multimedia signal and more precisely video signal. However, it come with its share of demands in the sense that processing of video signal uses more resources than normal information. In addition, the recent techniques used in detection and recognition tasks are machine learning/ deep learning approaches which is also demanding in terms of resources. On the other hand, computations in sensor network are subject to limitations in resources (memory in particular). This leads to challenges if an attempt is made to handle situations such as the monitoring of people in a quarantine zone for example. We propose in this paper an approach to solve the previously mentioned issues. The proposed approach combines an improved foreground segmentation approach a face detection and recognition approach as well a super resolution approach. This is with the intuition that in video surveillance, the processing of video information is necessary only when people move in the scene controlled by multimedia sensor network.
Ethical and Societal Issues of Automated Dark Web Investigation: Part 3

Law Enforcement Agencies (LEAs) have to proactively prevent, detect, and deter terrorist activity involving use of the Internet and the dark web. This demanding task not only involves investigations of specific case scenarios but also searching the Internet and dark web for potential terrorist-related content independently of an individual case. Especially such case independent searches of the Internet and dark web can and need to be supported by automated tools to be reasonably effective. This chapter discusses the relevant political, ethical, and societal aspects of LEAs using an automated tool and aims to provide guidance for the development and use of such a tool by LEAs supporting their fight against terrorism.
Text Hiding in MPEG-l Video File Using Header Location

Steganography is the science of concealing information inside cover media file. In a manner that prevents the outside observer from recognizing the presence of concealing information. Steganography plays an increasingly important role in today's connected society, as the demand for covert communications and digital copyright protection continues to rise. In this paper, we present a new steganography algorithm to hide text message in video. MPEG-1 file was used as a cover file. We employed the unused bits of group of pictures (GOP) header location to hide the text. Furthermore, the hidden text was encoded using an encryption algorithm. In contrast, steganography extraction and decoding algorithms will be presented as well. Our results showed the ability of proposed algorithm to a text message inside MPEG-l file. In addition, the hiding text inside MPEG-l file did not affect the essential information of video because we employed the empty partitions of GOP header to hide the message.
Solving Double Counting Problems with Dynamic Programming Algorithm

Abstract In view of the repetitive calculation problems that often appear in algorithmic questions, this paper analyzes the common thought methods and dynamic programming for solving such problems. Through several types of classic questions, such as the backpack problem, the longest common subsequence, etc., the analysis methods and scope of application of dynamic programming are summarized.
Unknown word handling in structured text classification

Abstract In the NLP task, high priority was accorded to the accumulation of vocabularies. To complement them, you need to find unknown words. Unique words with the help of an expert can be determined in the dictionary. This paper presents a technique for finding unknown words in Named Entity Recognition (NER).
Exploring the Limitations of Responsive Design Through a Case Study Approach

Having an online presence is essential for any company regardless of its size and type of business. Users are currently striving to interact with companies through the web, regardless of their access device. In this sense, responsive web design emerged as a very useful technique that allows the dynamic adaptation of the design regardless of the size and resolution of the access device. Despite the unequivocal advantages associated with this technique, there are also limitations which turn this approach not feasible or advisable for all projects. This study, through the realization of five case studies, seeks to identify the main limitations of responsive design and responsive design frameworks. Additionally, this study suggests further development models that may be more effective in the dynamic adaptation of the design and contents according to the features of the access device, such as the adoption of adaptive design, use of native apps, and hybrid models.
Unsupervised Multi-shot Person Re-identification via Dynamic Bi-directional Normalized Sparse Representation

Due to the abundant priori information and the widespread applications, multi-shot based person re-identification has drawn increasing attention in recent years. In this paper, the high labeling cost and the huge unlabeled data motivate us to focus on the unsupervised scenario and a unified coarse-to-fine framework is proposed, named by Dynamic Bi-directional Normalized Sparse Representation (DBNSR). Specifically, the method designs an alternatively iterative mechanism to dynamically update the estimated cross-camera labels and progressively learn a discriminative metric. In each iteration, a Bi-directional Normalized Sparse Representation (BNSR) model is applied to estimate the label for each pair of cross-camera person sequences with bi-directional probabilities defined to encode their mutual correlation. With the estimated labels, we learn a keep-it-simple-and-straight-forward metric (KISSME) to further make BNSR model available in a more discriminative and lower-dimensional subspace. Extensive experiments on PRID 2011, iLIDS-VID and MARS three benchmarks show that our method performs favorably against state-of-the-arts.
Virtual Reality Lifelog Explorer: A Prototype for Immersive Lifelog Analytics

The Virtual Reality Lifelog Explorer is a prototype for immersive personal data analytics, intended as an exploratory effort to produce more sophisticated virtual or augmented reality analysis prototypes in the future. An earlier version of this prototype competed in, and won, the first Lifelog Search Challenge (LSC) held at ACM ICMR in 2018.
The Application of CI-CSCKF Fusion in Tracking the Mid-Course of Boost-Glide Vehicles

In recent years, boost-glide vehicles have been widely studied in the military field of various powers. Because boost-glide vehicles can still generate maneuverability and high maneuverability in the free-glide segment, the traditional dynamic model-based tracking method is no longer applicable in this field. Due to the multisensor fusion tracking effect is superior to the single sensor tracking, combined with the field of maneuvering target tracking commonly used “current” statistical model, and compared with the UKF, nonlinear filter such as CKF filter has better stability and smaller computation. CS-CKF tracking model was constructed, and through the two radar observations, using the theory of Covariance Intersection (CI) fusion of two groups of observation data integration CI-CSCKF fusion. Simulation results show that the estimation effect of CI-CSCKF fusion algorithm is better than the tracking effect of single sensor based on CSCKF.
The Scientist’s Perspective on Brass Instrument Behaviour

This chapter discusses the application of the classic scientific method to observe, measure and record the behaviour of brass instruments and develops a simple theory (or “model”) to explain the main features of this behaviour. The sound pressure levels inside the player’s mouth and the instrument mouthpiece are related to the radiated sounds. The approximations and simplifications which are needed for basic modelling of the complexities of brass instrument playing are justified. Scientific terms and concepts commonly used in brass instrument studies are explained: these include acoustic pressure, waveform, mean flow of air, optimisation, nonlinear sound generation, Fourier analysis, frequency spectrum, spectral envelope, cutoff frequency, oscillation regime, sound reflection and sound transmission. The Bouasse-Benade prescription is formulated and the phenomenon of the trombone pedal note addressed. The chapter concludes with a discussion of the wind instrument paradox.