Reviewerly/exHarmony huggingface dataset:
Encoded Feature Enhancement in Watermarking Network for Distortion in Real Scenes

Deep-learning based watermarking framework has been extensively studied recently. The main structure of such framework is an encoder, a noise layer and a decoder. By training with different distortion sets in the noise layer, the whole network can realize different robustness. However, such framework has a huge drawback that the noise layer must be differentiable, otherwise it cannot be trained end-to-end. But for practical use, much distortions are non-differentiable, so such framework cannot be applied. To address such limitations, this paper propose a triple-phase watermarking framework for practical distortions. The proposed framework consists of three phases including a noise-free initial phase, a mask-guided frequency enhancement phase and an adversarial-training phase. Phase 1 aims to initialize an encoder to embed watermark with high visual quality and a decoder to extract the watermark. In order to generate high quality watermarked image, we design the just noticeable difference (JND)-mask image loss in phase 1 to guide the encoder. At phase 2, based on the investigation of the encoded features and distortions, we propose a mask-guided frequency enhancement algorithm to enhance the encoded feature which ensures the survival of such features after distortion, so that there will be enough features to be learned in phase 3. And phase 3 aims to train a stronger decoder to extract the watermark from the image after practical distortions. The combination of these 3 phases can well handle the non-differentiable problems and make the whole network trainable. Various experiments indicate the superior performance of the proposed scheme in the view of traditional differentiable image processing distortion robustness and practical non-differentiable distortion robustness.
Learning Bilingual Word Embedding Mappings with Similar Words in Related Languages Using GAN

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HxL3: Optimized Delivery Architecture for HTTP Low-Latency Live Streaming

While most of the HTTP adaptive streaming (HAS) traffic continues to be video-on-demand (VoD), more users have started generating and delivering live streams with high quality through popular online streaming platforms. Typically, the video contents are generated by streamers and being watched by large audiences which are geographically distributed far away from the streamers locations. The locations of streamers and audiences create a significant challenge in delivering HAS-based live streams with low latency and high quality. Any problem in the delivery paths will result in a reduced viewer experience. In this paper, we propose HxL3, a novel architecture for low-latency live streaming. HxL3 is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches. By holding the minimum number of live media segments through efficient caching and prefetching policies at the edge, improved transmissions, as well as transcoding capabilities, HxL3 is able to achieve high viewer experiences across the Internet by alleviating rebuffering and substantially reducing initial startup delay and live stream latency. HxL3 can be easily deployed and used. Its performance has been evaluated using real live stream sources and entities that are distributed worldwide. Experimental results show the superiority of the proposed architecture and give good insights into how low latency live streaming is working.
Polarization mapping

Photographers use the hardware of the camera (aperture, exposure time...), lens and filters as tools for artistic expressivity. This expressivity has often been enhanced by software, such as high dynamic range images have been edited in post-process with software tone mappers. In this paper, we propose a similar approach with polarization filters: we design a capture process that enables us to acquire a Stokes image (that encodes all the possible light polarization states) with a single camera, and we then offer a set of software tools that can apply any common polarization filter as a software post-process, delaying the choice of the adequate filter and enabling filters that can be mathematically modeled but are not available as hardware. Then, we devise and provide new algorithms that automatically select the optimal filter for specific goals, such as maximizing (or minimizing) brightness, contrast or saturation. We later show how such optimization filters can not only be applied to the whole image, but can also be at per-pixel level, obtaining new interesting effects. Such optimization can work at real time rates, fact that is illustrated with a brush based user editing interactive tool. The different types of filters are tested in a wide range of results.
A Literature Review and Research Agenda on Explainable Artificial Intelligence (XAI)

Purpose: When Artificial Intelligence is penetrating every walk of our affairs and business, we face enormous challenges and opportunities to adopt this revolution. Machine learning models are used to make the important decisions in critical areas such as medical diagnosis, financial transactions. We need to know how they make decisions to trust the systems powered by these models. However, there are challenges in this area of explaining predictions or decisions made by machine learning model. Ensembles like Random Forest, Deep learning algorithms make the matter worst in terms of explaining the outcomes of decision even though these models produce more accurate results. We cannot accept the black box nature of AI models as we encounter the consequences of those decisions. In this paper, we would like to open this Pandora box and review the current challenges and opportunities to explain the decisions or outcome of AI model. There has been lot of debate on this topic with headlines as Explainable Artificial Intelligence (XAI), Interpreting ML models, Explainable ML models etc. This paper does the literature review of latest findings and surveys published in various reputed journals and publications. Towards the end, we try to bring some open research agenda in these findings and future directions. Methodology: The literature survey on the chosen topic has been exhaustively covered to include fundamental concepts of the research topic. Journals from multiple secondary data sources such as books and research papers published in various reputable publications which are relevant for the work were chosen in the methodology. Findings/Result: While there are no single approaches currently solve the explainable ML model challenges, some model algorithms such as Decision Trees, KNN algorithm provides built in interpretations. However there is no common approach and they cannot be used in all the problems. Developing model specific interpretations will be complex and difficult for the user to make them adopt. Model specific explanations may lead to multiple explanations on same predictions which will lead to ambiguity of the outcome. In this paper, we have conceptualized a common approach to build explainable models that may fulfill current challenges of XAI. Originality: After the literature review, the knowledge gathered in the form of findings were used to model a theoretical framework for the research topic. Then concerted effort was made to develop a conceptual model to support the future research work. Paper Type: Literature Review.
Ultrasensitive Calorimetric Detection of Single Photons from Qubit Decay

We describe a qubit linearly coupled to a heat bath, either directly or via a cavity. The main focus of the paper is on calorimetric detection in a realistic circuit, specifically a solid-state qubit coupled to a resistor as an absorber. The bath in the model is formed of oscillators initially in the ground state with a distribution of energies and coupling strengths. A direct numerical solution of the Schrödinger equation for the full system including up to 106 oscillators in the bath verifies the expected decay process. We address quantitatively the question of separation of the qubit and bath by adding a cavity in between which by detuning allows one to adjust the decay rate into a convenient regime for detection purposes. Most importantly, we propose splitting a quantum to two uncoupled baths and performing a cross-correlation measurement of their temperatures. This technique enhances significantly the signal-to-noise ratio of the calorimeter.Received 21 October 2020Revised 1 December 2021Accepted 7 December 2021DOI:https://doi.org/10.1103/PhysRevX.12.011026Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasOpen quantum systemsQuantum thermodynamicsPhysical SystemsSuperconducting devicesSuperconducting qubitsCondensed Matter, Materials & Applied PhysicsStatistical Physics
Bounding the quantum capacity with flagged extensions

In this article we consider flagged extensions of convex combination of quantum channels, and find general sufficient conditions for the degradability of the flagged extension. An immediate application is a bound on the quantum <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Q</mml:mi></mml:math> and private <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>P</mml:mi></mml:math> capacities of any channel being a mixture of a unitary map and another channel, with the probability associated to the unitary component being larger than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:math>. We then specialize our sufficient conditions to flagged Pauli channels, obtaining a family of upper bounds on quantum and private capacities of Pauli channels. In particular, we establish new state-of-the-art upper bounds on the quantum and private capacities of the depolarizing channel, BB84 channel and generalized amplitude damping channel. Moreover, the flagged construction can be naturally applied to tensor powers of channels with less restricting degradability conditions, suggesting that better upper bounds could be found by considering a larger number of channel uses.
Class imbalance learning with Bayesian optimization applied in drug discovery

Machine intelligence (MI), including machine learning and deep learning, have been regarded as promising methods to reduce the prohibitively high cost of drug development. However, a dilemma within MI has limited its wide application: machine learning models are easier to interpret but yield worse predictive performance than deep learning models. Therefore, we propose a pipeline called Class Imbalance Learning with Bayesian Optimization (CILBO) to improve the performance of machine learning models in drug discovery. To demonstrate the efficacy of the CILBO pipeline, we developed an example model to predict antibacterial candidates. Comparison of the antibacterial prediction performance between our model and a well-known deep learning model published by Stokes et al. suggests that our model can perform as well as the deep learning model in drug activity prediction. The CILBO pipeline we propose provides a simple, alternative approach to accelerate preliminary screenings and decrease the cost of drug discovery.
Measuring the time spent on data curation

Purpose Budgeting data curation tasks in research projects is difficult. In this paper, we investigate the time spent on data curation, more specifically on cleaning and documenting quantitative data for data sharing. We develop recommendations on cost factors in research data management. Design/methodology/approach We make use of a pilot study conducted at the GESIS Data Archive for the Social Sciences in Germany between December 2016 and September 2017. During this period, data curators at GESIS - Leibniz Institute for the Social Sciences documented their working hours while cleaning and documenting data from ten quantitative survey studies. We analyse recorded times and discuss with the data curators involved in this work to identify and examine important cost factors in data curation, that is aspects that increase hours spent and factors that lead to a reduction of their work. Findings We identify two major drivers of time spent on data curation: The size of the data and personal information contained in the data. Learning effects can occur when data are similar, that is when they contain same variables. Important interdependencies exist between individual tasks in data curation and in connection with certain data characteristics. Originality/value The different tasks of data curation, time spent on them and interdependencies between individual steps in curation have so far not been analysed.
Teaching Professional Use of Social Media Through a Service-Learning Business Communication Project

Using social media communication (SMC) for personal and professional use represents two different skill sets. Though students often use SMC on a personal basis for fun and connecting with friends, they often fail to understand how SMC can be used effectively as a professional organizational/corporate communication tool. A service-learning project was conceptualized in a business and professional communication (BPC) course, where students ( n = 93) used professional SMC skills to design social media campaigns for fulfilling nongovernmental organizations’ needs of manpower, material, and/or money. Students’ attitudes and efficacy toward SMC were recorded using a survey questionnaire. The need and obstacles in including SMC in BPC are also discussed in the article.
Robust Poisson multi-Bernoulli mixture filter using adaptive birth distributions for extended targets

This paper presents a robust Poisson multi-Bernoulli mixture (PMBM) filter using adaptive birth distributions for the tracking of multiple extended targets. Firstly, in order to be immune to the uncertainty of target birth, we present a novel measurement-driven adaptive birth distribution that is robust to the arbitrariness of locations where new targets appear. Then, to enhance practicability of algorithm, the Beta distribution is employed to describe unknown detection probability augmented into the extended target state. On these bases, the detailed recursion and closed-form solutions to the proposed filter are derived by means of approximating the intensity of target birth and potential targets to Beta gamma Gaussian inverse Wishart (BGGIW) mixture form and density of existing Bernoulli component to a single BGGIW form. Moreover, the computational bottleneck caused by data associations is settled by truncating the PMBM filtering density using the Gibbs sampler of the Metropolis Hasting Markov Chain Monte Carlo (MCMC). Simulation results show the robustness and effectiveness of the proposed method.
Hiding in Temporal Networks

Social network analysis tools can infer various attributes just by scrutinizing one's connections.Several researchers have studied the problem faced by an evader whose goal is to strategically rewire their social connections in order to mislead such tools, thereby concealing their private attributes.However, to date this literature has only considered static networks, while neglecting the more general case of temporal networks, where the structure evolves over time.Driven by this observation, we study how the evader can conceal their importance from an adversary armed with temporal centrality measures.We consider computational and structural aspects of this problem: Is it computationally feasible to calculate optimal ways of hiding?If it is, what network characteristics facilitate hiding?This topic has been studied in static networks, but in this work, we add realism to the problem by considering temporal networks of edges changing in time.We find that it is usually computationally infeasible to find the optimal way of hiding.On the other hand, by manipulating one's contacts, one could add a surprising amount of privacy.Compared to static networks, temporal networks offer more strategies for this type of manipulation and are thus, to some extent, easier to hide in.
Covariance Intersection fusion with element-wise partial knowledge of correlation

Covariance Intersection fusion is a linear rule for combining estimates. If the cross-correlation matrix of the errors of two estimates is unknown, the rule is bound-optimal. This paper elaborates the case when some elements of the cross-correlation matrix are known. Techniques for constructing a family of upper bounds of the joint mean square error matrix are introduced. All configurations for the fusion of up to four estimates are considered explicitly. The techniques are also applicable for the fusion of more than four estimates.
Gifted Education on Reddit: A Social Media Sentiment Analysis

Social media continues to be an ever-present part of people’s lives. One of the largest social media websites, Reddit has more than 430 million unique visitors monthly. What is unknown to scholars is how gifted education fits into this modern form of communication. In our research, we examined how gifted education is discussed over Reddit using text mining in combination with sentiment analysis. In addition, our research conditioned sentiment on variables such as self-disclosure, interest, and controversy. We found that, overall, discussion of giftedness was neutral in terms of sentiment and used common language across subreddits. We also found that the language used and distribution of sentiment were similar between self-disclosures and non-self-disclosures.
The analysis of security and privacy risks in smart education environments

The contemporary smart educational environment uses different information technologies like social networks, virtual laboratories, augmented reality, artificial intelligence, big data, and so on. Each of these technologies has its security and privacy threats profile, but their integration in one system can lead to completely new challenges. The article analyses the technological development of smart educational environments from the point of view of their security and privacy issues. Any technological or legislative security control could be broken as the result of one mistake caused by human factors. People with different levels of competence are interacting every day with each other in educational environments. The risks of personal data leaking or hacking of educational services should be minimized during this interaction. Therefore, not only the key technologies that form the architecture of the educational environment but also the main points of interaction between the users and the education environment should be taken into account in the analysis. The article provides a basic analysis of security and privacy risks for smart education environments. As the result, the analysis identifies key information security technologies development of which is necessary for the sustainable development of a smart educational environment as part of a smart city.
An abstract, logical approach to characterizing strong equivalence in non-monotonic knowledge representation formalisms

Two knowledge bases are strongly equivalent if and only if they are mutually interchangeable in arbitrary contexts. This notion is of high interest for any logical formalism, since it allows to locally replace parts of a given theory without changing its meaning. In contrast to classical logic, where strong equivalence coincides with standard equivalence (having the same models), it is possible to find ordinary but not strongly equivalent objects for any nonmonotonic formalism available in the literature. Consequently, much effort has been devoted to characterizing strong equivalence for knowledge representation formalisms such as logic programs under the stable model semantics, Reiter's default logic, or Dung's argumentation frameworks. For example, strong equivalence for logic programs under stable models can be characterized by so-called HT-models. More precisely, two logic programs are strongly equivalent if and only if they are standard equivalent in the logic of here and there. This means that the logic of here and there can be seen as a characterizing formalism for logic programs under stable model semantics. The aim of this article is to study whether the existence of such characterization logics can be guaranteed for any logic. One main result is that every knowledge representation formalism that allows for a notion of strong equivalence on its finite knowledge bases also possesses a canonical characterizing formalism. In particular, we argue that those characterizing formalisms can be seen as classical, monotonic logics. Moreover, we will not only show the existence of characterizing formalism, but even that the model theory of any characterizing logic is uniquely determined (up to isomorphism).
Many Hands Make Light Work: Transferring Knowledge From Auxiliary Tasks for Video-Text Retrieval

The problem of video-text retrieval, which searches videos via natural language descriptions or vice versa, has attracted growing attention due to the explosive scale of videos produced every day. The dominant approaches for this problem follow the pipeline that firstly learns compact feature representations of videos and texts, and then jointly embeds them into a common feature space where matched video-text pairs are close and unmatched pairs are far away. However, most of them neither consider the structural similarities among cross-modal samples in a global view, nor leverage useful information from other relevant retrieval processes. We argue that both information has great potential for video-text retrieval. In this paper, we treat the relevant retrieval processes as auxiliary tasks and we extract useful knowledge from them by exploiting structural similarities via Graph Neural Networks (GNNs). We then progressively transfer the knowledge from auxiliary tasks in a general-to-specific manner to assist the main task of the current retrieval process. Specifically, for the retrieval of the given query, we first construct a sequence of query-graphs whose central queries are chosen from distant to close to the given query. Then we conduct knowledge-guided message passing in each query-graph to exploit regional structural similarities and gather knowledge of different levels from the updated query-graphs with a knowledge-based attention mechanism. Finally, we transfer the extracted useful knowledge from general to specific to assist the current retrieval process. Extensive experimental results show that our model outperforms the state-of-the-arts on four benchmarks.